diff --git a/.claude/settings.local.json b/.claude/settings.local.json
index 06a3b9e56..4eb761c85 100644
--- a/.claude/settings.local.json
+++ b/.claude/settings.local.json
@@ -5,7 +5,8 @@
       "Bash(tree:*)",
       "Bash(find:*)",
       "Bash(cat:*)",
-      "Bash(.venv/Scripts/python.exe:*)"
+      "Bash(.venv/Scripts/python.exe:*)",
+      "Bash(git ls-tree:*)"
     ],
     "deny": [],
     "ask": []
diff --git a/docs/confidence_stopping.md b/docs/confidence_stopping.md
index a4cdaa3ee..7e7708fbb 100644
--- a/docs/confidence_stopping.md
+++ b/docs/confidence_stopping.md
@@ -18,8 +18,8 @@ Early Stopping addresses this by:
 The Early Stopping feature leverages the trustworthiness, quantified through their `userScores`, to calculate the confidence level of each category for any given datapoint.
 
 ### Confidence Calculation
-- **UserScores**: Each annotator has a `userScore` between 0 and 1, representing their reliability. [More information](/understanding_the_results/#understanding-the-user-scores)
-- **Aggregated Confidence**: By combining the userScores of annotators who selected a particular category, the system computes the probability that this category is the correct one.
+- **UserScores**: Each labeler has a `userScore` between 0 and 1, representing their reliability. [More information](understanding_the_results.md#understanding-the-user-scores)
+- **Aggregated Confidence**: By combining the userScores of labelers who selected a particular category, the system computes the probability that this category is the correct one.
 - **Threshold Comparison**: If the calculated confidence exceeds your specified threshold, the system stops collecting further responses for that datapoint.
 
 ## Understanding the Confidence Threshold
@@ -28,17 +28,17 @@ We've created a plot based on empirical data aided by simulations to give you an
 
 There are a few things to keep in mind when interpreting the results:
 
-- **Unambiguous Scenario**: The graph represents an ideal situation such as in the [example below](#using-early-stopping-in-your-order) with no ambiguity which category is the correct one. A counter-example would be subjective tasks like "Which image do you prefer?", where there's no clear correct answer.
+- **Unambiguous Scenario**: The graph represents an ideal situation such as in the [example below](#using-early-stopping-in-your-job) with no ambiguity which category is the correct one. A counter-example would be subjective tasks like "Which image do you prefer?", where there's no clear correct answer.
 - **Real-World Variability**: Actual required responses may vary based on task complexity.
-- **Guidance Tool**: Use the graph as a reference to set realistic expectations for your orders. 
+- **Guidance Tool**: Use the graph as a reference to set realistic expectations for your jobs.
 - **Response Overflow**: The number of responses per datapoint may exceed the specified amount due to multiple users answering simultaneously.
 
 
 <div style="width: 780px; height: 650px; overflow: hidden;">
     <iframe src="/plots/confidence_threshold_plot_with_slider_darkmode.html"
-            width="100%" 
-            height="100%" 
-            frameborder="0" 
+            width="100%"
+            height="100%"
+            frameborder="0"
             scrolling="no"
             style="overflow: hidden;">
     </iframe>
@@ -46,33 +46,50 @@ There are a few things to keep in mind when interpreting the results:
 
 >**Note:** The Early Stopping feature is supported for the Classification and Comparison workflows. The number of categories is the number of options in the Classification task. For the Comparison task, the number of categories is always 2.
 
-## Using Early Stopping in Your Order
+## Using Early Stopping in Your Job
 
-Implementing Early Stopping is straightforward. You simply add the confidence threshold as a parameter when creating the order.
+Implementing Early Stopping is straightforward. You simply add the confidence threshold as a parameter when creating the job definition.
 
-### Example: Classification Order with Early Stopping
+### Example: Classification Job with Early Stopping
 
 ```python
-order = rapi.order.create_classification_order(
-    name="Test Classification Order with Early Stopping",
+from rapidata import RapidataClient
+
+client = RapidataClient()
+
+# Create audience with qualification example
+audience = client.audience.create_audience(name="Animal Classification Audience")
+audience.add_classification_example(
+    instruction="What do you see in the image?",
+    answer_options=["Cat", "Dog"],
+    datapoint="https://assets.rapidata.ai/cat.jpeg",
+    truth=["Cat"]
+)
+
+# Create job definition with early stopping
+job_definition = client.job.create_classification_job_definition(
+    name="Test Classification with Early Stopping",
     instruction="What do you see in the image?",
     answer_options=["Cat", "Dog"],
     datapoints=["https://assets.rapidata.ai/dog.jpeg"],
     responses_per_datapoint=50,
     confidence_threshold=0.99,
-).run()
-
-order.display_progress_bar()
-result = order.get_results()
-print(result)
+)
+
+# Preview and run
+job_definition.preview()
+job = audience.assign_job_to_audience(job_definition)
+job.display_progress_bar()
+results = job.get_results()
+print(results)
 ```
 
 In this example:
 
-- responses_per_datapoint=50: Sets the maximum number of responses per datapoint.
-- confidence_threshold=0.99: Specifies that data collection for a datapoint should stop once a 99% confidence level is reached.
+- `responses_per_datapoint=50`: Sets the maximum number of responses per datapoint.
+- `confidence_threshold=0.99`: Specifies that data collection for a datapoint should stop once a 99% confidence level is reached.
 
-We'd expect this to take roughtly 4 responses to reach the 99% confidence level.
+We'd expect this to take roughly 4 responses to reach the 99% confidence level.
 
 ## When to Use Early Stopping
 
@@ -83,7 +100,7 @@ We recommend using Early Stopping when:
 
 ## Analyzing Early Stopping Results
 
-When using Early Stopping, the [results](/understanding_the_results/) will additionally include a `confidencePerCategory` field for each datapoint. This field shows the confidence level for each of the categories in the task.
+When using Early Stopping, the [results](understanding_the_results.md) will additionally include a `confidencePerCategory` field for each datapoint. This field shows the confidence level for each of the categories in the task.
 
 Example:
 ```json
@@ -117,7 +134,7 @@ Example:
                     "Cat": 0.0
                 },
                 # this only appears when using early stopping
-                "confidencePerCategory": { 
+                "confidencePerCategory": {
                     "Dog": 0.9943,
                     "Cat": 0.0057
                 },
diff --git a/docs/examples/classify_job.md b/docs/examples/classify_job.md
new file mode 100644
index 000000000..dfa6f8a3a
--- /dev/null
+++ b/docs/examples/classify_job.md
@@ -0,0 +1,49 @@
+# Classification Job Example
+
+To learn about the basics of creating a job, please refer to the [quickstart guide](../quickstart.md).
+
+In this example, we want to rate different images based on a Likert scale to assess how well generated images match their descriptions. The `NoShuffle` setting ensures answer options remain in order since they represent a scale.
+
+```python
+from rapidata import RapidataClient, NoShuffle
+
+IMAGE_URLS = [
+    "https://assets.rapidata.ai/tshirt-4o.png",
+    "https://assets.rapidata.ai/tshirt-aurora.jpg",
+    "https://assets.rapidata.ai/teamleader-aurora.jpg",
+]
+
+CONTEXTS = ["A t-shirt with the text 'Running on caffeine & dreams'"] * len(IMAGE_URLS)
+
+client = RapidataClient()
+
+# Create audience with qualification example
+audience = client.audience.create_audience(name="Likert Scale Audience")
+audience.add_classification_example(
+    instruction="How well does the image match the description?",
+    answer_options=["1: Not at all", "2: A little", "3: Moderately", "4: Very well", "5: Perfectly"],
+    datapoint="https://assets.rapidata.ai/tshirt-4o.png",
+    truth=["5: Perfectly"],
+    context="A t-shirt with the text 'Running on caffeine & dreams'"
+)
+
+# Create job definition
+job_definition = client.job.create_classification_job_definition(
+    name="Likert Scale Example",
+    instruction="How well does the image match the description?",
+    answer_options=["1: Not at all", "2: A little", "3: Moderately", "4: Very well", "5: Perfectly"],
+    contexts=CONTEXTS,
+    datapoints=IMAGE_URLS,
+    responses_per_datapoint=25,
+    settings=[NoShuffle()]
+)
+
+# Preview the job definition
+job_definition.preview()
+
+# Assign to audience and get results
+job = audience.assign_job_to_audience(job_definition)
+job.display_progress_bar()
+results = job.get_results()
+print(results)
+```
diff --git a/docs/examples/classify_order.md b/docs/examples/classify_order.md
deleted file mode 100644
index b9592cb6c..000000000
--- a/docs/examples/classify_order.md
+++ /dev/null
@@ -1,46 +0,0 @@
-# Example Classify Order
-
-To learn about the basics of creating an order, please refer to the [quickstart guide](../quickstart.md).
-
-=== "Basic"
-
-    With this order, we want to rate different images based on a Likert scale to better understand how well the model generated the images we intended. We have various images that we want to evaluate, and we will assess how accurately they represent the desired concepts. When you run this with your own examples, you may use local paths to your images instead of the URLs.
-
-    The `NoShuffle` setting is used to ensure that the answer options remain in a fixed order, because the answer options are ordered.
-
-    ```python
-    --8<-- "examples/basic_classify_order_image.py"
-    ```
-
-    To preview the order and see what the annotators see, you can run the following code:
-
-    ```python
-    order.preview()
-    ```
-
-    To open the order in the browser, you can run the following code:
-
-    ```python
-    order.view()
-    ```
-
-=== "Advanced"
-    In the advanced example we will first create a validation set to give the annotators a reference how they should rate the images.
-
-    To get a better understanding of validation sets, please refer to the [Improve Quality](/improve_order_quality/) guide.
-
-    ```python
-    --8<-- "examples/advanced_classify_order.py"
-    ```
-
-    To preview the order and see what the annotators see, you can run the following code:
-
-    ```python
-    order.preview()
-    ```
-
-    To open the order in the browser, you can run the following code:
-
-    ```python
-    order.view()
-    ```
diff --git a/docs/examples/compare_job.md b/docs/examples/compare_job.md
new file mode 100644
index 000000000..70aa50a20
--- /dev/null
+++ b/docs/examples/compare_job.md
@@ -0,0 +1,57 @@
+# Compare Job Example
+
+To learn about the basics of creating a job, please refer to the [quickstart guide](../quickstart.md).
+
+In this example, we compare images from two image generation models (Flux and Midjourney) to determine which more accurately follows the given prompts.
+
+```python
+from rapidata import RapidataClient
+
+PROMPTS = [
+    "A sign that says 'Diffusion'.",
+    "A yellow flower sticking out of a green pot.",
+    "hyperrealism render of a surreal alien humanoid.",
+    "psychedelic duck",
+    "A small blue book sitting on a large red book."
+]
+
+IMAGE_PAIRS = [
+    ["https://assets.rapidata.ai/flux_sign_diffusion.jpg", "https://assets.rapidata.ai/mj_sign_diffusion.jpg"],
+    ["https://assets.rapidata.ai/flux_flower.jpg", "https://assets.rapidata.ai/mj_flower.jpg"],
+    ["https://assets.rapidata.ai/flux_alien.jpg", "https://assets.rapidata.ai/mj_alien.jpg"],
+    ["https://assets.rapidata.ai/flux_duck.jpg", "https://assets.rapidata.ai/mj_duck.jpg"],
+    ["https://assets.rapidata.ai/flux_book.jpg", "https://assets.rapidata.ai/mj_book.jpg"]
+]
+
+client = RapidataClient()
+
+# Create audience with qualification example
+audience = client.audience.create_audience(name="Prompt Alignment Audience")
+audience.add_compare_example(
+    instruction="Which image follows the prompt more accurately?",
+    datapoint=[
+        "https://assets.rapidata.ai/flux_sign_diffusion.jpg",
+        "https://assets.rapidata.ai/mj_sign_diffusion.jpg"
+    ],
+    truth="https://assets.rapidata.ai/flux_sign_diffusion.jpg",
+    context="A sign that says 'Diffusion'."
+)
+
+# Create job definition
+job_definition = client.job.create_compare_job_definition(
+    name="Example Image Prompt Alignment Job",
+    instruction="Which image follows the prompt more accurately?",
+    datapoints=IMAGE_PAIRS,
+    responses_per_datapoint=25,
+    contexts=PROMPTS
+)
+
+# Preview the job definition
+job_definition.preview()
+
+# Assign to audience and get results
+job = audience.assign_job_to_audience(job_definition)
+job.display_progress_bar()
+results = job.get_results()
+print(results)
+```
diff --git a/docs/examples/compare_order.md b/docs/examples/compare_order.md
deleted file mode 100644
index 82b21262e..000000000
--- a/docs/examples/compare_order.md
+++ /dev/null
@@ -1,49 +0,0 @@
-# Example Compare Order
-
-To learn about the basics of creating an order, please refer to the [quickstart guide](../quickstart.md).
-
-=== "Basic"
-
-    In this example we want to compare two image-to-text models, Flux and Midjourney, that have generated images based on a description, aka prompt. Those images have been saved to a public URL in order to be able to run the example anywhere. When you run this with your own examples, you may use local paths to your images instead of the URLs.
-
-    We now want to find out which of the two images more closely aligns with the prompt - for every prompt.
-
-    ```python
-    --8<-- "examples/basic_compare_order.py"
-    ```
-
-    To preview the order and see what the annotators see, you can run the following code:
-
-    ```python
-    order.preview()
-    ```
-
-    To open the order in the browser, you can run the following code:
-
-    ```python
-    order.view()
-    ```
-
-=== "Advanced"
-
-    In the advanced example we will first create a validation set to give the annotators a reference how they should rate the images.
-
-    To get a better understanding of validation sets, please refer to the [Improve Quality](/improve_order_quality/) guide.
-
-    ```python
-    --8<-- "examples/advanced_compare_order.py"
-    ```
-
-    To preview the order and see what the annotators see, you can run the following code:
-
-    ```python
-    order.preview()
-    ```
-
-    To open the order in the browser, you can run the following code:
-
-    ```python
-    order.view()
-    ```
-
-
diff --git a/docs/examples/draw_order.md b/docs/examples/draw_order.md
deleted file mode 100644
index 0b968f93e..000000000
--- a/docs/examples/draw_order.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Example Draw Order
-
-To learn about the basics of creating an order, please refer to the [quickstart guide](../quickstart.md).
-
-A powerful new capability of image generation models is their ability to edit images. But how do these models understand the location and size of objects they need to edit? In this example, we will create an order where annotators color in the object in images, providing training data that helps the model learn where to apply its edits.
-
-```python
---8<-- "examples/basic_draw_order.py"
-```
-
-To preview the order and see what the annotators see, you can run the following code:
-
-```python
-order.preview()
-```
-
-To open the order in the browser, you can run the following code:
-
-```python
-order.view()
-```
diff --git a/docs/examples/free_text_order.md b/docs/examples/free_text_order.md
deleted file mode 100644
index d9cc80aac..000000000
--- a/docs/examples/free_text_order.md
+++ /dev/null
@@ -1,48 +0,0 @@
-# Example Free Text Order
-
-To learn about the basics of creating an order, please refer to the [quickstart guide](../quickstart.md).
-
-=== "Basic"
-
-    Let's assume you want to build a new LLM chatbot and you want to know what people might want to ask the bot. You can create a free-text order to gather the questions people might have.
-
-    The free-text order will take longer to complete than the others, as the response process is slightly more involved from the perspective of the annotator.
-
-    ```python
-    --8<-- "examples/basic_free_text_order.py"
-    ```
-
-    To preview the order and see what the annotators see, you can run the following code:
-
-    ```python
-    order.preview()
-    ```
-
-    To open the order in the browser, you can run the following code:
-
-    ```python
-    order.view()
-    ```
-
-=== "Advanced"
-
-    In the advanced example we will add some filters to the order to make sure only annotators that have their phone set to english are able to answer. Additionally we will set a minimum length of 5 characters for the questions.
-
-    ```python
-    --8<-- "examples/advanced_free_text_order.py"
-    ```
-
-    To preview the order and see what the annotators see, you can run the following code:
-
-    ```python
-    order.preview()
-    ```
-
-    To open the order in the browser, you can run the following code:
-
-    ```python
-    order.view()
-    ```
-
-    
-    
diff --git a/docs/examples/locate_order.md b/docs/examples/locate_order.md
deleted file mode 100644
index bdeb7f406..000000000
--- a/docs/examples/locate_order.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Example Locate Order
-
-To learn about the basics of creating an order, please refer to the [quickstart guide](../quickstart.md).
-
-A big part of image generation is to make coherent images without any visual glitches. Rapidata makes it easy to get feedback on what parts of the image the model messed up. In this example, we will create an order where annotators are asked to select the parts of the image where there are visual glitches.
-
-```python
---8<-- "examples/basic_locate_order.py"
-```
-
-To preview the order and see what the annotators see, you can run the following code:
-
-```python
-order.preview()
-```
-
-To open the order in the browser, you can run the following code:
-
-```python
-order.view()
-```
diff --git a/docs/examples/ranking_order.md b/docs/examples/ranking_order.md
deleted file mode 100644
index 6f59b43a6..000000000
--- a/docs/examples/ranking_order.md
+++ /dev/null
@@ -1,23 +0,0 @@
-# Example Ranking Order
-
-To learn about the basics of creating an order, please refer to the [quickstart guide](../quickstart.md).
-
-In this example, we will create an order to rank various images of rabbits. The matchups will be generated automatically, comparing two images at a time. The ranking system is based on an Elo rating system, which updates rankings based on the results of these matchups.
-
-The instruction is designed to focus on the comparison between the two images rather than the overall ranking. For example, instead of asking "Which rabbit looks the coolest?", we ask "Which rabbit looks cooler?" to emphasize the specific matchup.
-
-```python
---8<-- "examples/basic_ranking_order.py"
-```
-
-To preview the order and see what the annotators see, you can run the following code:
-
-```python
-order.preview()
-```
-
-To open the order in the browser, you can run the following code:
-
-```python
-order.view()
-```
diff --git a/docs/examples/select_words_order.md b/docs/examples/select_words_order.md
deleted file mode 100644
index 6cc2016fb..000000000
--- a/docs/examples/select_words_order.md
+++ /dev/null
@@ -1,21 +0,0 @@
-# Example Select Words Order
-
-To learn about the basics of creating an order, please refer to the [quickstart guide](../quickstart.md).
-
-A big part of image generation is following the prompt accurately. Rapidata makes it easy to get feedback on what parts of the prompt the model is struggling with. In this example, we will create an order where annotators are asked to select the words that are not correctly depicted in the image.
-
-```python
---8<-- "examples/basic_select_words_order.py"
-```
-
-To preview the order and see what the annotators see, you can run the following code:
-
-```python
-order.preview()
-```
-
-To open the order in the browser, you can run the following code:
-
-```python
-order.view()
-```
diff --git a/docs/human_prompting.md b/docs/human_prompting.md
index c40edd482..05948edd6 100644
--- a/docs/human_prompting.md
+++ b/docs/human_prompting.md
@@ -1,10 +1,10 @@
 # Effective Instruction Design for Rapidata Tasks
 
-When creating tasks for human annotators using the Rapidata API, phrasing your instructions well can significantly improve quality and consistency of the responses you receive. This guide provides best practices for designing effective instructions for your Rapidata tasks.
+When creating tasks for human labelers using the Rapidata API, phrasing your instructions well can significantly improve quality and consistency of the responses you receive. This guide provides best practices for designing effective instructions for your Rapidata tasks.
 
 ## Time Constraints
 
-Each annotator session (specified in the selections) has a limited time window of 25 seconds to complete all tasks. With this in mind:
+Each labeler session has a limited time window of 25 seconds to complete all tasks. With this in mind:
 
 - **Be concise**: Keep instructions as brief as possible while maintaining clarity
 - **Use simple language**: Avoid complex terminology or jargon
@@ -12,7 +12,7 @@ Each annotator session (specified in the selections) has a limited time window o
 
 ## Language Clarity
 
-Since Rapidata tasks are presented to a diverse audience of annotators:
+Since Rapidata tasks are presented to a diverse audience of labelers:
 
 - **Use accessible language**: The average person should be able to understand your instructions clearly
 - **Avoid ambiguity**: Ensure there's only one way to interpret your instructions
@@ -36,7 +36,7 @@ Frame questions in the positive rather than negative. Positive questions are eas
 ```
 
 ### Limit Decision Criteria
-Don't overload annotators with multiple criteria in a single question.
+Don't overload labelers with multiple criteria in a single question.
 
 **Better:**
 ```
@@ -61,32 +61,38 @@ Provide distinct, non-overlapping response options.
 "Rate the image quality: bad/not good/fine/good/great"
 ```
 
-## Task Sequence Considerations
-
-When using multiple selections in a session, consider the cognitive load and time constraints:
-
-```python
-from rapidata import LabelingSelection, ValidationSelection
-
-# Keep the total number of tasks manageable for the 25-second window
-selections=[
-    ValidationSelection("67cafc95bc71604b08d8aa62", 1),  # Start with one validation task (id is the validation set id)
-    LabelingSelection(2)  # Follow with one labeling task
-]
-```
-
 ## Example Implementation
 
-When creating a Rapidata order, implement these principles as follows:
+When creating a Rapidata job, implement these principles as follows:
 
 ```python
-order = rapi.order.create_compare_order(
+from rapidata import RapidataClient
+
+client = RapidataClient()
+
+# Create audience with clear qualification example
+audience = client.audience.create_audience(name="Image Coherence Audience")
+audience.add_compare_example(
+    instruction="Which image has more glitches and is more likely to be AI generated?",
+    datapoint=[
+        "https://assets.rapidata.ai/good_ai_generated_image.png",
+        "https://assets.rapidata.ai/bad_ai_generated_image.png"
+    ],
+    truth="https://assets.rapidata.ai/bad_ai_generated_image.png"
+)
+
+# Create job definition with clear instruction
+job_definition = client.job.create_compare_job_definition(
     name="Image Coherence Comparison",
-    instruction="Which images has more glitches and is more likely to be AI generated?",  # Clear, positive framing
-    datapoints=[["https://assets.rapidata.ai/flux-1.1-pro/33_2.jpg", 
-    "https://assets.rapidata.ai/stable-diffusion-3/33_0.jpg"]],
-    selections=selections  # Include the above-defined selections
+    instruction="Which image has more glitches and is more likely to be AI generated?",
+    datapoints=[
+        ["https://assets.rapidata.ai/flux-1.1-pro/33_2.jpg",
+         "https://assets.rapidata.ai/stable-diffusion-3/33_0.jpg"]
+    ]
 )
+
+# Preview before running
+job_definition.preview()
 ```
 
 ## Common Task Types and Recommended Instructions
@@ -101,7 +107,7 @@ instruction="Which image do you prefer?"
 instruction="Which image matches the description better?"
 
 # Comparing image coherence
-instruction="Which images has more glitches and is more likely to be AI generated?"
+instruction="Which image has more glitches and is more likely to be AI generated?"
 
 # Comparing two texts
 instruction="Which of these sentences makes more sense?"
@@ -113,31 +119,30 @@ instruction="Which of these sentences makes more sense?"
 # Simple classification
 instruction="What object is in the image?"
 
-# Likert classification (add no shuffling setting)
-instruction="How well does the video match the description?
-answer_options=["1: Perfectly", 
-                "2: Very well", 
-                "3: Moderately", 
-                "4: A little", 
+# Likert classification (add NoShuffle setting)
+instruction="How well does the video match the description?"
+answer_options=["1: Perfectly",
+                "2: Very well",
+                "3: Moderately",
+                "4: A little",
                 "5: Not at all"]
 ```
 
 ## Monitoring and Iteration
 
-After launching your order, monitor the initial responses to see if annotators are understanding your instructions as intended.
+After assigning your job to an audience, monitor the initial responses to see if labelers are understanding your instructions as intended.
 
-You can see how the users will be presented with the task by calling the `.preview()` method on the order object:
+You can preview how users will see the task by calling the `.preview()` method on the job definition:
 ```python
-order.preview()
+job_definition.preview()
 ```
 
-If you see that annotators are giving inconsistent or incorrect answers:
+If you see that labelers are giving inconsistent or incorrect answers:
 
-1. Pause your order
-2. Review and simplify your instructions 
-3. Update your selections if needed
-4. Start a new order with the improved settings
+1. Review and simplify your instructions
+2. Update your audience's qualification examples if needed
+3. Create a new job definition with the improved settings
 
-This helps ensure you get high quality results from annotators.
+This helps ensure you get high quality results from labelers.
 
-For more information on creating and managing orders, refer to the [Rapidata API documentation](index.md) and [Understanding the Results](/understanding_the_results/) guide.
+For more information on creating and managing jobs, refer to the [Rapidata API documentation](starting_page.md) and [Understanding the Results](understanding_the_results.md) guide.
diff --git a/docs/improve_order_quality.md b/docs/improve_order_quality.md
deleted file mode 100644
index b9c499c4d..000000000
--- a/docs/improve_order_quality.md
+++ /dev/null
@@ -1,92 +0,0 @@
-# Improve Response Quality
-
-This guide builds on the [Quickstart](/quickstart/) and focuses on improving the quality of responses received for your orders. By creating a **Validation Set**, you can provide clear guidance to labelers, helping them better understand your expectations. While using a validation set is optional, it significantly enhances response accuracy and consistency, especially for more complex or unintuitive tasks.
-
-### Why Use a Validation Set?
-
-A validation set is a collection of tasks with known answers. It provides labelers with examples of how you want your data annotated, offering immediate feedback to align their work with your requirements.
-
-### How Does it Work?
-
-The validation task has a known truth and will be shown in front of the actual labeling task. Think of it as an interview question before the work starts. The labelers are only allowed to proceed to the actual task if they correctly solve one validation task.
-
-## Example Validation Set
-
-Let’s walk through how to create and use a validation set using the **Compare Order** as an example. The principles apply to any order type and the created validation set can be reused indefinitely.
-
-Here’s what labelers will see during validation for this example:
-
-![Compare Example](./media/order-types/good_bad_ai_image.png){ width="40%" }
-
-### Creating a Validation Set
-As always, we start by creating a [`RapidataClient`](reference/rapidata/rapidata_client/rapidata_client.md#rapidata.rapidata_client.rapidata_client.RapidataClient):
-
-```py
-from rapidata import RapidataClient
-
-rapi = RapidataClient()
-```
-
-All the validation-related operations are performed using `rapi.validation`. Here we create a compare validation set to be used in any future orders:
-
-The creation is structured in the same way as the order creation, except here we have to supply the correct answers as "truth" for each task.
-
-```py
-validation_set = rapi.validation.create_compare_set(
-     name="Example Compare Validation Set",
-     instruction="Which of the AI generated images looks more realistic?",
-     datapoints=[["https://assets.rapidata.ai/bad_ai_generated_image.png", 
-         "https://assets.rapidata.ai/good_ai_generated_image.png"]], 
-     truths=["https://assets.rapidata.ai/good_ai_generated_image.png"] 
-)
-```
-
-The parameters are as follows:
-
-- `name`: The name of the validation set. This is used to identify the validation set and to find it again later.
-- `criteria`: The criteria for the comparison. This is the question that the labeler will answer.
-- `datapoints`: The datapoints, each containing 2 images to compare.
-- `truths`: The truth, which image is the correct answer.
-
-The truths must be the same length as the datapoints and contain the correct answer for each datapoint.
-
-### Usage
-
-1. We can now use the validation set in any order we create. We first need to find the validation set we created:
-
-```py
-# find the validation set by name
-validation_set = rapi.validation.find_validation_sets("Example Compare Validation Set")[0] 
-
-# or by id
-validation_set = rapi.validation.get_validation_set_by_id("validation_set_id")
-```
-
-2. Now we can create a new order and add the validation set to it. It will automatically be shown in front of the datapoints we want to label. Ideally the criteria is the same or very closely related to the one in the validation set:
-
-```py
-order = rapi.order.create_compare_order(
-     name="Example Compare Validation Set",
-     instruction="Which of the AI generated images looks more realistic?", 
-     datapoints=[["https://assets.rapidata.ai/dalle-3_human.jpg", 
-        "https://assets.rapidata.ai/flux_human.jpg"]],
-     validation_set_id=validation_set.id
-).run()
-```
-
-If the labeler answers the validation task incorrectly, they will get warned and have to answer it correctly before they can proceed to the actual task.
-
-3. Finally we wait for the order to complete and look at the results:
-
-```py
-order.display_progress_bar()
-results = order.get_results()
-```
-
-The validation results will not be included in the final results, you'll only see the results of the actual tasks you wanted to have labeled. Likewise you'll only be charged credits for the actual tasks and not the validation tasks.
-
-### Next Steps
-
-You now know how to create and use a validation set to improve the quality of your orders. Check out our [example](/examples/compare_order/) for the benchmark described in [this paper](https://arxiv.org/abs/2409.11904) where we collected over 2 million responses! You may also have a look at the freely available [Huggingface dataset](https://huggingface.co/Rapidata). 
-
-Happy labeling!
diff --git a/docs/job_definition_parameters.md b/docs/job_definition_parameters.md
new file mode 100644
index 000000000..fe7496946
--- /dev/null
+++ b/docs/job_definition_parameters.md
@@ -0,0 +1,322 @@
+# Job Definition Parameter Reference
+
+This guide provides a comprehensive reference for all parameters available when creating job definitions in the Rapidata Python SDK.
+
+## Overview
+
+When creating a job definition, you'll use parameters to control:
+
+- **What data** is shown to labelers (datapoints, contexts)
+- **How many responses** you need (responses_per_datapoint)
+- **How tasks are displayed** (settings)
+- **Quality assurance** (confidence_threshold)
+
+---
+
+## Core Parameters
+
+These parameters are required or commonly used across all job types.
+
+### `name`
+
+| Property | Value |
+|----------|-------|
+| **Type** | `str` |
+| **Required** | Yes |
+
+A descriptive name for your job definition. Used to identify the job in the Rapidata Dashboard and when retrieving jobs programmatically. This name is **not shown to labelers**.
+
+```python
+name="Image Quality Rating v2 - January Batch"
+```
+
+---
+
+### `instruction`
+
+| Property | Value |
+|----------|-------|
+| **Type** | `str` |
+| **Required** | Yes |
+
+The task instruction shown to labelers. This should clearly explain what action they need to take.
+
+**Best Practices:**
+
+- Be specific and unambiguous
+- Use action verbs ("Select", "Choose", "Identify")
+- For comparisons, use comparative language ("Which looks better?")
+- See [Human Prompting](human_prompting.md) for detailed guidance
+
+```python
+instruction="Which image follows the prompt more accurately?"
+```
+
+---
+
+### `datapoints`
+
+| Property | Value |
+|----------|-------|
+| **Type** | `list[str]` or `list[list[str]]` |
+| **Required** | Yes |
+
+The data to be labeled. The format depends on the job type:
+
+| Job Type | Format | Description |
+|----------|--------|-------------|
+| Classification | `list[str]` | Single items to classify |
+| Compare | `list[list[str]]` | Pairs of items (exactly 2 per inner list) |
+
+**Supported Formats:**
+
+- Public URLs (https://...)
+- Local file paths (will be uploaded automatically)
+
+```python
+# Classification - list of single items
+datapoints=["https://example.com/img1.jpg", "https://example.com/img2.jpg"]
+
+# Compare - list of pairs
+datapoints=[
+    ["https://example.com/a1.jpg", "https://example.com/b1.jpg"],
+    ["https://example.com/a2.jpg", "https://example.com/b2.jpg"],
+]
+```
+
+---
+
+### `responses_per_datapoint`
+
+| Property | Value |
+|----------|-------|
+| **Type** | `int` |
+| **Required** | No |
+| **Default** | `10` |
+
+The minimum number of responses to collect for each datapoint. The actual number may slightly exceed this due to concurrent labelers.
+
+**Best Practices:**
+
+- Use 15-25 for ambiguous or subjective tasks
+- Use 5-10 for clear-cut decisions
+
+```python
+responses_per_datapoint=15
+```
+
+---
+
+## Data Type
+
+### `data_type`
+
+| Property | Value |
+|----------|-------|
+| **Type** | `Literal["media", "text"]` |
+| **Required** | No |
+| **Default** | `"media"` |
+
+Specifies how datapoints should be interpreted and displayed.
+
+| Value | Description |
+|-------|-------------|
+| `"media"` | Datapoints are URLs or paths to images, videos, or audio files |
+| `"text"` | Datapoints are raw text strings to be displayed directly |
+
+```python
+# Comparing two text responses
+job_definition = client.job.create_compare_job_definition(
+    name="LLM Response Comparison",
+    instruction="Which response is more helpful?",
+    datapoints=[
+        ["Response A text here...", "Response B text here..."],
+    ],
+    data_type="text",
+)
+```
+
+---
+
+## Context Parameters
+
+Context parameters allow you to provide additional information alongside each datapoint.
+
+### `contexts`
+
+| Property | Value |
+|----------|-------|
+| **Type** | `Optional[list[str]]` |
+| **Required** | No |
+| **Default** | `None` |
+
+Text context shown alongside each datapoint. Commonly used to provide prompts, descriptions, or additional instructions specific to each item.
+
+**Constraints:** If provided, must have the same length as `datapoints`.
+
+```python
+datapoints=["image1.jpg", "image2.jpg"],
+contexts=["A cat sitting on a red couch", "A blue car in the rain"]
+```
+
+---
+
+### `media_contexts`
+
+| Property | Value |
+|----------|-------|
+| **Type** | `Optional[list[str]]` |
+| **Required** | No |
+| **Default** | `None` |
+
+Media URLs shown as reference context alongside each datapoint. Useful when you need to show a reference image or video alongside the item being evaluated.
+
+**Constraints:** If provided, must have the same length as `datapoints`.
+
+```python
+# Show original image as context while evaluating edited versions
+datapoints=["edited1.jpg", "edited2.jpg"],
+media_contexts=["original1.jpg", "original2.jpg"]
+```
+
+---
+
+## Quality Control Parameters
+
+### `confidence_threshold`
+
+| Property | Value |
+|----------|-------|
+| **Type** | `Optional[float]` |
+| **Required** | No |
+| **Default** | `None` |
+| **Range** | `0.0` to `1.0` (typically `0.99` to `0.999`) |
+
+Enables early stopping when a specified confidence level is reached. The system stops collecting responses once consensus is achieved, reducing costs while maintaining quality.
+
+**How It Works:** Uses labeler trust scores (`userScore`) to calculate statistical confidence for each category.
+
+**Related:** [Confidence Stopping](confidence_stopping.md)
+
+```python
+job_definition = client.job.create_classification_job_definition(
+    name="Cat or Dog with Early Stopping",
+    instruction="What animal is in this image?",
+    answer_options=["Cat", "Dog"],
+    datapoints=["pet1.jpg", "pet2.jpg"],
+    responses_per_datapoint=50,  # Maximum responses
+    confidence_threshold=0.99,   # Stop at 99% confidence
+)
+```
+
+---
+
+## Settings
+
+Settings allow you to customize how tasks are displayed.
+
+| Property | Value |
+|----------|-------|
+| **Type** | `Sequence[RapidataSetting]` |
+| **Required** | No |
+| **Default** | `[]` |
+
+### Commonly Used Settings
+
+#### `NoShuffle()`
+
+Keeps answer options in the order you specified. By default, options are randomized to reduce bias. Use this for Likert scales or any ordered options.
+
+```python
+from rapidata import NoShuffle
+
+job_definition = client.job.create_classification_job_definition(
+    instruction="Rate the quality of this image",
+    answer_options=["1: Poor", "2: Fair", "3: Good", "4: Excellent"],
+    datapoints=["image.jpg"],
+    settings=[NoShuffle()]
+)
+```
+
+#### `Markdown()`
+
+Enables limited markdown rendering for text datapoints. Useful when comparing formatted text like LLM outputs.
+
+```python
+from rapidata import Markdown
+
+job_definition = client.job.create_compare_job_definition(
+    name="LLM Response Comparison",
+    instruction="Which response is better formatted?",
+    datapoints=[["**Bold** and _italic_", "Plain text only"]],
+    data_type="text",
+    settings=[Markdown()]
+)
+```
+
+#### `AllowNeitherBoth()`
+
+For Compare jobs, allows labelers to select "Neither" or "Both" instead of forcing a choice.
+
+```python
+from rapidata import AllowNeitherBoth
+
+job_definition = client.job.create_compare_job_definition(
+    name="Image Quality Comparison",
+    instruction="Which image is higher quality?",
+    datapoints=[["img_a.jpg", "img_b.jpg"]],
+    settings=[AllowNeitherBoth()]
+)
+```
+
+---
+
+## Job-Specific Parameters
+
+### Classification Job
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `answer_options` | `list[str]` | List of categories to classify into |
+
+```python
+job_definition = client.job.create_classification_job_definition(
+    name="Animal Classification",
+    instruction="What animal is in the image?",
+    answer_options=["Cat", "Dog", "Bird", "Other"],
+    datapoints=["image1.jpg", "image2.jpg"],
+)
+```
+
+### Compare Job
+
+| Parameter | Type | Description |
+|-----------|------|-------------|
+| `a_b_names` | `Optional[list[str]]` | Custom labels for the two options (list of exactly 2 strings) |
+
+```python
+job_definition = client.job.create_compare_job_definition(
+    name="Model Comparison",
+    instruction="Which image is better?",
+    datapoints=[["model_a.jpg", "model_b.jpg"]],
+    a_b_names=["Flux", "Midjourney"],  # Results will show these names
+)
+```
+
+---
+
+## Parameter Availability Matrix
+
+| Parameter | Classification | Compare |
+|-----------|:-:|:-:|
+| `name` | :white_check_mark: | :white_check_mark: |
+| `instruction` | :white_check_mark: | :white_check_mark: |
+| `datapoints` | :white_check_mark: | :white_check_mark: |
+| `responses_per_datapoint` | :white_check_mark: | :white_check_mark: |
+| `data_type` | :white_check_mark: | :white_check_mark: |
+| `contexts` | :white_check_mark: | :white_check_mark: |
+| `media_contexts` | :white_check_mark: | :white_check_mark: |
+| `confidence_threshold` | :white_check_mark: | :white_check_mark: |
+| `settings` | :white_check_mark: | :white_check_mark: |
+| `answer_options` | :white_check_mark: | :x: |
+| `a_b_names` | :x: | :white_check_mark: |
diff --git a/docs/quickstart.md b/docs/quickstart.md
index dc030a946..b92c99523 100644
--- a/docs/quickstart.md
+++ b/docs/quickstart.md
@@ -1,16 +1,12 @@
 # Quickstart Guide
 
-Directly ask real humans to compare your data. This guide will show you how to create a compare order using the Rapidata API.
+Get real humans to label your data. This guide shows you how to create a labeling job using the Rapidata API.
 
-There are many other types of orders you can create which you can find in the examples on the [Overview](index.md).
+The workflow consists of three main concepts:
 
-We will create an order assessing image-prompt-alignment, using 2 AI generated images and compare them against each other based on which image followed the prompt more accurately.
-
-Our annotators will then label the data according to the instruction we provided.
-
-They see the following screen:
-
-![Compare Example](./media/compare_quickstart.png){ width="40%" }
+1. **Audience**: A group of qualified labelers who have passed your qualification examples
+2. **Job Definition**: The configuration for your labeling task (instruction, datapoints, settings)
+3. **Job**: A running labeling task assigned to an audience
 
 ## Installation
 
@@ -20,117 +16,182 @@ Install Rapidata using pip:
 pip install -U rapidata
 ```
 
-
 ## Usage
 
-Orders are managed through the [`RapidataClient`](reference/rapidata/rapidata_client/rapidata_client.md#rapidata.rapidata_client.rapidata_client.RapidataClient).
+All operations are managed through the [`RapidataClient`](reference/rapidata/rapidata_client/rapidata_client.md#rapidata.rapidata_client.rapidata_client.RapidataClient).
 
-Create a client as follows, this will save your credentials in your `~/.config/rapidata/credentials.json` file so you don't have to log in again on that machine:
+Create a client as follows. This will save your credentials in your `~/.config/rapidata/credentials.json` file so you don't have to log in again on that machine:
 
 ```py
 from rapidata import RapidataClient
 
-#The first time executing it on a machine will require you to log in
-rapi = RapidataClient()
+# The first time executing it on a machine will require you to log in
+client = RapidataClient()
 ```
 
-Alternatively you can generate a Client ID and Secret in the [Rapidata Settings](https://app.rapidata.ai/settings/tokens) and pass them to the [`RapidataClient`](reference/rapidata/rapidata_client/rapidata_client.md#rapidata.rapidata_client.rapidata_client.RapidataClient) constructor:
+Alternatively you can generate a Client ID and Secret in the [Rapidata Settings](https://app.rapidata.ai/settings/tokens) and pass them to the client constructor:
 
 ```py
 from rapidata import RapidataClient
-rapi = RapidataClient(client_id="Your client ID", client_secret="Your client secret")
+client = RapidataClient(client_id="Your client ID", client_secret="Your client secret")
 ```
 
-### Creating an Order
+### Step 1: Create an Audience
+
+An audience is a pool of labelers who are qualified to work on your tasks. You create an audience and add qualification examples that labelers must answer correctly to join.
+
+```py
+audience = client.audience.create_audience(name="Image Comparison Audience")
+```
 
-All order-related operations are performed using rapi.order.
+### Step 2: Add Qualification Examples
 
-Here we create a compare order with a name and the instruction / question we want to ask. Additionally, we provide the prompt as context:
+Add examples that labelers must answer correctly to join your audience. This ensures only qualified labelers work on your data.
 
 ```py
-order = rapi.order.create_compare_order(
-    name="Example Alignment Order",
-    instruction="Which image matches the description better?",
-    contexts=["A small blue book sitting on a large red book."],
-    datapoints=[["https://assets.rapidata.ai/midjourney-5.2_37_3.jpg", 
-                "https://assets.rapidata.ai/flux-1-pro_37_0.jpg"]],
+audience.add_compare_example(
+    instruction="Which image follows the prompt more accurately?",
+    datapoint=[
+        "https://assets.rapidata.ai/flux_sign_diffusion.jpg",
+        "https://assets.rapidata.ai/mj_sign_diffusion.jpg"
+    ],
+    truth="https://assets.rapidata.ai/flux_sign_diffusion.jpg",
+    context="A sign that says 'Diffusion'."
 )
 ```
-> **Note:** When calling this function the data gets uploaded and prepared, but no annotators will start working on it yet.
 
-The parameters are as follows:
+The parameters are:
 
-- `name`: The name of the order. This is used to identify the order in the [Rapidata Dashboard](https://app.rapidata.ai/dashboard/orders). This name is also be used to find the order again later.
-- `instruction`: The instruction you want to show the annotators to select the image by.
-- `contexts`: The prompt that will be shown along side the two images and the instruction. (optional parameter)
-- `datapoints`: The image pairs we want to compare (order is randomized for every annotator). This can be any public URL (that points to an image, video or audio) or a local file path. This is a list of all datapoints you want to compare. Each datapoint consists of 2 files that are compared, as well as an optional context (which in this case is the prompt). The same instruction will be shown for each datapoint. There is a limit of 100 datapoints per order. If you need more than that, you can reach out to us at <info@rapidata.ai>.
+- `instruction`: The question shown to labelers
+- `datapoint`: The two items to compare
+- `truth`: The correct answer (must be one of the datapoint items)
+- `context`: Additional context shown alongside the comparison (optional)
 
-Optionally you may add additional specifications with the [other parameters](../reference/rapidata/rapidata_client/order/rapidata_order_manager/#rapidata.rapidata_client.order.rapidata_order_manager.RapidataOrderManager.create_compare_order). As an example, the `responses_per_datapoint` that specifies how many responses you want per datapoint<sup>1</sup>.
+### Step 3: Create a Job Definition
 
-Further more you can customize to whom, how and in what sequence the tasks are shown:
+A job definition configures what you want labeled. Here we create a compare job to assess image-prompt alignment:
 
-- [Filters](../reference/rapidata/rapidata_client/filter/rapidata_filters/) to specify who should work on the order
-- [Settings](../reference/rapidata/rapidata_client/settings/rapidata_settings/) to specify how the order should be shown
+```py
+job_definition = client.job.create_compare_job_definition(
+    name="Prompt Alignment Comparison",
+    instruction="Which image follows the prompt more accurately?",
+    datapoints=[
+        ["https://assets.rapidata.ai/flux_sign_diffusion.jpg",
+         "https://assets.rapidata.ai/mj_sign_diffusion.jpg"],
+        ["https://assets.rapidata.ai/flux_flower.jpg",
+         "https://assets.rapidata.ai/mj_flower.jpg"]
+    ],
+    contexts=[
+        "A sign that says 'Diffusion'.",
+        "A yellow flower sticking out of a green pot."
+    ]
+)
+```
 
-These customizations can be added to the order through the `filters` and `settings` parameters respectively.
+For a detailed explanation of all available parameters (including name, instruction, datapoints, contexts, quality control options, and more), see the [Job Definition Parameters Reference](job_definition_parameters.md).
 
-### Preview the Order
+### Step 4: Preview the Job Definition
 
-You can see how the users will be presented with the task by calling the `.preview()` method on the order object to make sure everything looks as expected:
+Before running your job, preview it to see exactly what labelers will see:
 
 ```py
-order.preview()
+job_definition.preview()
 ```
 
-### Start Collecting Responses
-To start the order and collect responses, call the `run` method:
+This opens your browser where you can review and adjust the job configuration.
+
+### Step 5: Assign Job to Audience
+
+Once you're satisfied with your job definition, assign it to your audience to start collecting responses:
 
 ```py
-order.run()
+job = audience.assign_job_to_audience(job_definition)
 ```
 
-Once you call this method, annotators will start working on your order immediately.
-
+Labelers who have passed your qualification examples will now start working on your data.
 
-### Retrieve Orders
+### Step 6: Monitor Progress and Get Results
 
-To retrieve old orders, you can use the `find_orders` method. This method allows you to filter by name and amount of orders to retrieve:
+Monitor progress on the [Rapidata Dashboard](https://app.rapidata.ai/dashboard) or programmatically:
 
 ```py
-example_orders = rapi.order.find_orders("Example Alignment Order")
-
-# if no name is provided it will just return the most recent one
-most_recent_order = rapi.order.find_orders()[0]
+job.display_progress_bar()
 ```
 
-Optionally you can also retrieve a specific order using the order ID:
+Once complete, retrieve your results:
 
 ```py
-order = rapi.order.get_order_by_id("order_id")
+results = job.get_results()
 ```
 
-### Monitoring Order Progress
+To understand the results format, see the [Understanding the Results](understanding_the_results.md) guide.
 
-You can monitor the progress of the order on the [Rapidata Dashboard](https://app.rapidata.ai/dashboard/orders) or by checking how many datapoints are already done with labeling:
+## Retrieve Existing Resources
+
+### Find Audiences
 
 ```py
-order.display_progress_bar()
-```
+# Find audiences by name
+audiences = client.audience.find_audiences("Image Comparison")
 
-### Downloading Results
+# Get a specific audience by ID
+audience = client.audience.get_audience_by_id("audience_id")
+```
 
-To download the results simply call the `get_results` method on the order:
+### Find Job Definitions
 
 ```py
-results = order.get_results()
+# Find job definitions by name
+job_definitions = client.job.find_job_definitions("Prompt Alignment")
+
+# Get a specific job definition by ID
+job_definition = client.job.get_job_defintion_by_id("job_definition_id")
 ```
 
-To better understand the results you can check out the [Understanding the Results](/understanding_the_results/) guide.
+## Complete Example
 
-## Next Steps
+Here's the full workflow in one script:
 
-This is just the beginning. You can create many different types of orders and customize them to your needs. Check out the [Overview](index.md) for more examples and information or check out how to improve the quality of your responses in the [Improve Quality](/improve_order_quality/) .
+```py
+from rapidata import RapidataClient
 
-------------------
+client = RapidataClient()
+
+# Create and configure audience
+audience = client.audience.create_audience(name="Prompt Alignment Audience")
+audience.add_compare_example(
+    instruction="Which image follows the prompt more accurately?",
+    datapoint=[
+        "https://assets.rapidata.ai/flux_sign_diffusion.jpg",
+        "https://assets.rapidata.ai/mj_sign_diffusion.jpg"
+    ],
+    truth="https://assets.rapidata.ai/flux_sign_diffusion.jpg",
+    context="A sign that says 'Diffusion'."
+)
+
+# Create job definition
+job_definition = client.job.create_compare_job_definition(
+    name="Prompt Alignment Job",
+    instruction="Which image follows the prompt more accurately?",
+    datapoints=[
+        ["https://assets.rapidata.ai/flux_flower.jpg",
+         "https://assets.rapidata.ai/mj_flower.jpg"]
+    ],
+    contexts=["A yellow flower sticking out of a green pot."]
+)
+
+# Preview before running
+job_definition.preview()
+
+# Assign to audience and get results
+job = audience.assign_job_to_audience(job_definition)
+job.display_progress_bar()
+results = job.get_results()
+print(results)
+```
+
+## Next Steps
 
-<sup>1</sup> Due to the possibility of multiple people answering at the same time, this number is treated as a minimum. The actual number of responses may be higher. The overshoot per datapoint will be lower the more datapoints are added.
+- Learn about [Classification Jobs](examples/classify_job.md) for categorizing data
+- Understand the [Results Format](understanding_the_results.md)
+- Configure [Early Stopping](confidence_stopping.md) based on confidence thresholds
diff --git a/docs/starting_page.md b/docs/starting_page.md
index 941fec5ec..19617d1cd 100644
--- a/docs/starting_page.md
+++ b/docs/starting_page.md
@@ -14,9 +14,7 @@ Set up your environment and make your first API request in minutes.
 
     ---
 
-    Compare two items and determine which one better matches your criteria.
-
-    *Estimated time: 5 minutes*
+    Create qualified audiences and have them label your data.
 
     ```python
     pip install -U rapidata
@@ -26,19 +24,34 @@ Set up your environment and make your first API request in minutes.
         ```python
         from rapidata import RapidataClient
 
-        rapi = RapidataClient()
+        client = RapidataClient()
 
-        order = rapi.order.create_compare_order(
+        # Create audience with qualification example
+        audience = client.audience.create_audience(name="Image Comparison Audience")
+        audience.add_compare_example(
+            instruction="Which image matches the description better?",
+            datapoint=[
+                "https://assets.rapidata.ai/flux_sign_diffusion.jpg",
+                "https://assets.rapidata.ai/mj_sign_diffusion.jpg"
+            ],
+            truth="https://assets.rapidata.ai/flux_sign_diffusion.jpg",
+            context="A sign that says 'Diffusion'."
+        )
+
+        # Create job definition
+        job_definition = client.job.create_compare_job_definition(
             name="Example Image Comparison",
             instruction="Which image matches the description better?",
             contexts=["A small blue book sitting on a large red book."],
-            datapoints=[["https://assets.rapidata.ai/midjourney-5.2_37_3.jpg", 
+            datapoints=[["https://assets.rapidata.ai/midjourney-5.2_37_3.jpg",
                         "https://assets.rapidata.ai/flux-1-pro_37_0.jpg"]],
-        ).run()
+        )
 
-        order.display_progress_bar()
-
-        results = order.get_results()
+        # Preview, then assign to audience
+        job_definition.preview()
+        job = audience.assign_job_to_audience(job_definition)
+        job.display_progress_bar()
+        results = job.get_results()
         print(results)
         ```
 
@@ -46,19 +59,34 @@ Set up your environment and make your first API request in minutes.
         ```python
         from rapidata import RapidataClient
 
-        rapi = RapidataClient()
+        client = RapidataClient()
 
-        order = rapi.order.create_compare_order(
+        # Create audience with qualification example
+        audience = client.audience.create_audience(name="Video Comparison Audience")
+        audience.add_compare_example(
+            instruction="Which video fits the description better?",
+            datapoint=[
+                "https://assets.rapidata.ai/0074_sora_1.mp4",
+                "https://assets.rapidata.ai/0074_hunyuan_1724.mp4"
+            ],
+            truth="https://assets.rapidata.ai/0074_sora_1.mp4",
+            context="A group of elephants painting murals."
+        )
+
+        # Create job definition
+        job_definition = client.job.create_compare_job_definition(
             name="Example Video Comparison",
             instruction="Which video fits the description better?",
-            contexts=["A group of elephants painting vibrant murals on a city wall during a lively street festival."],
-            datapoints=[["https://assets.rapidata.ai/0074_sora_1.mp4", 
+            contexts=["A group of elephants painting vibrant murals on a city wall."],
+            datapoints=[["https://assets.rapidata.ai/0074_sora_1.mp4",
                         "https://assets.rapidata.ai/0074_hunyuan_1724.mp4"]],
-        ).run()
+        )
 
-        order.display_progress_bar()
-
-        results = order.get_results()
+        # Preview, then assign to audience
+        job_definition.preview()
+        job = audience.assign_job_to_audience(job_definition)
+        job.display_progress_bar()
+        results = job.get_results()
         print(results)
         ```
 
@@ -66,40 +94,73 @@ Set up your environment and make your first API request in minutes.
         ```python
         from rapidata import RapidataClient, LanguageFilter
 
-        rapi = RapidataClient()
+        client = RapidataClient()
 
-        order = rapi.order.create_compare_order(
+        # Create audience with qualification example
+        audience = client.audience.create_audience(
+            name="Audio Comparison Audience",
+            filters=[LanguageFilter(["en"])]
+        )
+        audience.add_compare_example(
+            instruction="Which audio clip sounds more natural?",
+            datapoint=[
+                "https://assets.rapidata.ai/Chat_gpt.mp3",
+                "https://assets.rapidata.ai/ElevenLabs.mp3"
+            ],
+            truth="https://assets.rapidata.ai/ElevenLabs.mp3"
+        )
+
+        # Create job definition
+        job_definition = client.job.create_compare_job_definition(
             name="Example Audio Comparison",
             instruction="Which audio clip sounds more natural?",
-            datapoints=[["https://assets.rapidata.ai/Chat_gpt.mp3", 
+            datapoints=[["https://assets.rapidata.ai/Chat_gpt.mp3",
                         "https://assets.rapidata.ai/ElevenLabs.mp3"]],
-            filters=[LanguageFilter(["en"])]
-        ).run()
-
-        order.display_progress_bar()
+        )
 
-        results = order.get_results()
+        # Preview, then assign to audience
+        job_definition.preview()
+        job = audience.assign_job_to_audience(job_definition)
+        job.display_progress_bar()
+        results = job.get_results()
         print(results)
         ```
-    
+
     === "Text"
         ```python
         from rapidata import RapidataClient, LanguageFilter
 
-        rapi = RapidataClient()
+        client = RapidataClient()
 
-        order = rapi.order.create_compare_order(
+        # Create audience with qualification example
+        audience = client.audience.create_audience(
+            name="Text Comparison Audience",
+            filters=[LanguageFilter(["en"])]
+        )
+        audience.add_compare_example(
+            instruction="Which sentence is grammatically more correct?",
+            datapoint=[
+                "The children were amazed by the magician's tricks",
+                "The children were amusing by the magician's tricks."
+            ],
+            truth="The children were amazed by the magician's tricks",
+            data_type="text"
+        )
+
+        # Create job definition
+        job_definition = client.job.create_compare_job_definition(
             name="Example Text Comparison",
             instruction="Which sentence is grammatically more correct?",
-            datapoints=[["The children were amazed by the magician’s tricks", 
-                        "The children were amusing by the magician’s tricks."]],
+            datapoints=[["The children were amazed by the magician's tricks",
+                        "The children were amusing by the magician's tricks."]],
             data_type="text",
-            filters=[LanguageFilter(["en"])]
-        ).run()
-
-        order.display_progress_bar()
+        )
 
-        results = order.get_results()
+        # Preview, then assign to audience
+        job_definition.preview()
+        job = audience.assign_job_to_audience(job_definition)
+        job.display_progress_bar()
+        results = job.get_results()
         print(results)
         ```
     [:octicons-arrow-right-24: Let's go](quickstart.md)
diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css
index c56187fe2..f77c2963d 100644
--- a/docs/stylesheets/extra.css
+++ b/docs/stylesheets/extra.css
@@ -245,7 +245,7 @@
   /*--md-typeset-color:                var(--md-default-fg-color);*/
 
   /* Typeset `a` color shades */
-  --md-typeset-a-color:              var(--color-text-primary) !important;
+  --md-typeset-a-color:              var(--color-blue-400);
   /*--md-typeset-a-color:              var(--md-primary-fg-color); */
 
   /* Typeset `kbd` color shades */
diff --git a/docs/understanding_the_results.md b/docs/understanding_the_results.md
index 09f14cc7e..4f1515085 100644
--- a/docs/understanding_the_results.md
+++ b/docs/understanding_the_results.md
@@ -1,6 +1,6 @@
 # Interpreting the Results
 
-After running your comparison order and collecting responses, you'll receive a structured result containing valuable insights from the annotators. Understanding each component of this result is crucial for analyzing and utilizing the data effectively.
+After running your job and collecting responses, you'll receive a structured result containing valuable insights from the labelers. Understanding each component of this result is crucial for analyzing and utilizing the data effectively.
 
 Here's an example of the results you might receive when running a COMPARE task (for simplicity, this example uses 3 responses):
 
@@ -91,7 +91,7 @@ Here's an example of the results you might receive when running a COMPARE task (
     - `A_wins_total`: The total number of comparisons won by option A (index 0) across all pairs
     - `B_wins_total`: The total number of comparisons won by option B (index 1) across all pairs
 
-3. `results`: This section contains the actual comparison data collected from the annotators. For comparison orders, each item includes:
+3. `results`: This section contains the actual comparison data collected from the labelers. For comparison jobs, each item includes:
 
     - `context`: The prompt or description provided for the comparison task
     - `winner_index`: Index of the winning option (0 for first option, 1 for second option)
@@ -113,7 +113,7 @@ Here's an example of the results you might receive when running a COMPARE task (
         }
         ```
 
-    - `summedUserScores`: The sum of the annotators' global userScore values for each option. This metric accounts for the reliability of each annotator's response.
+    - `summedUserScores`: The sum of the labelers' global userScore values for each option. This metric accounts for the reliability of each labeler's response.
         ```json
         "summedUserScores": {
             "aurora-20-1-25_37_4.png": 0.0,
@@ -121,7 +121,7 @@ Here's an example of the results you might receive when running a COMPARE task (
         }
         ```
 
-    - `summedUserScoresRatios`: The proportion of the summed global userScores for each option, providing a weighted ratio based on annotator reliability.
+    - `summedUserScoresRatios`: The proportion of the summed global userScores for each option, providing a weighted ratio based on labeler reliability.
         ```json
         "summedUserScoresRatios": {
             "aurora-20-1-25_37_4.png": 0.0,
@@ -129,42 +129,42 @@ Here's an example of the results you might receive when running a COMPARE task (
         }
         ```
 
-    - `detailedResults`: A list of individual responses from each annotator, including:
-        - `votedFor`: The option chosen by the annotator
-        - `userDetails`: Information about the annotator
-            - `country`: Country code of the annotator
-            - `language`: Language in which the annotator viewed the task
-            - `userScores`: A score representing the annotator's reliability across different dimensions
-                - `global`: The global userScore of the annotator, which is a measure of their overall reliability
-            - `age`: Age group of the annotator
-            - `gender`: The gender of the annotator
-            - `occupation`: The occupation of the annotator
+    - `detailedResults`: A list of individual responses from each labeler, including:
+        - `votedFor`: The option chosen by the labeler
+        - `userDetails`: Information about the labeler
+            - `country`: Country code of the labeler
+            - `language`: Language in which the labeler viewed the task
+            - `userScores`: A score representing the labeler's reliability across different dimensions
+                - `global`: The global userScore of the labeler, which is a measure of their overall reliability
+            - `age`: Age group of the labeler
+            - `gender`: The gender of the labeler
+            - `occupation`: The occupation of the labeler
 
 ## Understanding the User Scores
 
-The `userScore` is a value between 0 and 1 (1 can never be reached, but can appear because of rounding) that indicates the reliability or trustworthiness of an annotator's responses. A higher score suggests that the annotator consistently provides accurate and reliable answers.
+The `userScore` is a value between 0 and 1 (1 can never be reached, but can appear because of rounding) that indicates the reliability or trustworthiness of a labeler's responses. A higher score suggests that the labeler consistently provides accurate and reliable answers.
 
 ### How is it Calculated?
 
-The `userScore` is derived from the annotator's performance on **Validation Tasks**—tasks with known correct answers. By evaluating how accurately an annotator completes these tasks, we assign a score that reflects their understanding and adherence to the task requirements. It is not simply the accuracy, as it also takes into account the difficulties of the tasks, but strongly related to it.
+The `userScore` is derived from the labeler's performance on **Qualification Tasks**—tasks with known correct answers. By evaluating how accurately a labeler completes these tasks, we assign a score that reflects their understanding and adherence to the task requirements. It is not simply the accuracy, as it also takes into account the difficulties of the tasks, but strongly related to it.
 
 For most tasks, the `global` userScore is the most relevant and can be used per default. If you need more specific information, you may contact us directly at <info@rapidata.ai>.
 
-To know more about the Validation Tasks have a look at the [Improve Order Quality](improve_order_quality.md) guide.
+Qualification tasks are examples with known correct answers that labelers must pass before working on your data.
 
 ### Why is it Important?
 
-- **Weighted Analysis**: Responses from annotators with higher `userScores` can be given more weight, improving the overall quality of the aggregated results.
+- **Weighted Analysis**: Responses from labelers with higher `userScores` can be given more weight, improving the overall quality of the aggregated results.
 - **Quality Control**: It helps in identifying and filtering for the most reliable responses.
-- **Insight into Annotator Performance**: Provides transparency into who is contributing to your data and how reliably.
+- **Insight into Labeler Performance**: Provides transparency into who is contributing to your data and how reliably.
 
 ## Utilizing the Results
 
 - **Clear Winners**: Use `winner` and `winner_index` to quickly identify which option was preferred. It is calculated based on the global userScores.
 - **Aggregated Insights**: Use `aggregatedResults` and `aggregatedResultsRatios` to understand the strength of preference between options
 - **Weighted Decisions**: Consider `summedUserScores` and `summedUserScoresRatios` to make decisions based on annotator reliability
-- **Detailed Analysis**: Explore `detailedResults` to see individual responses and gather insights about annotator demographics and performance
+- **Detailed Analysis**: Explore `detailedResults` to see individual responses and gather insights about labeler demographics and performance
 
 ## Conclusion
 
-By thoroughly understanding each component of the results, you can effectively interpret the data and make informed decisions. Leveraging the userScore and [validation sets](improve_order_quality.md) ensures high-quality, reliable data for your projects.
+By thoroughly understanding each component of the results, you can effectively interpret the data and make informed decisions. Leveraging the userScore and qualification examples ensures high-quality, reliable data for your projects.
diff --git a/hooks/search_boost.py b/hooks/search_boost.py
new file mode 100644
index 000000000..9679c6191
--- /dev/null
+++ b/hooks/search_boost.py
@@ -0,0 +1,74 @@
+"""MkDocs hook to boost search ranking for manually-written docs over auto-generated API reference."""
+
+import re
+from pathlib import Path
+
+
+def on_page_read_source(page, config) -> str | None:
+    """Inject search boost before MkDocs parses front matter.
+
+    - Manual docs (not in reference/) get boosted (10x)
+    - Auto-generated reference pages get demoted (0.2x)
+    """
+    page_path = page.file.src_path
+
+    # Check if source file exists (auto-generated pages may not have one)
+    src_file = Path(page.file.abs_src_path)
+    if not src_file.exists():
+        return None  # Let MkDocs handle auto-generated files differently
+
+    # Read the source file
+    with open(src_file, encoding="utf-8") as f:
+        source = f.read()
+
+    # Skip if page already has search config in front matter
+    if source.startswith("---"):
+        front_matter_match = re.match(r"^---\n(.*?)\n---", source, re.DOTALL)
+        if front_matter_match and "search:" in front_matter_match.group(1):
+            return None  # Don't modify
+
+    # Auto-generated reference pages get demoted, manual docs get boosted
+    if page_path.startswith("reference/"):
+        boost_value = 0.2  # Demote API reference
+    else:
+        boost_value = 10  # Strongly boost manual docs
+
+    # Inject front matter with search boost
+    if source.startswith("---"):
+        # Has existing front matter - inject search config after opening ---
+        source = re.sub(
+            r"^---\n",
+            f"---\nsearch:\n  boost: {boost_value}\n",
+            source,
+            count=1
+        )
+    else:
+        # No front matter - add it
+        source = f"---\nsearch:\n  boost: {boost_value}\n---\n\n{source}"
+
+    return source
+
+
+def on_page_markdown(markdown: str, page, config, files) -> str | None:
+    """Fallback for auto-generated pages that don't have source files.
+
+    This handles reference/ pages generated by mkdocs-gen-files.
+    """
+    page_path = page.file.src_path
+
+    # Only handle reference pages that weren't processed by on_page_read_source
+    if not page_path.startswith("reference/"):
+        return None
+
+    # Check if already has search metadata
+    if hasattr(page, 'meta') and page.meta.get('search', {}).get('boost') is not None:
+        return None
+
+    # Set the boost via page.meta directly for auto-generated pages
+    if not hasattr(page, 'meta') or page.meta is None:
+        page.meta = {}
+    if 'search' not in page.meta:
+        page.meta['search'] = {}
+    page.meta['search']['boost'] = 0.2
+
+    return None
diff --git a/mkdocs.yml b/mkdocs.yml
index 81bea8571..81430d4b8 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -8,9 +8,18 @@ extra_javascript:
 extra_css:
   - stylesheets/extra.css
 
+hooks:
+  - hooks/search_boost.py
+
 plugins:
   - search
   - mike
+  - glightbox:
+      touchNavigation: true
+      loop: false
+      effect: zoom
+      width: 100%
+      height: auto
   - autorefs:
       resolve_closest: true
   - mkdocstrings:
@@ -73,23 +82,14 @@ nav:
     - "🏠 Guides":
       - "👀 Overview": starting_page.md
       - "🚀 Quick Start": quickstart.md
+      - "📖 Parameter Reference": job_definition_parameters.md
       - "📊 Understanding Results": understanding_the_results.md
-      - "🎯 Improve Quality": improve_order_quality.md
       - "🛑 Confidence Stopping": confidence_stopping.md
       - "💡 Human Prompting": human_prompting.md
-    - "📋 Task Examples":
-      - "🏷️ Classification": examples/classify_order.md
-      - "⚖️ Compare": examples/compare_order.md
-      - "✍️ Free Text": examples/free_text_order.md
-      - "🔤 Select Words": examples/select_words_order.md
-      - "📍 Locate": examples/locate_order.md
-      - "✏️ Draw": examples/draw_order.md
-      - "📊 Ranking": examples/ranking_order.md
-    - "🔧 Advanced Configuration":
-      - "🔍 Filters": reference/rapidata/rapidata_client/filter/rapidata_filters.md
-      - "⚙️ Settings": reference/rapidata/rapidata_client/settings/rapidata_settings.md
-      - "📤 Selections": reference/rapidata/rapidata_client/selection/rapidata_selections.md
       - "📝 Logging & Config": config.md
+    - "📋 Job Examples":
+      - "🏷️ Classification": examples/classify_job.md
+      - "⚖️ Compare": examples/compare_job.md
     - "📈 Benchmarks":
       - "🧠 Model Ranking Insights": mri.md
       - "🔭 Model Ranking Insights Advanced": mri_advanced.md
diff --git a/pyproject.toml b/pyproject.toml
index 6a11be527..4c65b76a0 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -44,6 +44,7 @@ docs = [
     "mkdocs-section-index>=0.3.9,<0.4",
     "mike>=2.1.3,<3",
     "mkdocs-redirects>=1.2.2,<2",
+    "mkdocs-glightbox>=0.4.0,<1",
 ]
 
 [tool.hatch.build.targets.sdist]
diff --git a/src/rapidata/__init__.py b/src/rapidata/__init__.py
index e74fc7681..607b4af00 100644
--- a/src/rapidata/__init__.py
+++ b/src/rapidata/__init__.py
@@ -2,6 +2,8 @@
 
 from .rapidata_client import (
     RapidataClient,
+    RapidataAudience,
+    RapidataAudienceManager,
     DemographicSelection,
     LabelingSelection,
     EffortSelection,
@@ -32,9 +34,7 @@
     AgeGroup,
     GenderFilter,
     Gender,
-    Box,
     Datapoint,
-    PromptMetadata,
     FailedUploadException,
     rapidata_config,
     logger,
diff --git a/src/rapidata/rapidata_client/__init__.py b/src/rapidata/rapidata_client/__init__.py
index 4a6ee8a0b..26c6a6a18 100644
--- a/src/rapidata/rapidata_client/__init__.py
+++ b/src/rapidata/rapidata_client/__init__.py
@@ -1,4 +1,8 @@
 from .rapidata_client import RapidataClient
+from .audience import (
+    RapidataAudience,
+    RapidataAudienceManager,
+)
 from .selection import (
     DemographicSelection,
     LabelingSelection,
@@ -42,6 +46,5 @@
     OrFilter,
     ResponseCountFilter,
 )
-from .validation import Box
 from .exceptions import FailedUploadException
 from .config import rapidata_config, logger, managed_print
diff --git a/src/rapidata/rapidata_client/audience/__init__.py b/src/rapidata/rapidata_client/audience/__init__.py
new file mode 100644
index 000000000..2282e6738
--- /dev/null
+++ b/src/rapidata/rapidata_client/audience/__init__.py
@@ -0,0 +1,2 @@
+from .rapidata_audience import RapidataAudience
+from .rapidata_audience_manager import RapidataAudienceManager
diff --git a/src/rapidata/rapidata_client/audience/audience_example_handler.py b/src/rapidata/rapidata_client/audience/audience_example_handler.py
new file mode 100644
index 000000000..2dedea9c7
--- /dev/null
+++ b/src/rapidata/rapidata_client/audience/audience_example_handler.py
@@ -0,0 +1,167 @@
+from typing import Literal
+
+from rapidata.api_client.models.i_validation_truth_attach_category_truth import (
+    IValidationTruthAttachCategoryTruth,
+)
+from rapidata.api_client.models.i_rapid_payload_classify_payload import (
+    IRapidPayloadClassifyPayload,
+)
+from rapidata.api_client.models.classify_payload_category import ClassifyPayloadCategory
+from rapidata.api_client.models.i_rapid_payload_compare_payload import (
+    IRapidPayloadComparePayload,
+)
+from rapidata.api_client.models.i_validation_truth_compare_truth import (
+    IValidationTruthCompareTruth,
+)
+from rapidata.service.openapi_service import OpenAPIService
+from rapidata.api_client.models.i_rapid_payload import IRapidPayload
+from rapidata.api_client.models.i_validation_truth import IValidationTruth
+from rapidata.rapidata_client.datapoints._asset_uploader import AssetUploader
+
+
+class AudienceExampleHandler:
+    """
+    Can be used to build different types of examples. That can then be added to Example sets
+    """
+
+    def __init__(self, openapi_service: OpenAPIService, audience_id: str):
+        self._openapi_service = openapi_service
+        self._audience_id = audience_id
+        self._asset_uploader = AssetUploader(openapi_service)
+
+    def add_classification_example(
+        self,
+        instruction: str,
+        answer_options: list[str],
+        datapoint: str,
+        truth: list[str],
+        data_type: Literal["media", "text"] = "media",
+        context: str | None = None,
+        media_context: str | None = None,
+        explanation: str | None = None,
+    ) -> None:
+        """add a classification example to the audience
+
+        Args:
+            instruction (str): The instruction/question to be shown to the labeler.
+            answer_options (list[str]): The options that the labeler can choose from to answer the question.
+            datapoint (str): The datapoint that the labeler will be labeling.
+            truts (list[str]): The correct answers to the question.
+            data_type (str, optional): The type of the datapoint. Defaults to "media" (any form of image, video or audio).
+            context (str, optional): The context is text that will be shown in addition to the instruction. Defaults to None.
+            media_context (str, optional): The media context is a link to an image / video that will be shown in addition to the instruction (can be combined with context). Defaults to None.
+            explanation (str, optional): The explanation that will be shown to the labeler if the answer is wrong. Defaults to None.
+        """
+        from rapidata.api_client.models.add_rapid_to_audience_model import (
+            AddRapidToAudienceModel,
+        )
+
+        if not isinstance(truth, list):
+            raise ValueError("Truth must be a list of strings")
+
+        if not all(truth in answer_options for truth in truth):
+            raise ValueError("Truth must be part of the answer options")
+
+        if data_type == "media":
+            asset_input = self._asset_uploader.get_uploaded_asset_input(datapoint)
+        else:
+            asset_input = self._asset_uploader.get_uploaded_text_input(datapoint)
+
+        payload = IRapidPayload(
+            actual_instance=IRapidPayloadClassifyPayload(
+                _t="ClassifyPayload",
+                categories=[
+                    ClassifyPayloadCategory(label=option, value=option)
+                    for option in answer_options
+                ],
+                title=instruction,
+            )
+        )
+        model_truth = IValidationTruth(
+            actual_instance=IValidationTruthAttachCategoryTruth(
+                correctCategories=truth, _t="AttachCategoryTruth"
+            )
+        )
+
+        self._openapi_service.audience_api.audience_audience_id_rapid_post(
+            audience_id=self._audience_id,
+            add_rapid_to_audience_model=AddRapidToAudienceModel(
+                asset=asset_input,
+                payload=payload,
+                truth=model_truth,
+                context=context,
+                contextAsset=(
+                    self._asset_uploader.get_uploaded_asset_input(media_context)
+                    if media_context
+                    else None
+                ),
+                explanation=explanation,
+                randomCorrectProbability=len(truth) / len(answer_options),
+            ),
+        )
+
+    def add_compare_example(
+        self,
+        instruction: str,
+        truth: str,
+        datapoint: list[str],
+        data_type: Literal["media", "text"] = "media",
+        context: str | None = None,
+        media_context: str | None = None,
+        explanation: str | None = None,
+    ) -> None:
+        """add a compare example to the audience
+
+        Args:
+            instruction (str): The instruction that the labeler will be comparing the assets on.
+            truth (str): The correct answer to the comparison. (has to be one of the assets)
+            datapoint (list[str]): The two assets that the labeler will be comparing.
+            data_type (str, optional): The type of the datapoint. Defaults to "media" (any form of image, video or audio).
+            context (str, optional): The context is text that will be shown in addition to the instruction. Defaults to None.
+            media_context (str, optional): The media context is a link to an image / video that will be shown in addition to the instruction (can be combined with context). Defaults to None.
+            explanation (str, optional): The explanation that will be shown to the labeler if the answer is wrong. Defaults to None.
+        """
+        from rapidata.api_client.models.add_rapid_to_audience_model import (
+            AddRapidToAudienceModel,
+        )
+
+        payload = IRapidPayload(
+            actual_instance=IRapidPayloadComparePayload(
+                _t="ComparePayload", criteria=instruction
+            )
+        )
+        model_truth = IValidationTruth(
+            actual_instance=IValidationTruthCompareTruth(
+                _t="CompareTruth", winnerId=truth
+            )
+        )
+
+        if data_type == "media":
+            asset_input = self._asset_uploader.get_uploaded_asset_input(datapoint)
+        else:
+            asset_input = self._asset_uploader.get_uploaded_text_input(datapoint)
+
+        if len(datapoint) != 2:
+            raise ValueError("Compare rapid requires exactly two media paths")
+
+        self._openapi_service.audience_api.audience_audience_id_rapid_post(
+            audience_id=self._audience_id,
+            add_rapid_to_audience_model=AddRapidToAudienceModel(
+                asset=asset_input,
+                payload=payload,
+                truth=model_truth,
+                context=context,
+                contextAsset=(
+                    self._asset_uploader.get_uploaded_asset_input(media_context)
+                    if media_context
+                    else None
+                ),
+                explanation=explanation,
+            ),
+        )
+
+    def __str__(self) -> str:
+        return "RapidsManager"
+
+    def __repr__(self) -> str:
+        return self.__str__()
diff --git a/src/rapidata/rapidata_client/audience/rapidata_audience.py b/src/rapidata/rapidata_client/audience/rapidata_audience.py
new file mode 100644
index 000000000..5621871a0
--- /dev/null
+++ b/src/rapidata/rapidata_client/audience/rapidata_audience.py
@@ -0,0 +1,154 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Literal
+from rapidata.rapidata_client.config import logger, tracer, rapidata_config
+from rapidata.rapidata_client.audience.audience_example_handler import (
+    AudienceExampleHandler,
+)
+
+if TYPE_CHECKING:
+    from rapidata.service.openapi_service import OpenAPIService
+    from rapidata.rapidata_client.filter import RapidataFilter
+    from rapidata.rapidata_client.job.job_definition import (
+        JobDefinition,
+    )
+
+
+class RapidataAudience:
+    def __init__(
+        self,
+        id: str,
+        name: str,
+        filters: list[RapidataFilter],
+        openapi_service: OpenAPIService,
+    ):
+        self.id = id
+        self._name = name
+        self._filters = filters
+        self._openapi_service = openapi_service
+        self._example_handler = AudienceExampleHandler(openapi_service, id)
+
+    @property
+    def name(self) -> str:
+        return self._name
+
+    @property
+    def filters(self) -> list[RapidataFilter]:
+        return self._filters
+
+    def update_filters(self, filters: list[RapidataFilter]) -> RapidataAudience:
+        # will update the filters for the audience as soon as endpoint is ready
+        with tracer.start_as_current_span("RapidataAudience.update_filters"):
+            from rapidata.api_client.models.update_audience_request import (
+                UpdateAudienceRequest,
+            )
+
+            logger.debug(f"Updating filters for audience: {self.id} to {filters}")
+            self._openapi_service.audience_api.audience_audience_id_patch(
+                audience_id=self.id,
+                update_audience_request=UpdateAudienceRequest(
+                    filters=[filter._to_audience_model() for filter in filters],
+                ),
+            )
+            self._filters = filters
+            return self
+
+    def update_name(self, name: str) -> RapidataAudience:
+        with tracer.start_as_current_span("RapidataAudience.update_name"):
+            from rapidata.api_client.models.update_audience_request import (
+                UpdateAudienceRequest,
+            )
+
+            logger.debug(f"Updating name for audience: {self.id} to {name}")
+            self._openapi_service.audience_api.audience_audience_id_patch(
+                audience_id=self.id,
+                update_audience_request=UpdateAudienceRequest(name=name),
+            )
+            self._name = name
+            return self
+
+    def start_recruiting(self) -> RapidataAudience:
+        # will start the recruiting for the audience as soon as endpoint is ready
+        with tracer.start_as_current_span("RapidataAudience.start_recruiting"):
+            logger.debug(f"Sending request to start recruiting for audience: {self.id}")
+            self._openapi_service.audience_api.audience_audience_id_recruit_post(
+                audience_id=self.id,
+            )
+            logger.info(f"Started recruiting for audience: {self.id}")
+            return self
+
+    def assign_job_to_audience(self, job: JobDefinition) -> RapidataAudience:
+        with tracer.start_as_current_span("RapidataAudience.assign_job_to_audience"):
+            from rapidata.api_client.models.create_job_endpoint_input import (
+                CreateJobEndpointInput,
+            )
+
+            logger.debug(f"Assigning job to audience: {self.id}")
+            self._openapi_service.job_api.job_post(
+                create_job_endpoint_input=CreateJobEndpointInput(
+                    audienceId=self.id,
+                    jobDefinitionId=job._id,
+                ),
+            )
+            logger.info(f"Assigned job to audience: {self.id}")
+            return self
+
+    def add_classification_example(
+        self,
+        instruction: str,
+        answer_options: list[str],
+        datapoint: str,
+        truth: list[str],
+        data_type: Literal["media", "text"] = "media",
+        context: str | None = None,
+        media_context: str | None = None,
+        explanation: str | None = None,
+    ) -> RapidataAudience:
+        with tracer.start_as_current_span(
+            "RapidataAudience.add_classification_example"
+        ):
+            logger.debug(
+                f"Adding classification example to audience: {self.id} with instruction: {instruction}, answer_options: {answer_options}, datapoint: {datapoint}, truths: {truth}, data_type: {data_type}, context: {context}, media_context: {media_context}, explanation: {explanation}"
+            )
+            self._example_handler.add_classification_example(
+                instruction,
+                answer_options,
+                datapoint,
+                truth,
+                data_type,
+                context,
+                media_context,
+                explanation,
+            )
+            return self
+
+    def add_compare_example(
+        self,
+        instruction: str,
+        truth: str,
+        datapoint: list[str],
+        data_type: Literal["media", "text"] = "media",
+        context: str | None = None,
+        media_context: str | None = None,
+        explanation: str | None = None,
+    ) -> RapidataAudience:
+        with tracer.start_as_current_span("RapidataAudience.add_compare_example"):
+            logger.debug(
+                f"Adding compare example to audience: {self.id} with instruction: {instruction}, truth: {truth}, datapoint: {datapoint}, data_type: {data_type}, context: {context}, media_context: {media_context}, explanation: {explanation}"
+            )
+            self._example_handler.add_compare_example(
+                instruction,
+                truth,
+                datapoint,
+                data_type,
+                context,
+                media_context,
+                explanation,
+            )
+            return self
+
+    def __str__(self) -> str:
+        return f"RapidataAudience(id={self.id}, name={self._name}, filters={self._filters})"
+
+    def __repr__(self) -> str:
+        return self.__str__()
diff --git a/src/rapidata/rapidata_client/audience/rapidata_audience_manager.py b/src/rapidata/rapidata_client/audience/rapidata_audience_manager.py
new file mode 100644
index 000000000..3e3fdf712
--- /dev/null
+++ b/src/rapidata/rapidata_client/audience/rapidata_audience_manager.py
@@ -0,0 +1,134 @@
+from __future__ import annotations
+from typing import TYPE_CHECKING
+from rapidata.rapidata_client.config import tracer
+from rapidata.rapidata_client.config import logger
+
+if TYPE_CHECKING:
+    from rapidata.rapidata_client.audience.rapidata_audience import RapidataAudience
+    from rapidata.service.openapi_service import OpenAPIService
+    from rapidata.rapidata_client.filter import RapidataFilter
+
+
+class RapidataAudienceManager:
+    """
+    A manager for audiences.
+    Used to create and retrieve audiences.
+    """
+
+    def __init__(self, openapi_service: OpenAPIService):
+        self._openapi_service = openapi_service
+
+    def create_audience(
+        self,
+        name: str,
+        filters: list[RapidataFilter] | None = None,
+    ) -> RapidataAudience:
+
+        with tracer.start_as_current_span("RapidataAudienceManager.create_audience"):
+            from rapidata.rapidata_client.audience.rapidata_audience import (
+                RapidataAudience,
+            )
+            from rapidata.api_client.models.create_audience_request import (
+                CreateAudienceRequest,
+            )
+
+            logger.debug(f"Creating audience: {name}")
+            if filters is None:
+                filters = []
+            response = self._openapi_service.audience_api.audience_post(
+                create_audience_request=CreateAudienceRequest(
+                    name=name,
+                    filters=[filter._to_audience_model() for filter in filters],
+                ),
+            )
+            return RapidataAudience(
+                id=response.audience_id,
+                name=name,
+                filters=filters,
+                openapi_service=self._openapi_service,
+            )
+
+    def get_audience_by_id(self, audience_id: str) -> RapidataAudience:
+        with tracer.start_as_current_span("RapidataAudienceManager.get_audience_by_id"):
+            from rapidata.rapidata_client.filter._backend_filter_mapper import (
+                BackendFilterMapper,
+            )
+            from rapidata.rapidata_client.audience.rapidata_audience import (
+                RapidataAudience,
+            )
+
+            logger.debug(f"Getting audience by id: {audience_id}")
+            response = self._openapi_service.audience_api.audience_audience_id_get(
+                audience_id=audience_id,
+            )
+            return RapidataAudience(
+                id=audience_id,
+                name=response.name,
+                filters=[
+                    BackendFilterMapper.backend_filter_from_rapidata_filter(filter)
+                    for filter in response.filters
+                ],
+                openapi_service=self._openapi_service,
+            )
+
+    def find_audiences(
+        self, name: str = "", amount: int = 10
+    ) -> list[RapidataAudience]:
+        with tracer.start_as_current_span("RapidataAudienceManager.find_audiences"):
+            from rapidata.rapidata_client.filter._backend_filter_mapper import (
+                BackendFilterMapper,
+            )
+            from rapidata.api_client.models.page_info import PageInfo
+            from rapidata.api_client.models.query_model import QueryModel
+            from rapidata.api_client.models.root_filter import RootFilter
+            from rapidata.api_client.models.filter import Filter
+            from rapidata.api_client.models.filter_operator import FilterOperator
+            from rapidata.api_client.models.sort_criterion import SortCriterion
+            from rapidata.api_client.models.sort_direction import SortDirection
+            from rapidata.rapidata_client.audience.rapidata_audience import (
+                RapidataAudience,
+            )
+
+            logger.debug(f"Finding audiences: {name}, {amount}")
+            response = self._openapi_service.audience_api.audiences_get(
+                request=QueryModel(
+                    page=PageInfo(index=1, size=amount),
+                    filter=RootFilter(
+                        filters=[
+                            Filter(
+                                field="Name",
+                                operator=FilterOperator.CONTAINS,
+                                value=name,
+                            )
+                        ]
+                    ),
+                    sortCriteria=[
+                        SortCriterion(
+                            direction=SortDirection.DESC, propertyName="CreatedAt"
+                        )
+                    ],
+                )
+            )
+            audiences = []
+            for item in response.items:
+                audiences.append(
+                    RapidataAudience(
+                        id=item.id,
+                        name=item.name,
+                        filters=[
+                            BackendFilterMapper.backend_filter_from_rapidata_filter(
+                                filter
+                            )
+                            for filter in item.filters
+                        ],
+                        openapi_service=self._openapi_service,
+                    )
+                )
+
+            return audiences
+
+    def __str__(self) -> str:
+        return "RapidataAudienceManager"
+
+    def __repr__(self) -> str:
+        return self.__str__()
diff --git a/src/rapidata/rapidata_client/order/rapidata_results.py b/src/rapidata/rapidata_client/audience/rapidata_results.py
similarity index 100%
rename from src/rapidata/rapidata_client/order/rapidata_results.py
rename to src/rapidata/rapidata_client/audience/rapidata_results.py
diff --git a/src/rapidata/rapidata_client/benchmark/rapidata_benchmark.py b/src/rapidata/rapidata_client/benchmark/rapidata_benchmark.py
index 03093daa0..d72fcfa15 100644
--- a/src/rapidata/rapidata_client/benchmark/rapidata_benchmark.py
+++ b/src/rapidata/rapidata_client/benchmark/rapidata_benchmark.py
@@ -370,7 +370,7 @@ def create_leaderboard(
                         [filter._to_model() for filter in filters] if filters else None
                     ),
                     featureFlags=(
-                        [setting._to_feature_flag() for setting in settings]
+                        [setting._to_feature_flag() for setting in settings]  # type: ignore - until backend fixes generation
                         if settings
                         else None
                     ),
diff --git a/src/rapidata/rapidata_client/benchmark/rapidata_benchmark_manager.py b/src/rapidata/rapidata_client/benchmark/rapidata_benchmark_manager.py
index c3ec6e6ed..845e7b3e3 100644
--- a/src/rapidata/rapidata_client/benchmark/rapidata_benchmark_manager.py
+++ b/src/rapidata/rapidata_client/benchmark/rapidata_benchmark_manager.py
@@ -19,9 +19,6 @@ class RapidataBenchmarkManager:
     Used to create and retrieve benchmarks.
 
     A benchmark is a collection of leaderboards.
-
-    Args:
-        openapi_service: The OpenAPIService instance for API interaction.
     """
 
     def __init__(self, openapi_service: OpenAPIService):
diff --git a/src/rapidata/rapidata_client/dataset/__init__.py b/src/rapidata/rapidata_client/dataset/__init__.py
new file mode 100644
index 000000000..b1fc90b3f
--- /dev/null
+++ b/src/rapidata/rapidata_client/dataset/__init__.py
@@ -0,0 +1 @@
+from ._rapidata_dataset import RapidataDataset
diff --git a/src/rapidata/rapidata_client/dataset/_rapidata_dataset.py b/src/rapidata/rapidata_client/dataset/_rapidata_dataset.py
new file mode 100644
index 000000000..467966f47
--- /dev/null
+++ b/src/rapidata/rapidata_client/dataset/_rapidata_dataset.py
@@ -0,0 +1,55 @@
+from rapidata.rapidata_client.datapoints._datapoint import Datapoint
+from rapidata.service.openapi_service import OpenAPIService
+from rapidata.rapidata_client.config import logger
+from rapidata.rapidata_client.datapoints._datapoint_uploader import DatapointUploader
+from rapidata.rapidata_client.utils.threaded_uploader import ThreadedUploader
+
+
+class RapidataDataset:
+    def __init__(self, dataset_id: str, openapi_service: OpenAPIService):
+        self.id = dataset_id
+        self.openapi_service = openapi_service
+        self.datapoint_uploader = DatapointUploader(openapi_service)
+
+    def add_datapoints(
+        self,
+        datapoints: list[Datapoint],
+    ) -> tuple[list[Datapoint], list[Datapoint]]:
+        """
+        Process uploads in chunks with a ThreadPoolExecutor.
+
+        Args:
+            datapoints: List of datapoints to upload
+
+        Returns:
+            tuple[list[Datapoint], list[Datapoint]]: Lists of successful and failed uploads
+        """
+
+        def upload_single_datapoint(datapoint: Datapoint, index: int) -> None:
+            self.datapoint_uploader.upload_datapoint(
+                dataset_id=self.id,
+                datapoint=datapoint,
+                index=index,
+            )
+
+        uploader: ThreadedUploader[Datapoint] = ThreadedUploader(
+            upload_fn=upload_single_datapoint,
+            description="Uploading datapoints",
+        )
+
+        successful_uploads, failed_uploads = uploader.upload(datapoints)
+
+        if failed_uploads:
+            logger.error(
+                "Upload failed for %s datapoints: %s",
+                len(failed_uploads),
+                failed_uploads,
+            )
+
+        return successful_uploads, failed_uploads
+
+    def __str__(self) -> str:
+        return f"RapidataDataset(id={self.id})"
+
+    def __repr__(self) -> str:
+        return self.__str__()
diff --git a/src/rapidata/rapidata_client/exceptions/failed_upload_exception.py b/src/rapidata/rapidata_client/exceptions/failed_upload_exception.py
index f00ca58ec..41bd4198a 100644
--- a/src/rapidata/rapidata_client/exceptions/failed_upload_exception.py
+++ b/src/rapidata/rapidata_client/exceptions/failed_upload_exception.py
@@ -1,6 +1,10 @@
+from __future__ import annotations
 from rapidata.rapidata_client.datapoints._datapoint import Datapoint
-from rapidata.rapidata_client.order.dataset._rapidata_dataset import RapidataDataset
-from rapidata.rapidata_client.order.rapidata_order import RapidataOrder
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from rapidata.rapidata_client.dataset._rapidata_dataset import RapidataDataset
+    from rapidata.rapidata_client.job.job_definition import JobDefinition
 
 
 class FailedUploadException(Exception):
@@ -9,11 +13,11 @@ class FailedUploadException(Exception):
     def __init__(
         self,
         dataset: RapidataDataset,
-        order: RapidataOrder,
+        job: JobDefinition,
         failed_uploads: list[Datapoint],
     ):
         self.dataset = dataset
-        self.order = order
+        self.job = job
         self.failed_uploads = failed_uploads
 
     def __str__(self) -> str:
diff --git a/src/rapidata/rapidata_client/filter/_backend_filter_mapper.py b/src/rapidata/rapidata_client/filter/_backend_filter_mapper.py
new file mode 100644
index 000000000..f4ce1c818
--- /dev/null
+++ b/src/rapidata/rapidata_client/filter/_backend_filter_mapper.py
@@ -0,0 +1,134 @@
+from __future__ import annotations
+
+from typing import Any, TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from rapidata.api_client.models.i_audience_filter import (
+        IAudienceFilter,
+    )
+    from rapidata.rapidata_client.filter._base_filter import RapidataFilter
+
+
+class BackendFilterMapper:
+    """Maps backend API filter models to client-side RapidataFilter instances."""
+
+    # Cache for imported modules to avoid repeated imports
+    _imported_backend_filters: dict[str, Any] = {}
+    _imported_client_filters: dict[str, Any] = {}
+
+    @classmethod
+    def _ensure_backend_filters_imported(cls) -> None:
+        """Lazy import all backend filter models."""
+        if cls._imported_backend_filters:
+            return
+
+        from rapidata.api_client.models.i_audience_filter_and_audience_filter import (
+            IAudienceFilterAndAudienceFilter,
+        )
+        from rapidata.api_client.models.i_audience_filter_or_audience_filter import (
+            IAudienceFilterOrAudienceFilter,
+        )
+        from rapidata.api_client.models.i_audience_filter_not_audience_filter import (
+            IAudienceFilterNotAudienceFilter,
+        )
+        from rapidata.api_client.models.i_audience_filter_country_audience_filter import (
+            IAudienceFilterCountryAudienceFilter,
+        )
+        from rapidata.api_client.models.i_audience_filter_language_audience_filter import (
+            IAudienceFilterLanguageAudienceFilter,
+        )
+
+        cls._imported_backend_filters = {
+            "AndAudienceFilter": IAudienceFilterAndAudienceFilter,
+            "OrAudienceFilter": IAudienceFilterOrAudienceFilter,
+            "NotAudienceFilter": IAudienceFilterNotAudienceFilter,
+            "CountryAudienceFilter": IAudienceFilterCountryAudienceFilter,
+            "LanguageAudienceFilter": IAudienceFilterLanguageAudienceFilter,
+        }
+
+    @classmethod
+    def _ensure_client_filters_imported(cls) -> None:
+        """Lazy import all client filter models."""
+        if cls._imported_client_filters:
+            return
+
+        from rapidata.rapidata_client.filter.and_filter import AndFilter
+        from rapidata.rapidata_client.filter.country_filter import CountryFilter
+        from rapidata.rapidata_client.filter.language_filter import LanguageFilter
+        from rapidata.rapidata_client.filter.not_filter import NotFilter
+        from rapidata.rapidata_client.filter.or_filter import OrFilter
+
+        cls._imported_client_filters = {
+            "AndFilter": AndFilter,
+            "CountryFilter": CountryFilter,
+            "LanguageFilter": LanguageFilter,
+            "NotFilter": NotFilter,
+            "OrFilter": OrFilter,
+        }
+
+    @classmethod
+    def backend_filter_from_rapidata_filter(
+        cls, filter: IAudienceFilter
+    ) -> RapidataFilter:
+        """Convert a backend API filter model to a client-side RapidataFilter instance.
+
+        Args:
+            filter: Backend API filter wrapped in IAudienceFilter
+
+        Returns:
+            RapidataFilter: Client-side filter instance
+        """
+        cls._ensure_backend_filters_imported()
+        cls._ensure_client_filters_imported()
+
+        actual_instance = filter.actual_instance
+        if actual_instance is None:
+            raise ValueError("Filter actual_instance is None")
+
+        # Import backend models for isinstance checks
+        BackendAndAudienceFilter = cls._imported_backend_filters["AndAudienceFilter"]
+        BackendOrAudienceFilter = cls._imported_backend_filters["OrAudienceFilter"]
+        BackendNotAudienceFilter = cls._imported_backend_filters["NotAudienceFilter"]
+        BackendCountryAudienceFilter = cls._imported_backend_filters[
+            "CountryAudienceFilter"
+        ]
+        BackendLanguageAudienceFilter = cls._imported_backend_filters[
+            "LanguageAudienceFilter"
+        ]
+
+        # Import client models
+        ClientAndFilter = cls._imported_client_filters["AndFilter"]
+        ClientOrFilter = cls._imported_client_filters["OrFilter"]
+        ClientNotFilter = cls._imported_client_filters["NotFilter"]
+        ClientCountryFilter = cls._imported_client_filters["CountryFilter"]
+        ClientLanguageFilter = cls._imported_client_filters["LanguageFilter"]
+
+        # Handle recursive filters (AndFilter, OrFilter, NotFilter)
+        if isinstance(actual_instance, BackendAndAudienceFilter):
+            return ClientAndFilter(
+                [
+                    cls.backend_filter_from_rapidata_filter(child_filter)
+                    for child_filter in actual_instance.filters  # type: ignore[attr-defined]
+                ]
+            )
+        elif isinstance(actual_instance, BackendOrAudienceFilter):
+            return ClientOrFilter(
+                [
+                    cls.backend_filter_from_rapidata_filter(child_filter)
+                    for child_filter in actual_instance.filters  # type: ignore[attr-defined]
+                ]
+            )
+        elif isinstance(actual_instance, BackendNotAudienceFilter):
+            return ClientNotFilter(
+                cls.backend_filter_from_rapidata_filter(actual_instance.filter)  # type: ignore[attr-defined]
+            )
+
+        # Handle simple filters with straightforward mappings
+        elif isinstance(actual_instance, BackendCountryAudienceFilter):
+            return ClientCountryFilter(actual_instance.countries)  # type: ignore[attr-defined]
+        elif isinstance(actual_instance, BackendLanguageAudienceFilter):
+            return ClientLanguageFilter(actual_instance.languages)  # type: ignore[attr-defined]
+
+        else:
+            backend_type_name = type(actual_instance).__name__
+            raise ValueError(f"Unhandled filter type: {backend_type_name}")
diff --git a/src/rapidata/rapidata_client/filter/_base_filter.py b/src/rapidata/rapidata_client/filter/_base_filter.py
index a47c71e47..1cb91bd8b 100644
--- a/src/rapidata/rapidata_client/filter/_base_filter.py
+++ b/src/rapidata/rapidata_client/filter/_base_filter.py
@@ -1,6 +1,7 @@
 from abc import abstractmethod
 
 from rapidata.api_client.models.i_user_filter_model import IUserFilterModel
+from rapidata.api_client.models.i_audience_filter import IAudienceFilter
 
 
 class RapidataFilter:
@@ -10,6 +11,10 @@ class RapidataFilter:
     def _to_model(self) -> IUserFilterModel:
         pass
 
+    @abstractmethod
+    def _to_audience_model(self) -> IAudienceFilter:
+        pass
+
     def __or__(self, other):
         """Enable the | operator to create OrFilter combinations."""
         if not isinstance(other, RapidataFilter):
diff --git a/src/rapidata/rapidata_client/filter/age_filter.py b/src/rapidata/rapidata_client/filter/age_filter.py
index 52c88718f..89ca56236 100644
--- a/src/rapidata/rapidata_client/filter/age_filter.py
+++ b/src/rapidata/rapidata_client/filter/age_filter.py
@@ -29,6 +29,11 @@ def _to_model(self):
         return IUserFilterModel(
             actual_instance=IUserFilterModelAgeUserFilterModel(
                 _t="AgeFilter",
-                ageGroups=[age_group._to_backend_model() for age_group in self.age_groups],
+                ageGroups=[
+                    age_group._to_backend_model() for age_group in self.age_groups
+                ],
             )
         )
+
+    def _to_audience_model(self):
+        raise NotImplementedError("AgeFilter is not supported for audiences")
diff --git a/src/rapidata/rapidata_client/filter/and_filter.py b/src/rapidata/rapidata_client/filter/and_filter.py
index 5ba0bea66..0c9a532d6 100644
--- a/src/rapidata/rapidata_client/filter/and_filter.py
+++ b/src/rapidata/rapidata_client/filter/and_filter.py
@@ -40,3 +40,16 @@ def _to_model(self):
                 filters=[filter._to_model() for filter in self.filters],
             )
         )
+
+    def _to_audience_model(self):
+        from rapidata.api_client.models.i_audience_filter import IAudienceFilter
+        from rapidata.api_client.models.i_audience_filter_and_audience_filter import (
+            IAudienceFilterAndAudienceFilter,
+        )
+
+        return IAudienceFilter(
+            actual_instance=IAudienceFilterAndAudienceFilter(
+                _t="AndFilter",
+                filters=[filter._to_audience_model() for filter in self.filters],
+            ),
+        )
diff --git a/src/rapidata/rapidata_client/filter/campaign_filter.py b/src/rapidata/rapidata_client/filter/campaign_filter.py
index 6982beec1..11bd2880b 100644
--- a/src/rapidata/rapidata_client/filter/campaign_filter.py
+++ b/src/rapidata/rapidata_client/filter/campaign_filter.py
@@ -32,3 +32,6 @@ def _to_model(self):
                 campaignIds=self.campaign_ids,
             )
         )
+
+    def _to_campaign_model(self):
+        raise NotImplementedError("CampaignFilter is not supported for audiences")
diff --git a/src/rapidata/rapidata_client/filter/country_filter.py b/src/rapidata/rapidata_client/filter/country_filter.py
index 64c675dd4..c774d660f 100644
--- a/src/rapidata/rapidata_client/filter/country_filter.py
+++ b/src/rapidata/rapidata_client/filter/country_filter.py
@@ -46,3 +46,15 @@ def _to_model(self):
                 _t="CountryFilter", countries=self.country_codes
             )
         )
+
+    def _to_audience_model(self):
+        from rapidata.api_client.models.i_audience_filter import IAudienceFilter
+        from rapidata.api_client.models.i_audience_filter_country_filter import (
+            IAudienceFilterCountryFilter,
+        )
+
+        return IAudienceFilter(
+            actual_instance=IAudienceFilterCountryFilter(
+                _t="CountryFilter", countries=self.country_codes
+            )
+        )
diff --git a/src/rapidata/rapidata_client/filter/custom_filter.py b/src/rapidata/rapidata_client/filter/custom_filter.py
index cd54603f2..bb8622fb2 100644
--- a/src/rapidata/rapidata_client/filter/custom_filter.py
+++ b/src/rapidata/rapidata_client/filter/custom_filter.py
@@ -37,3 +37,6 @@ def _to_model(self):
                 values=self.values,
             )
         )
+
+    def _to_audience_model(self):
+        raise NotImplementedError("CustomFilter cannot be used in campaigns")
diff --git a/src/rapidata/rapidata_client/filter/gender_filter.py b/src/rapidata/rapidata_client/filter/gender_filter.py
index 6b98e8d54..368e556d9 100644
--- a/src/rapidata/rapidata_client/filter/gender_filter.py
+++ b/src/rapidata/rapidata_client/filter/gender_filter.py
@@ -33,3 +33,6 @@ def _to_model(self):
                 genders=[gender._to_backend_model() for gender in self.genders],
             )
         )
+
+    def _to_audience_model(self):
+        raise NotImplementedError("GenderFilter is not supported for audiences")
diff --git a/src/rapidata/rapidata_client/filter/language_filter.py b/src/rapidata/rapidata_client/filter/language_filter.py
index 8519e9873..90e16acba 100644
--- a/src/rapidata/rapidata_client/filter/language_filter.py
+++ b/src/rapidata/rapidata_client/filter/language_filter.py
@@ -53,3 +53,15 @@ def _to_model(self):
                 languages=self.language_codes,
             )
         )
+
+    def _to_audience_model(self):
+        from rapidata.api_client.models.i_audience_filter import IAudienceFilter
+        from rapidata.api_client.models.i_audience_filter_language_filter import (
+            IAudienceFilterLanguageFilter,
+        )
+
+        return IAudienceFilter(
+            actual_instance=IAudienceFilterLanguageFilter(
+                _t="LanguageFilter", languages=self.language_codes
+            )
+        )
diff --git a/src/rapidata/rapidata_client/filter/new_user_filter.py b/src/rapidata/rapidata_client/filter/new_user_filter.py
index 6409dbade..5a788b87a 100644
--- a/src/rapidata/rapidata_client/filter/new_user_filter.py
+++ b/src/rapidata/rapidata_client/filter/new_user_filter.py
@@ -18,3 +18,6 @@ def _to_model(self):
         return IUserFilterModel(
             actual_instance=IUserFilterModelNewUserFilterModel(_t="NewUserFilter")
         )
+
+    def _to_audience_model(self):
+        raise NotImplementedError("NewUserFilter is not supported for audiences")
diff --git a/src/rapidata/rapidata_client/filter/not_filter.py b/src/rapidata/rapidata_client/filter/not_filter.py
index 8a0b2b7d0..513c4b935 100644
--- a/src/rapidata/rapidata_client/filter/not_filter.py
+++ b/src/rapidata/rapidata_client/filter/not_filter.py
@@ -40,3 +40,15 @@ def _to_model(self):
                 filter=self.filter._to_model(),
             )
         )
+
+    def _to_audience_model(self):
+        from rapidata.api_client.models.i_audience_filter import IAudienceFilter
+        from rapidata.api_client.models.i_audience_filter_not_audience_filter import (
+            IAudienceFilterNotAudienceFilter,
+        )
+
+        return IAudienceFilter(
+            actual_instance=IAudienceFilterNotAudienceFilter(
+                _t="NotFilter", filter=self.filter._to_audience_model()
+            )
+        )
diff --git a/src/rapidata/rapidata_client/filter/or_filter.py b/src/rapidata/rapidata_client/filter/or_filter.py
index dd9ce76ac..b93bb7d3c 100644
--- a/src/rapidata/rapidata_client/filter/or_filter.py
+++ b/src/rapidata/rapidata_client/filter/or_filter.py
@@ -40,3 +40,16 @@ def _to_model(self):
                 filters=[filter._to_model() for filter in self.filters],
             )
         )
+
+    def _to_audience_model(self):
+        from rapidata.api_client.models.i_audience_filter import IAudienceFilter
+        from rapidata.api_client.models.i_audience_filter_or_audience_filter import (
+            IAudienceFilterOrAudienceFilter,
+        )
+
+        return IAudienceFilter(
+            actual_instance=IAudienceFilterOrAudienceFilter(
+                _t="OrFilter",
+                filters=[filter._to_audience_model() for filter in self.filters],
+            )
+        )
diff --git a/src/rapidata/rapidata_client/filter/rapidata_filters.py b/src/rapidata/rapidata_client/filter/rapidata_filters.py
index 264f29af6..a67d790e9 100644
--- a/src/rapidata/rapidata_client/filter/rapidata_filters.py
+++ b/src/rapidata/rapidata_client/filter/rapidata_filters.py
@@ -1,7 +1,5 @@
 from rapidata.rapidata_client.filter import (
-    AgeFilter,
     CountryFilter,
-    GenderFilter,
     LanguageFilter,
     UserScoreFilter,
     NotFilter,
@@ -22,9 +20,7 @@ class RapidataFilters:
 
     Attributes:
         user_score (UserScoreFilter): Filters for users with a specific user score.
-        age (AgeFilter): Filters for users with a specific age.
         country (CountryFilter): Filters for users with a specific country.
-        gender (GenderFilter): Filters for users with a specific gender.
         language (LanguageFilter): Filters for users with a specific language.
         not_filter (NotFilter): Inverts the filter.
         or_filter (OrFilter): Combines multiple filters with a logical OR operation.
@@ -43,18 +39,15 @@ class RapidataFilters:
         The AND is additionally given by the elements in the list.
 
         ```python
-        from rapidata import AgeFilter, LanguageFilter, CountryFilter
-        filters=[~AgeFilter([AgeGroup.UNDER_18]), CountryFilter(["US"]) | (CountryFilter(["CA"]) & LanguageFilter(["en"]))]
+        from rapidata import LanguageFilter, CountryFilter
+        filters=[(CountryFilter(["US"]) & ~LanguageFilter(["fr"])) | (CountryFilter(["CA"]) & LanguageFilter(["en"]))]
         ```
 
-        This would return users who are not under 18 years old and are from the US or who are from Canada and whose phones are set to English.
+        This would return users who are from the US and whose phones are not set to French or who are from Canada and whose phones are set to English.
     """
 
-    user_score = UserScoreFilter
-    age = AgeFilter
-    country = CountryFilter
-    gender = GenderFilter
-    language = LanguageFilter
-    not_filter = NotFilter
-    or_filter = OrFilter
-    and_filter = AndFilter
+    Country = CountryFilter
+    Language = LanguageFilter
+    Not_filter = NotFilter
+    Or_filter = OrFilter
+    And_filter = AndFilter
diff --git a/src/rapidata/rapidata_client/filter/response_count_filter.py b/src/rapidata/rapidata_client/filter/response_count_filter.py
index 40d9d4e80..2b8f54b80 100644
--- a/src/rapidata/rapidata_client/filter/response_count_filter.py
+++ b/src/rapidata/rapidata_client/filter/response_count_filter.py
@@ -60,3 +60,18 @@ def _to_model(self):
                 operator=self.operator,
             )
         )
+
+    def _to_campaign_model(self):
+        from rapidata.api_client.models.i_campaign_filter import ICampaignFilter
+        from rapidata.api_client.models.i_campaign_filter_response_count_filter import (
+            ICampaignFilterResponseCountFilter,
+        )
+
+        return ICampaignFilter(
+            actual_instance=ICampaignFilterResponseCountFilter(
+                _t="ResponseCountFilter",
+                responseCount=self.response_count,
+                dimension=self.dimension,
+                operator=self.operator,
+            )
+        )
diff --git a/src/rapidata/rapidata_client/filter/user_score_filter.py b/src/rapidata/rapidata_client/filter/user_score_filter.py
index 4bb56ddbc..2ee0e675d 100644
--- a/src/rapidata/rapidata_client/filter/user_score_filter.py
+++ b/src/rapidata/rapidata_client/filter/user_score_filter.py
@@ -63,3 +63,18 @@ def _to_model(self):
                 dimension=self.dimension,
             )
         )
+
+    def _to_campaign_model(self):
+        from rapidata.api_client.models.i_campaign_filter import ICampaignFilter
+        from rapidata.api_client.models.i_campaign_filter_user_score_filter import (
+            ICampaignFilterUserScoreFilter,
+        )
+
+        return ICampaignFilter(
+            actual_instance=ICampaignFilterUserScoreFilter(
+                _t="UserScoreFilter",
+                upperbound=self.upper_bound,
+                lowerbound=self.lower_bound,
+                dimension=self.dimension if self.dimension else "global",
+            )
+        )
diff --git a/src/rapidata/rapidata_client/job/__init__.py b/src/rapidata/rapidata_client/job/__init__.py
new file mode 100644
index 000000000..c5bcafe5a
--- /dev/null
+++ b/src/rapidata/rapidata_client/job/__init__.py
@@ -0,0 +1,2 @@
+from .job_definition import JobDefinition
+from .job_manager import JobManager
diff --git a/src/rapidata/rapidata_client/job/job_definition.py b/src/rapidata/rapidata_client/job/job_definition.py
new file mode 100644
index 000000000..604eb3d46
--- /dev/null
+++ b/src/rapidata/rapidata_client/job/job_definition.py
@@ -0,0 +1,103 @@
+from rapidata.rapidata_client.exceptions.failed_upload_exception import (
+    FailedUploadException,
+)
+from typing import Literal
+from rapidata.service.openapi_service import OpenAPIService
+from rapidata.rapidata_client.config import tracer, logger, managed_print
+import webbrowser
+import urllib.parse
+from colorama import Fore
+
+
+class JobDefinition:
+    def __init__(
+        self,
+        id: str,
+        name: str,
+        openapi_service: OpenAPIService,
+    ):
+        self._id = id
+        self._name = name
+        self._openapi_service = openapi_service
+        self._job_details_page = (
+            f"https://app.{self._openapi_service.environment}/definitions/{self._id}"
+        )
+
+    def preview(self) -> None:
+        """Will open the browser where you can preview the job definition before giving it to an audience."""
+        logger.info("Opening order details page in browser...")
+        if not webbrowser.open(self._job_details_page):
+            encoded_url = urllib.parse.quote(
+                self._job_details_page, safe="%/:=&?~#+!$,;'@()*[]"
+            )
+            managed_print(
+                Fore.RED
+                + f"Please open this URL in your browser: '{encoded_url}'"
+                + Fore.RESET
+            )
+
+    def update_dataset(
+        self,
+        datapoints: list[str] | list[list[str]],
+        data_type: Literal["text", "media"] = "media",
+        contexts: list[str] | None = None,
+        media_contexts: list[str] | None = None,
+        sentences: list[str] | None = None,
+        private_notes: list[str] | None = None,
+    ) -> None:
+        """Update the dataset of the job definition.
+
+        Args:
+            datapoints (list[str] | list[list[str]]): paths to the datapoints or strings for text datapoints.
+            data_type (Literal["text", "media"]): The type of the datapoints.
+        """
+        with tracer.start_as_current_span("JobDefinition.update_dataset"):
+            from rapidata.rapidata_client.datapoints._datapoints_validator import (
+                DatapointsValidator,
+            )
+            from rapidata.api_client.models.create_dataset_endpoint_input import (
+                CreateDatasetEndpointInput,
+            )
+            from rapidata.rapidata_client.dataset._rapidata_dataset import (
+                RapidataDataset,
+            )
+            from rapidata.api_client.models.create_job_revision_endpoint_input import (
+                CreateJobRevisionEndpointInput,
+            )
+
+            datapoints_list = DatapointsValidator.map_datapoints(
+                datapoints=datapoints,
+                contexts=contexts,
+                media_contexts=media_contexts,
+                sentences=sentences,
+                private_notes=private_notes,
+                data_type=data_type,
+            )
+
+            dataset = self._openapi_service.dataset_api.dataset_post(
+                create_dataset_endpoint_input=CreateDatasetEndpointInput(
+                    name=self._name + "_dataset"
+                )
+            )
+
+            rapidata_dataset = RapidataDataset(
+                dataset.dataset_id, self._openapi_service
+            )
+
+            with tracer.start_as_current_span("update_datapoints"):
+                _, failed_uploads = rapidata_dataset.add_datapoints(datapoints_list)
+                if failed_uploads:
+                    raise FailedUploadException(rapidata_dataset, self, failed_uploads)
+
+            self._openapi_service.job_api.job_definition_definition_id_revision_post(
+                definition_id=self._id,
+                create_job_revision_endpoint_input=CreateJobRevisionEndpointInput(
+                    datasetId=rapidata_dataset.id,
+                ),
+            )
+
+    def __str__(self) -> str:
+        return f"JobDefinition(id={self._id}, name={self._name})"
+
+    def __repr__(self) -> str:
+        return self.__str__()
diff --git a/src/rapidata/rapidata_client/order/rapidata_order_manager.py b/src/rapidata/rapidata_client/job/job_manager.py
similarity index 66%
rename from src/rapidata/rapidata_client/order/rapidata_order_manager.py
rename to src/rapidata/rapidata_client/job/job_manager.py
index 58729691a..f84d16784 100644
--- a/src/rapidata/rapidata_client/order/rapidata_order_manager.py
+++ b/src/rapidata/rapidata_client/job/job_manager.py
@@ -1,72 +1,45 @@
-from __future__ import annotations
-
-from typing import Literal, Optional, Sequence, get_args, TYPE_CHECKING
-
+from rapidata.service.openapi_service import OpenAPIService
 from rapidata.rapidata_client.config import logger
 from rapidata.rapidata_client.config.tracer import tracer
-from rapidata.rapidata_client.datapoints._asset_uploader import AssetUploader
 from rapidata.rapidata_client.datapoints._datapoint import Datapoint
+from rapidata.rapidata_client.workflow import Workflow
+from rapidata.rapidata_client.settings import RapidataSetting
+from rapidata.rapidata_client.job.job_definition import JobDefinition
+from typing import Sequence, Literal
+from rapidata.rapidata_client.datapoints._asset_uploader import AssetUploader
+from rapidata.rapidata_client.dataset._rapidata_dataset import RapidataDataset
+from rapidata.rapidata_client.exceptions.failed_upload_exception import (
+    FailedUploadException,
+)
 from rapidata.rapidata_client.datapoints._datapoints_validator import (
     DatapointsValidator,
 )
-from rapidata.rapidata_client.filter import RapidataFilter
-from rapidata.rapidata_client.filter.rapidata_filters import RapidataFilters
-from rapidata.rapidata_client.settings import RapidataSetting, RapidataSettings
-from rapidata.rapidata_client.selection.rapidata_selections import RapidataSelections
-from rapidata.service.openapi_service import OpenAPIService
-
-if TYPE_CHECKING:
-    from rapidata.rapidata_client.order._rapidata_order_builder import (
-        StickyStateLiteral,
-    )
-    from rapidata.rapidata_client.order.rapidata_order import RapidataOrder
-    from rapidata.rapidata_client.selection._base_selection import RapidataSelection
-    from rapidata.rapidata_client.workflow import (
-        Workflow,
-    )
 
 
-class RapidataOrderManager:
+class JobManager:
+    """
+    A manager for job definitions.
+    Used to create and retrieve job definitions.
     """
-    Handels everything regarding the orders from creation to retrieval.
-
-    Attributes:
-        filters (RapidataFilters): The RapidataFilters instance.
-        settings (RapidataSettings): The RapidataSettings instance.
-        selections (RapidataSelections): The RapidataSelections instance."""
 
     def __init__(self, openapi_service: OpenAPIService):
-        self.__openapi_service = openapi_service
-        self.filters = RapidataFilters
-        self.settings = RapidataSettings
-        self.selections = RapidataSelections
-        from rapidata.rapidata_client.order._rapidata_order_builder import (
-            StickyStateLiteral,
-        )
+        self._openapi_service = openapi_service
 
         self.__priority: int | None = None
-        self.__sticky_state: StickyStateLiteral | None = None
         self._asset_uploader = AssetUploader(openapi_service)
-        logger.debug("RapidataOrderManager initialized")
+        logger.debug("JobManager initialized")
 
-    def _create_general_order(
+    def _create_general_job_definition(
         self,
         name: str,
         workflow: Workflow,
         datapoints: list[Datapoint],
         responses_per_datapoint: int = 10,
-        validation_set_id: str | None = None,
         confidence_threshold: float | None = None,
-        filters: Sequence[RapidataFilter] | None = None,
         settings: Sequence[RapidataSetting] | None = None,
-        selections: Sequence[RapidataSelection] | None = None,
-    ) -> RapidataOrder:
-        if filters is None:
-            filters = []
+    ) -> JobDefinition:
         if settings is None:
             settings = []
-        if selections is None:
-            selections = []
 
         if not confidence_threshold:
             from rapidata.rapidata_client.referee._naive_referee import NaiveReferee
@@ -79,74 +52,58 @@ def _create_general_order(
 
             referee = EarlyStoppingReferee(
                 threshold=confidence_threshold,
-                max_vote_count=responses_per_datapoint,
+                max_responses=responses_per_datapoint,
             )
 
         logger.debug(
-            "Creating order with parameters: name %s, workflow %s, datapoints %s, responses_per_datapoint %s, validation_set_id %s, confidence_threshold %s, filters %s, settings %s, selections %s",
+            "Creating job with parameters: name %s, workflow %s, datapoints %s, responses_per_datapoint %s, confidence_threshold %s, settings %s",
             name,
             workflow,
             datapoints,
             responses_per_datapoint,
-            validation_set_id,
             confidence_threshold,
-            filters,
             settings,
-            selections,
         )
-
-        from rapidata.rapidata_client.order._rapidata_order_builder import (
-            RapidataOrderBuilder,
+        from rapidata.api_client.models.create_dataset_endpoint_input import (
+            CreateDatasetEndpointInput,
         )
-
-        order_builder = RapidataOrderBuilder(
-            name=name, openapi_service=self.__openapi_service
+        from rapidata.api_client.models.create_job_definition_endpoint_input import (
+            CreateJobDefinitionEndpointInput,
         )
 
-        if selections and validation_set_id:
-            logger.warning(
-                "Warning: Both selections and validation_set_id provided. Ignoring validation_set_id."
+        dataset = self._openapi_service.dataset_api.dataset_post(
+            create_dataset_endpoint_input=CreateDatasetEndpointInput(
+                name=name + "_dataset"
             )
-
-        order = (
-            order_builder._set_workflow(workflow)
-            ._set_datapoints(datapoints=datapoints)
-            ._set_referee(referee)
-            ._set_filters(filters)
-            ._set_selections(selections)
-            ._set_settings(settings)
-            ._set_validation_set_id(validation_set_id if not selections else None)
-            ._set_priority(self.__priority)
-            ._set_sticky_state(self.__sticky_state)
-            ._create()
         )
-        logger.debug("Order created: %s", order)
-        return order
-
-    def _set_priority(self, priority: int | None):
-        if priority is not None and not isinstance(priority, int):
-            raise TypeError("Priority must be an integer or None")
-
-        if priority is not None and priority < 0:
-            raise ValueError("Priority must be greater than 0 or None")
-
-        self.__priority = priority
-
-    def _set_sticky_state(self, sticky_state: StickyStateLiteral | None):
-        from rapidata.rapidata_client.order._rapidata_order_builder import (
-            StickyStateLiteral,
+        rapidata_dataset = RapidataDataset(dataset.dataset_id, self._openapi_service)
+
+        with tracer.start_as_current_span("add_datapoints"):
+            _, failed_uploads = rapidata_dataset.add_datapoints(datapoints)
+
+        job_definition_response = self._openapi_service.job_api.job_definition_post(
+            create_job_definition_endpoint_input=CreateJobDefinitionEndpointInput(
+                definitionName=name,
+                workflow=workflow._to_model(),
+                datasetId=rapidata_dataset.id,
+                referee=referee._to_model(),
+                featureFlags=(
+                    [s._to_feature_flag() for s in settings] if settings else None
+                ),
+            )
+        )
+        job_model = JobDefinition(
+            id=job_definition_response.definition_id,
+            name=name,
+            openapi_service=self._openapi_service,
         )
 
-        sticky_state_valid_values = get_args(StickyStateLiteral)
-
-        if sticky_state is not None and sticky_state not in sticky_state_valid_values:
-            raise ValueError(
-                f"Sticky state must be one of {sticky_state_valid_values} or None"
-            )
+        if failed_uploads:
+            raise FailedUploadException(rapidata_dataset, job_model, failed_uploads)
 
-        self.__sticky_state = sticky_state
+        return job_model
 
-    def create_classification_order(
+    def create_classification_job_definition(
         self,
         name: str,
         instruction: str,
@@ -156,20 +113,17 @@ def create_classification_order(
         responses_per_datapoint: int = 10,
         contexts: list[str] | None = None,
         media_contexts: list[str] | None = None,
-        validation_set_id: str | None = None,
         confidence_threshold: float | None = None,
-        filters: Sequence[RapidataFilter] | None = None,
         settings: Sequence[RapidataSetting] | None = None,
-        selections: Sequence[RapidataSelection] | None = None,
         private_notes: list[str] | None = None,
-    ) -> RapidataOrder:
-        """Create a classification order.
+    ) -> JobDefinition:
+        """Create a classification job definition.
 
         With this order you can have a datapoint (image, text, video, audio) be classified into one of the answer options.
         Each response will be exactly one of the answer options.
 
         Args:
-            name (str): The name of the order. (Will not be shown to the labeler)
+            name (str): The name of the job. (Will not be shown to the labeler)
             instruction (str): The instruction for how the data should be classified.
             answer_options (list[str]): The list of options for the classification.
             datapoints (list[str]): The list of datapoints for the classification - each datapoint will be labeled.
@@ -181,20 +135,14 @@ def create_classification_order(
                 Will be match up with the datapoints using the list index.
             media_contexts (list[str], optional): The list of media contexts for the classification i.e links to the images / videos. Defaults to None.\n
                 If provided has to be the same length as datapoints and will be shown in addition to the instruction and options. (Therefore will be different for each datapoint)
-            validation_set_id (str, optional): The ID of the validation set. Defaults to None.\n
-                If provided, one validation task will be shown infront of the datapoints that will be labeled.
             confidence_threshold (float, optional): The probability threshold for the classification. Defaults to None.\n
                 If provided, the classification datapoint will stop after the threshold is reached or at the number of responses, whatever happens first.
-            filters (Sequence[RapidataFilter], optional): The list of filters for the classification. Defaults to []. Decides who the tasks should be shown to.
             settings (Sequence[RapidataSetting], optional): The list of settings for the classification. Defaults to []. Decides how the tasks should be shown.
-            selections (Sequence[RapidataSelection], optional): The list of selections for the classification. Defaults to []. Decides in what order the tasks should be shown.
             private_notes (list[str], optional): The list of private notes for the classification. Defaults to None.
                 If provided has to be the same length as datapoints.\n
                 This will NOT be shown to the labelers but will be included in the result purely for your own reference.
         """
-        with tracer.start_as_current_span(
-            "RapidataOrderManager.create_classification_order"
-        ):
+        with tracer.start_as_current_span("JobManager.create_classification_job"):
             if not isinstance(datapoints, list) or not all(
                 isinstance(datapoint, str) for datapoint in datapoints
             ):
@@ -209,21 +157,18 @@ def create_classification_order(
                 private_notes=private_notes,
                 data_type=data_type,
             )
-            return self._create_general_order(
+            return self._create_general_job_definition(
                 name=name,
                 workflow=ClassifyWorkflow(
                     instruction=instruction, answer_options=answer_options
                 ),
                 datapoints=datapoints_instances,
                 responses_per_datapoint=responses_per_datapoint,
-                validation_set_id=validation_set_id,
                 confidence_threshold=confidence_threshold,
-                filters=filters,
-                selections=selections,
                 settings=settings,
             )
 
-    def create_compare_order(
+    def create_compare_job_definition(
         self,
         name: str,
         instruction: str,
@@ -233,19 +178,16 @@ def create_compare_order(
         contexts: list[str] | None = None,
         media_contexts: list[str] | None = None,
         a_b_names: list[str] | None = None,
-        validation_set_id: str | None = None,
         confidence_threshold: float | None = None,
-        filters: Sequence[RapidataFilter] | None = None,
         settings: Sequence[RapidataSetting] | None = None,
-        selections: Sequence[RapidataSelection] | None = None,
         private_notes: list[str] | None = None,
-    ) -> RapidataOrder:
-        """Create a compare order.
+    ) -> JobDefinition:
+        """Create a compare job definition.
 
         With this order you compare two datapoints (image, text, video, audio) and the annotators will choose one of the two based on the instruction.
 
         Args:
-            name (str): The name of the order. (Will not be shown to the labeler)
+            name (str): The name of the job. (Will not be shown to the labeler)
             instruction (str): The instruction for the comparison. Will be shown along side each datapoint.
             datapoints (list[list[str]]): Outher list is the datapoints, inner list is the options for the comparison - each datapoint will be labeled.
             data_type (str, optional): The data type of the datapoints. Defaults to "media" (any form of image, video or audio). \n
@@ -266,18 +208,14 @@ def create_compare_order(
                 ```
                 The results will then correctly show "Model A" and "Model B".
                 If not provided, the results will be shown as "A" and "B".
-            validation_set_id (str, optional): The ID of the validation set. Defaults to None.\n
-                If provided, one validation task will be shown infront of the datapoints that will be labeled.
             confidence_threshold (float, optional): The probability threshold for the comparison. Defaults to None.\n
                 If provided, the comparison datapoint will stop after the threshold is reached or at the number of responses, whatever happens first.
-            filters (Sequence[RapidataFilter], optional): The list of filters for the comparison. Defaults to []. Decides who the tasks should be shown to.
             settings (Sequence[RapidataSetting], optional): The list of settings for the comparison. Defaults to []. Decides how the tasks should be shown.
-            selections (Sequence[RapidataSelection], optional): The list of selections for the comparison. Defaults to []. Decides in what order the tasks should be shown.
             private_notes (list[str], optional): The list of private notes for the comparison. Defaults to None.\n
                 If provided has to be the same length as datapoints.\n
                 This will NOT be shown to the labelers but will be included in the result purely for your own reference.
         """
-        with tracer.start_as_current_span("RapidataOrderManager.create_compare_order"):
+        with tracer.start_as_current_span("JobManager.create_compare_job"):
             if any(not isinstance(datapoint, list) for datapoint in datapoints):
                 raise ValueError("Each datapoint must be a list of 2 paths/texts")
 
@@ -300,19 +238,16 @@ def create_compare_order(
                 private_notes=private_notes,
                 data_type=data_type,
             )
-            return self._create_general_order(
+            return self._create_general_job_definition(
                 name=name,
                 workflow=CompareWorkflow(instruction=instruction, a_b_names=a_b_names),
                 datapoints=datapoints_instances,
                 responses_per_datapoint=responses_per_datapoint,
-                validation_set_id=validation_set_id,
                 confidence_threshold=confidence_threshold,
-                filters=filters,
-                selections=selections,
                 settings=settings,
             )
 
-    def create_ranking_order(
+    def _create_ranking_job_definition(
         self,
         name: str,
         instruction: str,
@@ -321,21 +256,18 @@ def create_ranking_order(
         responses_per_comparison: int = 1,
         data_type: Literal["media", "text"] = "media",
         random_comparisons_ratio: float = 0.5,
-        contexts: Optional[list[str]] = None,
-        media_contexts: Optional[list[str]] = None,
-        validation_set_id: Optional[str] = None,
-        filters: Sequence[RapidataFilter] | None = None,
+        contexts: list[str] | None = None,
+        media_contexts: list[str] | None = None,
         settings: Sequence[RapidataSetting] | None = None,
-        selections: Sequence[RapidataSelection] | None = None,
-    ) -> RapidataOrder:
+    ) -> JobDefinition:
         """
-        Create a ranking order.
+        Create a ranking job definition.
 
         With this order you can have a multiple lists of datapoints (image, text, video, audio) be ranked based on the instruction.
         Each list will be ranked independently, based on comparison matchups.
 
         Args:
-            name (str): The name of the order.
+            name (str): The name of the job.
             instruction (str): The instruction for the ranking. Will be shown with each matchup.
             datapoints (list[list[str]]): The outer list is determines the independent rankings, the inner list is the datapoints for each ranking.
             comparison_budget_per_ranking (int): The number of comparisons that will be collected per ranking (outer list of datapoints).
@@ -351,11 +283,9 @@ def create_ranking_order(
                 Will be matched up with the datapoints using the list index.
             validation_set_id (str, optional): The ID of the validation set. Defaults to None.\n
                 If provided, one validation task will be shown infront of the datapoints that will be labeled.
-            filters (Sequence[RapidataFilter], optional): The list of filters for the ranking. Defaults to []. Decides who the tasks should be shown to.
             settings (Sequence[RapidataSetting], optional): The list of settings for the ranking. Defaults to []. Decides how the tasks should be shown.
-            selections (Sequence[RapidataSelection], optional): The list of selections for the ranking. Defaults to []. Decides in what order the tasks should be shown.
         """
-        with tracer.start_as_current_span("RapidataOrderManager.create_ranking_order"):
+        with tracer.start_as_current_span("JobManager.create_ranking_job"):
             if contexts and len(contexts) != len(datapoints):
                 raise ValueError(
                     "Number of contexts must match the number of sets that will be ranked"
@@ -417,7 +347,7 @@ def create_ranking_order(
                 else None
             )
 
-            return self._create_general_order(
+            return self._create_general_job_definition(
                 name=name,
                 workflow=MultiRankingWorkflow(
                     instruction=instruction,
@@ -428,13 +358,10 @@ def create_ranking_order(
                 ),
                 datapoints=datapoints_instances,
                 responses_per_datapoint=responses_per_comparison,
-                validation_set_id=validation_set_id,
-                filters=filters,
-                selections=selections,
                 settings=settings,
             )
 
-    def create_free_text_order(
+    def _create_free_text_job_definition(
         self,
         name: str,
         instruction: str,
@@ -443,18 +370,16 @@ def create_free_text_order(
         responses_per_datapoint: int = 10,
         contexts: list[str] | None = None,
         media_contexts: list[str] | None = None,
-        filters: Sequence[RapidataFilter] | None = None,
         settings: Sequence[RapidataSetting] | None = None,
-        selections: Sequence[RapidataSelection] | None = None,
         private_notes: list[str] | None = None,
-    ) -> RapidataOrder:
-        """Create a free text order.
+    ) -> JobDefinition:
+        """Create a free text job definition.
 
         With this order you can have a datapoint (image, text, video, audio) be labeled with free text.
         The annotators will be shown a datapoint and will be asked to answer a question with free text.
 
         Args:
-            name (str): The name of the order.
+            name (str): The name of the job.
             instruction (str): The instruction to answer with free text. Will be shown along side each datapoint.
             datapoints (list[str]): The list of datapoints for the free text - each datapoint will be labeled.
             data_type (str, optional): The data type of the datapoints. Defaults to "media" (any form of image, video or audio). \n
@@ -466,16 +391,12 @@ def create_free_text_order(
             media_contexts (list[str], optional): The list of media contexts for the free text i.e links to the images / videos. Defaults to None.\n
                 If provided has to be the same length as datapoints and will be shown in addition to the instruction. (Therefore will be different for each datapoint)
                 Will be matched up with the datapoints using the list index.
-            filters (Sequence[RapidataFilter], optional): The list of filters for the free text. Defaults to []. Decides who the tasks should be shown to.
             settings (Sequence[RapidataSetting], optional): The list of settings for the free text. Defaults to []. Decides how the tasks should be shown.
-            selections (Sequence[RapidataSelection], optional): The list of selections for the free text. Defaults to []. Decides in what order the tasks should be shown.
             private_notes (list[str], optional): The list of private notes for the free text. Defaults to None.\n
                 If provided has to be the same length as datapoints.\n
                 This will NOT be shown to the labelers but will be included in the result purely for your own reference.
         """
-        with tracer.start_as_current_span(
-            "RapidataOrderManager.create_free_text_order"
-        ):
+        with tracer.start_as_current_span("JobManager.create_free_text_job"):
             from rapidata.rapidata_client.workflow import FreeTextWorkflow
 
             datapoints_instances = DatapointsValidator.map_datapoints(
@@ -485,54 +406,43 @@ def create_free_text_order(
                 private_notes=private_notes,
                 data_type=data_type,
             )
-            return self._create_general_order(
+            return self._create_general_job_definition(
                 name=name,
                 workflow=FreeTextWorkflow(instruction=instruction),
                 datapoints=datapoints_instances,
                 responses_per_datapoint=responses_per_datapoint,
-                filters=filters,
-                selections=selections,
                 settings=settings,
             )
 
-    def create_select_words_order(
+    def _create_select_words_job_definition(
         self,
         name: str,
         instruction: str,
         datapoints: list[str],
         sentences: list[str],
         responses_per_datapoint: int = 10,
-        validation_set_id: str | None = None,
-        filters: Sequence[RapidataFilter] | None = None,
         settings: Sequence[RapidataSetting] | None = None,
-        selections: Sequence[RapidataSelection] | None = None,
         private_notes: list[str] | None = None,
-    ) -> RapidataOrder:
-        """Create a select words order.
+    ) -> JobDefinition:
+        """Create a select words job definition.
 
         With this order you can have a datapoint (image, text, video, audio) be labeled with a list of words.
         The annotators will be shown a datapoint as well as a list of sentences split up by spaces.
         They will then select specific words based on the instruction.
 
         Args:
-            name (str): The name of the order.
+            name (str): The name of the job.
             instruction (str): The instruction for how the words should be selected. Will be shown along side each datapoint.
             datapoints (list[str]): The list of datapoints for the select words - each datapoint will be labeled.
             sentences (list[str]): The list of sentences for the select words - Will be split up by spaces and shown along side each datapoint.\n
                 Must be the same length as datapoints.
             responses_per_datapoint (int, optional): The number of responses that will be collected per datapoint. Defaults to 10.
-            validation_set_id (str, optional): The ID of the validation set. Defaults to None.\n
-                If provided, one validation task will be shown infront of the datapoints that will be labeled.
-            filters (Sequence[RapidataFilter], optional): The list of filters for the select words. Defaults to []. Decides who the tasks should be shown to.
             settings (Sequence[RapidataSetting], optional): The list of settings for the select words. Defaults to []. Decides how the tasks should be shown.
-            selections (Sequence[RapidataSelection], optional): The list of selections for the select words. Defaults to []. Decides in what order the tasks should be shown.
             private_notes (list[str], optional): The list of private notes for the select words. Defaults to None.\n
                 If provided has to be the same length as datapoints.\n
                 This will NOT be shown to the labelers but will be included in the result purely for your own reference.
         """
-        with tracer.start_as_current_span(
-            "RapidataOrderManager.create_select_words_order"
-        ):
+        with tracer.start_as_current_span("JobManager.create_select_words_job"):
             from rapidata.rapidata_client.workflow import SelectWordsWorkflow
 
             datapoints_instances = DatapointsValidator.map_datapoints(
@@ -540,20 +450,17 @@ def create_select_words_order(
                 sentences=sentences,
                 private_notes=private_notes,
             )
-            return self._create_general_order(
+            return self._create_general_job_definition(
                 name=name,
                 workflow=SelectWordsWorkflow(
                     instruction=instruction,
                 ),
                 datapoints=datapoints_instances,
                 responses_per_datapoint=responses_per_datapoint,
-                validation_set_id=validation_set_id,
-                filters=filters,
-                selections=selections,
                 settings=settings,
             )
 
-    def create_locate_order(
+    def _create_locate_job_definition(
         self,
         name: str,
         instruction: str,
@@ -561,19 +468,16 @@ def create_locate_order(
         responses_per_datapoint: int = 10,
         contexts: list[str] | None = None,
         media_contexts: list[str] | None = None,
-        validation_set_id: str | None = None,
-        filters: Sequence[RapidataFilter] | None = None,
         settings: Sequence[RapidataSetting] | None = None,
-        selections: Sequence[RapidataSelection] | None = None,
         private_notes: list[str] | None = None,
-    ) -> RapidataOrder:
-        """Create a locate order.
+    ) -> JobDefinition:
+        """Create a locate job definition.
 
         With this order you can have people locate specific objects in a datapoint (image, text, video, audio).
         The annotators will be shown a datapoint and will be asked to select locations based on the instruction.
 
         Args:
-            name (str): The name of the order.
+            name (str): The name of the job. (Will not be shown to the labeler)
             instruction (str): The instruction what should be located. Will be shown along side each datapoint.
             datapoints (list[str]): The list of datapoints for the locate - each datapoint will be labeled.
             responses_per_datapoint (int, optional): The number of responses that will be collected per datapoint. Defaults to 10.
@@ -582,16 +486,12 @@ def create_locate_order(
                 Will be match up with the datapoints using the list index.
             media_contexts (list[str], optional): The list of media contexts for the locate i.e links to the images / videos. Defaults to None.\n
                 If provided has to be the same length as datapoints and will be shown in addition to the instruction. (Therefore will be different for each datapoint)
-            validation_set_id (str, optional): The ID of the validation set. Defaults to None.\n
-                If provided, one validation task will be shown infront of the datapoints that will be labeled.
-            filters (Sequence[RapidataFilter], optional): The list of filters for the locate. Defaults to []. Decides who the tasks should be shown to.
             settings (Sequence[RapidataSetting], optional): The list of settings for the locate. Defaults to []. Decides how the tasks should be shown.
-            selections (Sequence[RapidataSelection], optional): The list of selections for the locate. Defaults to []. Decides in what order the tasks should be shown.
             private_notes (list[str], optional): The list of private notes for the locate. Defaults to None.\n
                 If provided has to be the same length as datapoints.\n
                 This will NOT be shown to the labelers but will be included in the result purely for your own reference.
         """
-        with tracer.start_as_current_span("RapidataOrderManager.create_locate_order"):
+        with tracer.start_as_current_span("JobManager.create_locate_job"):
             from rapidata.rapidata_client.workflow import LocateWorkflow
 
             datapoints_instances = DatapointsValidator.map_datapoints(
@@ -600,18 +500,15 @@ def create_locate_order(
                 media_contexts=media_contexts,
                 private_notes=private_notes,
             )
-            return self._create_general_order(
+            return self._create_general_job_definition(
                 name=name,
                 workflow=LocateWorkflow(target=instruction),
                 datapoints=datapoints_instances,
                 responses_per_datapoint=responses_per_datapoint,
-                validation_set_id=validation_set_id,
-                filters=filters,
-                selections=selections,
                 settings=settings,
             )
 
-    def create_draw_order(
+    def _create_draw_job_definition(
         self,
         name: str,
         instruction: str,
@@ -619,19 +516,16 @@ def create_draw_order(
         responses_per_datapoint: int = 10,
         contexts: list[str] | None = None,
         media_contexts: list[str] | None = None,
-        validation_set_id: str | None = None,
-        filters: Sequence[RapidataFilter] = [],
-        settings: Sequence[RapidataSetting] = [],
-        selections: Sequence[RapidataSelection] = [],
+        settings: Sequence[RapidataSetting] | None = None,
         private_notes: list[str] | None = None,
-    ) -> RapidataOrder:
-        """Create a draw order.
+    ) -> JobDefinition:
+        """Create a draw job definition.
 
         With this order you can have people draw lines on a datapoint (image, text, video, audio).
         The annotators will be shown a datapoint and will be asked to draw lines based on the instruction.
 
         Args:
-            name (str): The name of the order.
+            name (str): The name of the job. (Will not be shown to the labeler)
             instruction (str): The instruction for how the lines should be drawn. Will be shown along side each datapoint.
             datapoints (list[str]): The list of datapoints for the draw lines - each datapoint will be labeled.
             responses_per_datapoint (int, optional): The number of responses that will be collected per datapoint. Defaults to 10.
@@ -640,16 +534,12 @@ def create_draw_order(
                 Will be match up with the datapoints using the list index.
             media_contexts (list[str], optional): The list of media contexts for the draw lines i.e links to the images / videos. Defaults to None.\n
                 If provided has to be the same length as datapoints and will be shown in addition to the instruction. (Therefore will be different for each datapoint)
-            validation_set_id (str, optional): The ID of the validation set. Defaults to None.\n
-                If provided, one validation task will be shown infront of the datapoints that will be labeled.
-            filters (Sequence[RapidataFilter], optional): The list of filters for the draw lines. Defaults to []. Decides who the tasks should be shown to.
             settings (Sequence[RapidataSetting], optional): The list of settings for the draw lines. Defaults to []. Decides how the tasks should be shown.
-            selections (Sequence[RapidataSelection], optional): The list of selections for the draw lines. Defaults to []. Decides in what order the tasks should be shown.
             private_notes (list[str], optional): The list of private notes for the draw lines. Defaults to None.\n
                 If provided has to be the same length as datapoints.\n
                 This will NOT be shown to the labelers but will be included in the result purely for your own reference.
         """
-        with tracer.start_as_current_span("RapidataOrderManager.create_draw_order"):
+        with tracer.start_as_current_span("JobManager.create_draw_job"):
             from rapidata.rapidata_client.workflow import DrawWorkflow
 
             datapoints_instances = DatapointsValidator.map_datapoints(
@@ -658,18 +548,15 @@ def create_draw_order(
                 media_contexts=media_contexts,
                 private_notes=private_notes,
             )
-            return self._create_general_order(
+            return self._create_general_job_definition(
                 name=name,
                 workflow=DrawWorkflow(target=instruction),
                 datapoints=datapoints_instances,
                 responses_per_datapoint=responses_per_datapoint,
-                validation_set_id=validation_set_id,
-                filters=filters,
-                selections=selections,
                 settings=settings,
             )
 
-    def create_timestamp_order(
+    def _create_timestamp_job_definition(
         self,
         name: str,
         instruction: str,
@@ -677,22 +564,19 @@ def create_timestamp_order(
         responses_per_datapoint: int = 10,
         contexts: list[str] | None = None,
         media_contexts: list[str] | None = None,
-        validation_set_id: str | None = None,
-        filters: Sequence[RapidataFilter] | None = None,
         settings: Sequence[RapidataSetting] | None = None,
-        selections: Sequence[RapidataSelection] | None = None,
         private_notes: list[str] | None = None,
-    ) -> RapidataOrder:
-        """Create a timestamp order.
+    ) -> JobDefinition:
+        """Create a timestamp job definition.
 
         Warning:
-            This order is currently not fully supported and may give unexpected results.
+            This job is currently not fully supported and may give unexpected results.
 
         With this order you can have people mark specific timestamps in a datapoint (video, audio).
         The annotators will be shown a datapoint and will be asked to select a timestamp based on the instruction.
 
         Args:
-            name (str): The name of the order.
+            name (str): The name of the job. (Will not be shown to the labeler)
             instruction (str): The instruction for the timestamp task. Will be shown along side each datapoint.
             datapoints (list[str]): The list of datapoints for the timestamp - each datapoint will be labeled.
             responses_per_datapoint (int, optional): The number of responses that will be collected per datapoint. Defaults to 10.
@@ -701,19 +585,12 @@ def create_timestamp_order(
                 Will be match up with the datapoints using the list index.
             media_contexts (list[str], optional): The list of media contexts for the timestamp i.e links to the images / videos. Defaults to None.\n
                 If provided has to be the same length as datapoints and will be shown in addition to the instruction. (Therefore will be different for each datapoint)
-            validation_set_id (str, optional): The ID of the validation set. Defaults to None.\n
-                If provided, one validation task will be shown infront of the datapoints that will be labeled.
-            filters (Sequence[RapidataFilter], optional): The list of filters for the timestamp. Defaults to []. Decides who the tasks should be shown to.
             settings (Sequence[RapidataSetting], optional): The list of settings for the timestamp. Defaults to []. Decides how the tasks should be shown.
-            selections (Sequence[RapidataSelection], optional): The list of selections for the timestamp. Defaults to []. Decides in what order the tasks should be shown.
             private_notes (list[str], optional): The list of private notes for the timestamp. Defaults to None.\n
                 If provided has to be the same length as datapoints.\n
                 This will NOT be shown to the labelers but will be included in the result purely for your own reference.
         """
-
-        with tracer.start_as_current_span(
-            "RapidataOrderManager.create_timestamp_order"
-        ):
+        with tracer.start_as_current_span("JobManager.create_timestamp_job"):
             from rapidata.rapidata_client.workflow import TimestampWorkflow
 
             datapoints_instances = DatapointsValidator.map_datapoints(
@@ -722,48 +599,55 @@ def create_timestamp_order(
                 media_contexts=media_contexts,
                 private_notes=private_notes,
             )
-            return self._create_general_order(
+            return self._create_general_job_definition(
                 name=name,
                 workflow=TimestampWorkflow(instruction=instruction),
                 datapoints=datapoints_instances,
                 responses_per_datapoint=responses_per_datapoint,
-                validation_set_id=validation_set_id,
-                filters=filters,
-                selections=selections,
                 settings=settings,
             )
 
-    def get_order_by_id(self, order_id: str) -> RapidataOrder:
-        """Get an order by ID.
+    def get_job_defintion_by_id(self, job_definition_id: str) -> JobDefinition:
+        """Get a job by ID.
 
         Args:
-            order_id (str): The ID of the order.
+            job_definition_id (str): The ID of the job definition.
 
         Returns:
-            RapidataOrder: The Order instance.
+            JobDefinition: The JobDefinition instance.
         """
-        with tracer.start_as_current_span("RapidataOrderManager.get_order_by_id"):
-            from rapidata.rapidata_client.order.rapidata_order import RapidataOrder
+        with tracer.start_as_current_span("JobManager.get_job_by_id"):
 
-            order = self.__openapi_service.order_api.order_order_id_get(order_id)
+            job_definition = (
+                self._openapi_service.job_api.job_definition_definition_id_get(
+                    definition_id=job_definition_id,
+                )
+            )
 
-            return RapidataOrder(
-                order_id=order_id,
-                name=order.order_name,
-                openapi_service=self.__openapi_service,
+            latest_revision = self._openapi_service.job_api.job_definition_definition_id_revision_revision_number_get(
+                definition_id=job_definition_id,
+                revision_number=1,
             )
 
-    def find_orders(self, name: str = "", amount: int = 10) -> list[RapidataOrder]:
-        """Find your recent orders given criteria. If nothing is provided, it will return the most recent order.
+            return JobDefinition(
+                id=job_definition.definition_id,
+                name=job_definition.name,
+                openapi_service=self._openapi_service,
+            )
+
+    def find_job_definitions(
+        self, name: str = "", amount: int = 10
+    ) -> list[JobDefinition]:
+        """Find your recent jobs given criteria. If nothing is provided, it will return the most recent job.
 
         Args:
-            name (str, optional): The name of the order - matching order will contain the name. Defaults to "" for any order.
-            amount (int, optional): The amount of orders to return. Defaults to 10.
+            name (str, optional): The name of the job - matching job will contain the name. Defaults to "" for any job.
+            amount (int, optional): The amount of jobs to return. Defaults to 10.
 
         Returns:
-            list[RapidataOrder]: A list of RapidataOrder instances.
+            list[JobDefinition]: A list of JobDefinition instances.
         """
-        with tracer.start_as_current_span("RapidataOrderManager.find_orders"):
+        with tracer.start_as_current_span("JobManager.find_jobs"):
             from rapidata.api_client.models.page_info import PageInfo
             from rapidata.api_client.models.query_model import QueryModel
             from rapidata.api_client.models.root_filter import RootFilter
@@ -772,33 +656,54 @@ def find_orders(self, name: str = "", amount: int = 10) -> list[RapidataOrder]:
             from rapidata.api_client.models.sort_criterion import SortCriterion
             from rapidata.api_client.models.sort_direction import SortDirection
 
-            order_page_result = self.__openapi_service.order_api.orders_get(
-                QueryModel(
-                    page=PageInfo(index=1, size=amount),
-                    filter=RootFilter(
-                        filters=[
-                            Filter(
-                                field="OrderName",
-                                operator=FilterOperator.CONTAINS,
-                                value=name,
+            job_definition_page_result = (
+                self._openapi_service.job_api.job_definitions_get(
+                    request=QueryModel(
+                        page=PageInfo(index=1, size=amount),
+                        filter=RootFilter(
+                            filters=[
+                                Filter(
+                                    field="DefinitionName",
+                                    operator=FilterOperator.CONTAINS,
+                                    value=name,
+                                )
+                            ]
+                        ),
+                        sortCriteria=[
+                            SortCriterion(
+                                direction=SortDirection.DESC, propertyName="CreatedAt"
                             )
-                        ]
+                        ],
                     ),
-                    sortCriteria=[
-                        SortCriterion(
-                            direction=SortDirection.DESC, propertyName="OrderDate"
-                        )
-                    ],
                 )
             )
 
-            orders = [
-                self.get_order_by_id(order.id) for order in order_page_result.items
+            jobs = [
+                JobDefinition(
+                    id=job_def.definition_id,
+                    name=job_def.name,
+                    openapi_service=self._openapi_service,
+                )
+                for job_def in job_definition_page_result.items
             ]
-            return orders
+            return jobs
+
+    def _get_definition_object(self, definition_id: str) -> JobDefinition:
+        # get the latest revision number
+        max_number = 1  # TO BE CHANGED
+        definition_name = "something"
+        latest_revision = self._openapi_service.job_api.job_definition_definition_id_revision_revision_number_get(
+            definition_id=definition_id,
+            revision_number=max_number,
+        )
+        return JobDefinition(
+            id=latest_revision.definition_id,
+            name=definition_name,
+            openapi_service=self._openapi_service,
+        )
 
     def __str__(self) -> str:
-        return "RapidataOrderManager"
+        return "JobManager"
 
     def __repr__(self) -> str:
         return self.__str__()
diff --git a/src/rapidata/rapidata_client/order/__init__.py b/src/rapidata/rapidata_client/order/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/src/rapidata/rapidata_client/order/_rapidata_order_builder.py b/src/rapidata/rapidata_client/order/_rapidata_order_builder.py
deleted file mode 100644
index 3d8434b20..000000000
--- a/src/rapidata/rapidata_client/order/_rapidata_order_builder.py
+++ /dev/null
@@ -1,481 +0,0 @@
-from __future__ import annotations
-
-from typing import TYPE_CHECKING, Literal, Optional, Sequence, get_args
-import random
-import secrets
-
-from rapidata.rapidata_client.datapoints._datapoint import Datapoint
-
-if TYPE_CHECKING:
-    from rapidata.api_client.models.create_order_model import CreateOrderModel
-from rapidata.rapidata_client.exceptions.failed_upload_exception import (
-    FailedUploadException,
-)
-from rapidata.rapidata_client.filter import RapidataFilter, UserScoreFilter
-from rapidata.rapidata_client.config import (
-    logger,
-    managed_print,
-    rapidata_config,
-    tracer,
-)
-from rapidata.rapidata_client.validation.validation_set_manager import (
-    ValidationSetManager,
-)
-from rapidata.rapidata_client.validation.rapidata_validation_set import (
-    RapidataValidationSet,
-)
-from rapidata.rapidata_client.order.dataset._rapidata_dataset import RapidataDataset
-from rapidata.rapidata_client.order.rapidata_order import RapidataOrder
-from rapidata.rapidata_client.referee import Referee
-from rapidata.rapidata_client.referee._naive_referee import NaiveReferee
-from rapidata.rapidata_client.selection._base_selection import RapidataSelection
-from rapidata.rapidata_client.settings import RapidataSetting
-from rapidata.rapidata_client.workflow import (
-    Workflow,
-    CompareWorkflow,
-    ClassifyWorkflow,
-)
-from rapidata.rapidata_client.selection import (
-    ConditionalValidationSelection,
-    LabelingSelection,
-    CappedSelection,
-)
-from rapidata.service.openapi_service import OpenAPIService
-from rapidata.rapidata_client.api.rapidata_api_client import (
-    suppress_rapidata_error_logging,
-)
-
-StickyStateLiteral = Literal["Temporary", "Permanent", "Passive"]
-
-
-class RapidataOrderBuilder:
-    """Builder object for creating Rapidata orders.
-
-    Use the fluent interface to set the desired configuration. Add a workflow to the order using `.workflow()` and finally call `.create()` to create the order.
-
-    Args:
-        openapi_service (OpenAPIService): The OpenAPIService instance.
-        name (str): The name of the order.
-    """
-
-    def __init__(
-        self,
-        name: str,
-        openapi_service: OpenAPIService,
-    ):
-        self._name = name
-        self._openapi_service = openapi_service
-        self._dataset: Optional[RapidataDataset] = None
-        self._workflow: Workflow | None = None
-        self._referee: Referee | None = None
-        self._validation_set: RapidataValidationSet | None = None
-        self._settings: Sequence[RapidataSetting] | None = None
-        self._user_filters: list[RapidataFilter] = []
-        self._selections: Sequence[RapidataSelection] = []
-        self._priority: int | None = None
-        self._datapoints: list[Datapoint] = []
-        self._sticky_state_value: StickyStateLiteral | None = None
-        self._temporary_sticky_enabled: bool = False
-        self._validation_set_manager: ValidationSetManager = ValidationSetManager(
-            self._openapi_service
-        )
-
-    def _to_model(self) -> CreateOrderModel:
-        """
-        Convert the builder configuration to a CreateOrderModel.
-
-        Raises:
-            ValueError: If no workflow is provided.
-
-        Returns:
-            CreateOrderModel: The model representing the order configuration.
-        """
-        if self._workflow is None:
-            raise ValueError("You must provide a workflow to create an order.")
-
-        if self._referee is None:
-            managed_print("No referee provided, using default NaiveReferee.")
-            self._referee = NaiveReferee()
-
-        sticky_state = self._sticky_state_value
-        if not sticky_state and self._temporary_sticky_enabled:
-            sticky_state = "Temporary"
-            logger.debug(
-                "Setting sticky state to Temporary due to temporary sticky enabled."
-            )
-
-        validation_set_id = (
-            self._validation_set.id
-            if (self._validation_set and not self._selections)
-            else None
-        )
-        from rapidata.api_client.models.create_order_model import CreateOrderModel
-        from rapidata.api_client.models.sticky_state import StickyState
-
-        return CreateOrderModel(
-            orderName=self._name,
-            workflow=self._workflow._to_model(),
-            userFilters=[user_filter._to_model() for user_filter in self._user_filters],
-            referee=self._referee._to_model(),
-            validationSetId=validation_set_id,
-            featureFlags=(
-                [setting._to_feature_flag() for setting in self._settings]
-                if self._settings is not None
-                else None
-            ),
-            selections=(
-                [selection._to_model() for selection in self._selections]
-                if self._selections
-                else None
-            ),
-            priority=self._priority,
-            stickyState=(StickyState(sticky_state) if sticky_state else None),
-        )
-
-    def _generate_id(self, length=9):
-        """Optimal balance: fast comparisons + low collision risk"""
-        alphabet = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
-        return "".join(secrets.choice(alphabet) for _ in range(length))
-
-    def _attach_validation_set_id(self) -> None:
-        """
-        Sets the validation set for the order.
-        """
-        assert self._workflow is not None
-
-        required_amount = min(int(len(self._datapoints) * 0.01) or 3, 15)
-
-        if self._validation_set is None:
-            try:
-                with suppress_rapidata_error_logging():
-                    val_set_id = (
-                        (
-                            self._openapi_service.validation_api.validation_set_recommended_get(
-                                asset_type=[self._datapoints[0].get_asset_type()],
-                                modality=[self._workflow.modality],
-                                instruction=self._workflow._get_instruction(),
-                                prompt_type=[
-                                    t.value
-                                    for t in self._datapoints[0].get_prompt_type()
-                                ],
-                            )
-                        )
-                        .validation_sets[0]
-                        .id
-                    )
-                    self._validation_set = (
-                        self._validation_set_manager.get_validation_set_by_id(
-                            val_set_id
-                        )
-                    )
-
-            except Exception as e:
-                logger.debug("No recommended validation set found, error: %s", e)
-
-        sufficient_rapids_count = False
-        if self._validation_set is not None:
-            sufficient_rapids_count = (
-                self._validation_set_manager._get_total_and_labeled_rapids_count(
-                    self._validation_set.id
-                )[1]
-                >= required_amount
-            )
-
-        if self._validation_set is None or not sufficient_rapids_count:
-            if (
-                len(self._datapoints)
-                < rapidata_config.order.minOrderDatapointsForValidation
-            ):
-                logger.debug(
-                    "No recommended validation set found, dataset too small to create one."
-                )
-                return
-
-            logger.info("No recommended validation set found, creating new one.")
-            managed_print()
-            managed_print(
-                f"No recommended validation set found, new one will be created."
-            )
-
-            new_dimension = self._generate_id()
-            logger.debug("New dimension created: %s", new_dimension)
-            rng = random.Random(42)
-            self._validation_set = (
-                self._validation_set_manager._create_order_validation_set(
-                    workflow=self._workflow,
-                    name=self._name,
-                    datapoints=rng.sample(
-                        self._datapoints, len(self._datapoints)
-                    ),  # shuffle the datapoints with a specific seed
-                    required_amount=required_amount,
-                    settings=self._settings,
-                    dimensions=[new_dimension],
-                )
-            )
-
-        self._validation_set.update_should_alert(False)
-        self._validation_set.update_can_be_flagged(False)
-
-        logger.debug("New validation set created for order: %s", self._validation_set)
-
-        self._selections = [
-            CappedSelection(
-                selections=[
-                    ConditionalValidationSelection(
-                        validation_set_id=self._validation_set.id,
-                        dimensions=self._validation_set.dimensions,
-                        thresholds=[0, 0.5, 0.7],
-                        chances=[1, 1, 0.2],
-                        rapid_counts=[10, 1, 1],
-                    ),
-                    LabelingSelection(amount=1),
-                ],
-                max_rapids=3,
-            )
-        ]
-
-        for dimension in self._validation_set.dimensions:
-            self._user_filters.append(
-                UserScoreFilter(
-                    lower_bound=0.3,
-                    upper_bound=1,
-                    dimension=dimension,
-                )
-            )
-
-    def _create(self) -> RapidataOrder:
-        """
-        Create the Rapidata order by making the necessary API calls based on the builder's configuration.
-
-        Raises:
-            ValueError: If both media paths and texts are provided, or if neither is provided.
-            AssertionError: If the workflow is a CompareWorkflow and media paths are not in pairs.
-
-        Returns:
-            RapidataOrder: The created RapidataOrder instance.
-        """
-        if (
-            rapidata_config.order.autoValidationSetCreation
-            and isinstance(self._workflow, (CompareWorkflow, ClassifyWorkflow))
-            and not self._selections
-            and rapidata_config.enableBetaFeatures
-        ):
-            self._attach_validation_set_id()
-            self._temporary_sticky_enabled = True
-            logger.debug("Temporary sticky enabled for order creation.")
-
-        order_model = self._to_model()
-        logger.debug("Creating order with model: %s", order_model)
-
-        self._temporary_sticky_enabled = False
-        logger.debug("Disabling temporary sticky after order creation.")
-
-        result = self._openapi_service.order_api.order_post(
-            create_order_model=order_model
-        )
-
-        order = RapidataOrder(
-            order_id=str(result.order_id),
-            openapi_service=self._openapi_service,
-            name=self._name,
-        )
-        logger.debug("Order created: %s", order)
-
-        if not result.dataset_id:
-            logger.error("No Dataset was created for order %s", order.id)
-            return order
-
-        self._dataset = RapidataDataset(result.dataset_id, self._openapi_service)
-
-        logger.debug("Dataset created with ID: %s", self._dataset.id)
-        logger.debug("Adding datapoints to the order.")
-        with tracer.start_as_current_span("add_datapoints"):
-            _, failed_uploads = self._dataset.add_datapoints(self._datapoints)
-
-            if failed_uploads:
-                raise FailedUploadException(self._dataset, order, failed_uploads)
-
-        logger.debug("Datapoints added to the order.")
-        logger.debug("Setting order to preview")
-        try:
-            self._openapi_service.order_api.order_order_id_preview_post(order.id)
-        except Exception:
-            raise FailedUploadException(self._dataset, order, failed_uploads)
-        return order
-
-    def _set_workflow(self, workflow: Workflow) -> RapidataOrderBuilder:
-        """
-        Set the workflow for the order.
-
-        Args:
-            workflow (Workflow): The workflow to be set.
-
-        Returns:
-            RapidataOrderBuilder: The updated RapidataOrderBuilder instance.
-        """
-        if not isinstance(workflow, Workflow):
-            raise TypeError("Workflow must be of type Workflow.")
-
-        self._workflow = workflow
-        return self
-
-    def _set_referee(self, referee: Referee) -> "RapidataOrderBuilder":
-        """
-        Set the referee for the order.
-
-        Args:
-            referee (Referee): The referee to be set.
-
-        Returns:
-            RapidataOrderBuilder: The updated RapidataOrderBuilder instance.
-        """
-        if not isinstance(referee, Referee):
-            raise TypeError("Referee must be of type Referee.")
-
-        self._referee = referee
-        return self
-
-    def _set_datapoints(
-        self,
-        datapoints: list[Datapoint],
-    ) -> RapidataOrderBuilder:
-        """
-        Set the datapoints for the order.
-
-        Args:
-            datapoints: (Sequence[Datapoint]): The datapoints to be set.
-
-        Returns:
-            RapidataOrderBuilder: The updated RapidataOrderBuilder instance.
-        """
-        if not isinstance(datapoints, list):
-            raise TypeError(
-                "Datapoints must be provided as a list of Datapoint objects."
-            )
-
-        self._datapoints = datapoints
-        return self
-
-    def _set_settings(
-        self, settings: Sequence[RapidataSetting]
-    ) -> RapidataOrderBuilder:
-        """
-        Set the settings for the order.
-
-        Args:
-            settings (Settings): The settings to be set.
-
-        Returns:
-            RapidataOrderBuilder: The updated RapidataOrderBuilder instance.
-        """
-
-        if not isinstance(settings, list):
-            raise TypeError("Settings must be provided as a list of Setting objects.")
-
-        for s in settings:
-            if not isinstance(s, RapidataSetting):
-                raise TypeError("The settings list must only contain Setting objects.")
-
-        self._settings = settings
-        return self
-
-    def _set_filters(self, filters: Sequence[RapidataFilter]) -> RapidataOrderBuilder:
-        """
-        Set the filters for the order, e.g., country, language, userscore, etc.
-
-        Args:
-            filters (Sequence[Filters]): The user filters to be set.
-
-        Returns:
-            RapidataOrderBuilder: The updated RapidataOrderBuilder instance.
-        """
-        if not isinstance(filters, list):
-            raise TypeError("Filters must be provided as a list of Filter objects.")
-
-        for f in filters:
-            if not isinstance(f, RapidataFilter):
-                raise TypeError("Filters must be of type Filter.")
-
-        if len(self._user_filters) > 0:
-            managed_print("Overwriting existing user filters.")
-
-        self._user_filters = filters
-        return self
-
-    def _set_validation_set_id(
-        self, validation_set_id: str | None = None
-    ) -> RapidataOrderBuilder:
-        """
-        Set the validation set ID for the order.
-
-        Args:
-            validation_set_id (str): The validation set ID to be set.
-
-        Returns:
-            RapidataOrderBuilder: The updated RapidataOrderBuilder instance.
-        """
-        if validation_set_id is not None and not isinstance(validation_set_id, str):
-            raise TypeError("Validation set ID must be of type str.")
-        if validation_set_id is not None:
-            self._validation_set = (
-                self._validation_set_manager.get_validation_set_by_id(validation_set_id)
-            )
-            return self
-        self._validation_set = validation_set_id
-        return self
-
-    def _set_selections(
-        self, selections: Sequence[RapidataSelection]
-    ) -> RapidataOrderBuilder:
-        """
-        Set the selections for the order.
-
-        Args:
-            selections (Sequence[Selection]): The selections to be set.
-
-        Returns:
-            RapidataOrderBuilder: The updated RapidataOrderBuilder instance.
-        """
-        if not isinstance(selections, list):
-            raise TypeError(
-                "Selections must be provided as a list of Selection objects."
-            )
-
-        for selection in selections:
-            if not isinstance(selection, RapidataSelection):
-                raise TypeError("Selections must be of type Selection.")
-
-        self._selections = selections  # type: ignore
-        return self
-
-    def _set_priority(self, priority: int | None = None) -> RapidataOrderBuilder:
-        """
-        Set the priority for the order.
-
-        Args:
-            priority (int): The priority to be set.
-
-        Returns:
-            RapidataOrderBuilder: The updated RapidataOrderBuilder instance.
-        """
-        if priority is not None and not isinstance(priority, int):
-            raise TypeError("Priority must be of type int.")
-
-        self._priority = priority
-        return self
-
-    def _set_sticky_state(
-        self, sticky_state: StickyStateLiteral | None = None
-    ) -> RapidataOrderBuilder:
-        """
-        Set the sticky state for the order.
-        """
-        sticky_state_valid_values = get_args(StickyStateLiteral)
-
-        if sticky_state is not None and sticky_state not in sticky_state_valid_values:
-            raise ValueError(
-                f"Sticky state must be one of {sticky_state_valid_values} or None"
-            )
-
-        self._sticky_state_value = sticky_state
-        return self
diff --git a/src/rapidata/rapidata_client/order/rapidata_order.py b/src/rapidata/rapidata_client/order/rapidata_order.py
deleted file mode 100644
index 7ea4984bb..000000000
--- a/src/rapidata/rapidata_client/order/rapidata_order.py
+++ /dev/null
@@ -1,440 +0,0 @@
-from __future__ import annotations
-
-
-# Standard library imports
-import json
-import urllib.parse
-import webbrowser
-from time import sleep
-from typing import cast, Callable, TypeVar, TYPE_CHECKING
-from colorama import Fore
-from datetime import datetime
-from tqdm import tqdm
-
-# Local/application imports
-from rapidata.service.openapi_service import OpenAPIService
-from rapidata.rapidata_client.config import (
-    logger,
-    managed_print,
-    rapidata_config,
-    tracer,
-)
-from rapidata.rapidata_client.api.rapidata_api_client import (
-    suppress_rapidata_error_logging,
-)
-
-if TYPE_CHECKING:
-    from rapidata.api_client.models.campaign_artifact_model import CampaignArtifactModel
-    from rapidata.api_client.models.file_stream_result import FileStreamResult
-    from rapidata.api_client.models.order_state import OrderState
-    from rapidata.api_client.models.workflow_artifact_model import WorkflowArtifactModel
-    from rapidata.api_client.models.get_workflow_progress_result import (
-        GetWorkflowProgressResult,
-    )
-    from rapidata.rapidata_client.order.rapidata_results import RapidataResults
-
-T = TypeVar("T")
-
-
-class RapidataOrder:
-    """
-    An instance of a Rapidata order.
-
-    Used to interact with a specific order in the Rapidata system, such as starting,
-    pausing, and retrieving results.
-
-    Args:
-        name: The name of the order.
-        order_id: The ID of the order.
-        openapi_service: The OpenAPIService instance for API interaction.
-    """
-
-    def __init__(
-        self,
-        name: str,
-        order_id: str,
-        openapi_service: OpenAPIService,
-    ):
-        self.id = order_id
-        self.name = name
-        self.__created_at: datetime | None = None
-        self._openapi_service = openapi_service
-        self.__workflow_id: str = ""
-        self.__campaign_id: str = ""
-        self.__pipeline_id: str = ""
-        self.order_details_page = (
-            f"https://app.{self._openapi_service.environment}/order/detail/{self.id}"
-        )
-        logger.debug("RapidataOrder initialized")
-
-    def _get_order_failure_message(self) -> str | None:
-        """Retrieves the failure message from the order if available."""
-        try:
-            order = self._openapi_service.order_api.order_order_id_get(self.id)
-            return order.failure_message
-        except Exception:
-            logger.debug("Failed to get order failure message", self, exc_info=True)
-            return None
-
-    def _retry_operation(
-        self,
-        operation: Callable[[], T],
-        max_retries: int = 10,
-        retry_delay: float = 2,
-    ) -> T:
-        """
-        Unified retry logic for all operations with failure message handling.
-
-        Args:
-            operation: The operation to retry
-            max_retries: Maximum number of retry attempts
-            retry_delay: Delay between retries in seconds
-
-        Returns:
-            The result of the operation
-
-        Raises:
-            Exception: If the operation fails after all retries, includes order failure message if available
-        """
-        last_exception = None
-
-        for attempt in range(max_retries):
-            try:
-                return operation()
-            except Exception as e:
-                last_exception = e
-                if attempt < max_retries - 1:
-                    sleep(retry_delay)
-
-        failure_message = self._get_order_failure_message()
-        if failure_message:
-            raise Exception(failure_message) from last_exception
-
-        raise Exception(
-            f"Operation failed after {max_retries} retries: {str(last_exception)}"
-        ) from last_exception
-
-    def _wait_for_state(
-        self,
-        target_states: list[OrderState],
-        check_interval: float = 5,
-        status_message: str | None = None,
-    ) -> str:
-        """
-        Wait until the order reaches one of the target states.
-
-        Args:
-            target_states: List of states to wait for
-            check_interval: How often to check the state in seconds
-            status_message: Optional message to display while waiting
-
-        Returns:
-            The final state reached
-        """
-        while (current_state := self.get_status()) not in target_states:
-            if status_message:
-                logger.debug(status_message, self, current_state)
-            sleep(check_interval)
-
-        return current_state
-
-    @property
-    def created_at(self) -> datetime:
-        """Returns the creation date of the order."""
-        if not self.__created_at:
-            self.__created_at = self._openapi_service.order_api.order_order_id_get(
-                self.id
-            ).order_date
-        return self.__created_at
-
-    def __get_pipeline_id(self) -> str:
-        """Gets the pipeline ID for this order (cached, internal use only)."""
-        if not self.__pipeline_id:
-            self.__pipeline_id = self._retry_operation(
-                lambda: self._openapi_service.order_api.order_order_id_get(
-                    self.id
-                ).pipeline_id,
-            )
-        return self.__pipeline_id
-
-    def __get_workflow_id(self) -> str:
-        """Gets the workflow ID for this order (cached, internal use only)."""
-        if not self.__workflow_id:
-            self.__load_workflow_and_campaign_ids()
-        return self.__workflow_id
-
-    def __get_campaign_id(self) -> str:
-        """Gets the campaign ID for this order (cached, internal use only)."""
-        if not self.__campaign_id:
-            self.__load_workflow_and_campaign_ids()
-        return self.__campaign_id
-
-    def __load_workflow_and_campaign_ids(self) -> None:
-        """Loads workflow and campaign IDs from the pipeline (with retry logic)."""
-        if self.__workflow_id and self.__campaign_id:
-            return
-
-        def fetch_ids():
-            from rapidata.api_client.models.workflow_artifact_model import (
-                WorkflowArtifactModel,
-            )
-            from rapidata.api_client.models.campaign_artifact_model import (
-                CampaignArtifactModel,
-            )
-
-            pipeline_id = self.__get_pipeline_id()
-            pipeline = self._openapi_service.pipeline_api.pipeline_pipeline_id_get(
-                pipeline_id
-            )
-            self.__workflow_id = cast(
-                WorkflowArtifactModel,
-                pipeline.artifacts["workflow-artifact"].actual_instance,
-            ).workflow_id
-            self.__campaign_id = cast(
-                CampaignArtifactModel,
-                pipeline.artifacts["campaign-artifact"].actual_instance,
-            ).campaign_id
-
-        self._retry_operation(fetch_ids)
-
-    def __get_workflow_progress(self) -> GetWorkflowProgressResult:
-        """Gets the workflow progress (internal use only)."""
-
-        def get_progress():
-            from rapidata.api_client.models.get_workflow_progress_result import (
-                GetWorkflowProgressResult,
-            )
-
-            with suppress_rapidata_error_logging():
-                workflow_id = self.__get_workflow_id()
-                return self._openapi_service.workflow_api.workflow_workflow_id_progress_get(
-                    workflow_id
-                )
-
-        return self._retry_operation(
-            get_progress,
-            max_retries=5,
-            retry_delay=4,
-        )
-
-    def run(self) -> RapidataOrder:
-        """Runs the order to start collecting responses."""
-        with tracer.start_as_current_span("RapidataOrder.run"):
-            from rapidata.api_client.models.submit_order_model import SubmitOrderModel
-
-            logger.info("Starting order '%s'", self)
-            self._openapi_service.order_api.order_order_id_submit_post(
-                self.id, SubmitOrderModel(ignoreFailedDatapoints=True)
-            )
-            logger.debug("Order '%s' has been started.", self)
-            managed_print(
-                f"Order '{self.name}' is now viewable under: {self.order_details_page}"
-            )
-            return self
-
-    def pause(self) -> None:
-        """Pauses the order."""
-        with tracer.start_as_current_span("RapidataOrder.pause"):
-            logger.info("Pausing order '%s'", self)
-            self._openapi_service.order_api.order_order_id_pause_post(self.id)
-            logger.debug("Order '%s' has been paused.", self)
-            managed_print(f"Order '{self}' has been paused.")
-
-    def unpause(self) -> None:
-        """Unpauses/resumes the order."""
-        with tracer.start_as_current_span("RapidataOrder.unpause"):
-            logger.info("Unpausing order '%s'", self)
-            self._openapi_service.order_api.order_order_id_resume_post(self.id)
-            logger.debug("Order '%s' has been unpaused.", self)
-            managed_print(f"Order '{self}' has been unpaused.")
-
-    def delete(self) -> None:
-        """Deletes the order."""
-        with tracer.start_as_current_span("RapidataOrder.delete"):
-            logger.info("Deleting order '%s'", self)
-            self._openapi_service.order_api.order_order_id_delete(self.id)
-            logger.debug("Order '%s' has been deleted.", self)
-            managed_print(f"Order '{self}' has been deleted.")
-
-    def get_status(self) -> str:
-        """
-        Gets the status of the order.
-
-        States:
-            Created: The order has been created but not started yet.\n
-            Preview: The order has been set up and ready but not collecting responses yet.\n
-            Submitted: The order has been submitted and is being reviewed.\n
-            ManualReview: The order is in manual review - something went wrong with the automatic approval.\n
-            Processing: The order is actively being processed.\n
-            Paused: The order has been paused.\n
-            Completed: The order has been completed.\n
-            Failed: The order has failed.
-        """
-        with tracer.start_as_current_span("RapidataOrder.get_status"):
-            return self._openapi_service.order_api.order_order_id_get(self.id).state
-
-    def display_progress_bar(self, refresh_rate: int = 5) -> None:
-        """
-        Displays a progress bar for the order processing using tqdm.
-
-        Args:
-            refresh_rate: How often to refresh the progress bar, in seconds.
-        """
-        from rapidata.api_client.models.order_state import OrderState
-
-        if refresh_rate < 1:
-            raise ValueError("refresh_rate must be at least 1")
-
-        if self.get_status() == OrderState.CREATED:
-            raise Exception("Order has not been started yet. Please start it first.")
-
-        # Wait for submission review
-        while self.get_status() == OrderState.SUBMITTED:
-            managed_print(
-                f"Order '{self}' is submitted and being reviewed. Standby...", end="\r"
-            )
-            sleep(1)
-
-        if self.get_status() == OrderState.MANUALREVIEW:
-            raise Exception(
-                f"Order '{self}' is in manual review. It might take some time to start. "
-                f"To speed up the process, contact support (info@rapidata.ai).\n"
-                f"Once started, run this method again to display the progress bar."
-            )
-
-        with tqdm(
-            total=100,
-            desc="Processing order",
-            unit="%",
-            bar_format="{desc}: {percentage:3.0f}%|{bar}| completed [{elapsed}<{remaining}, {rate_fmt}]",
-            disable=rapidata_config.logging.silent_mode,
-        ) as pbar:
-            last_percentage = 0
-            while True:
-                current_percentage = (
-                    self.__get_workflow_progress().completion_percentage
-                )
-
-                if current_percentage > last_percentage:
-                    pbar.update(current_percentage - last_percentage)
-                    last_percentage = current_percentage
-
-                if current_percentage >= 100:
-                    break
-
-                sleep(refresh_rate)
-
-    def get_results(self, preliminary_results: bool = False) -> RapidataResults:
-        """
-        Gets the results of the order.
-        If the order is still processing, this method will block until the order is completed and then return the results.
-
-        Args:
-            preliminary_results: If True, returns the preliminary results of the order. Defaults to False.
-                Note that preliminary results are not final and may not contain all the datapoints & responses. Only the ones that are already available.
-        """
-        with tracer.start_as_current_span("RapidataOrder.get_results"):
-            from rapidata.api_client.models.order_state import OrderState
-            from rapidata.api_client.exceptions import ApiException
-            from rapidata.rapidata_client.order.rapidata_results import RapidataResults
-
-            logger.info("Getting results for order '%s'...", self)
-
-            if preliminary_results and self.get_status() not in [OrderState.COMPLETED]:
-                return self._get_preliminary_results()
-
-            if preliminary_results and self.get_status() == OrderState.COMPLETED:
-                managed_print("Order is already completed. Returning final results.")
-
-            self._wait_for_state(
-                target_states=[
-                    OrderState.COMPLETED,
-                    OrderState.PAUSED,
-                    OrderState.MANUALREVIEW,
-                    OrderState.FAILED,
-                ],
-                status_message="Order '%s' is in state %s, waiting for completion...",
-            )
-
-            try:
-                results = (
-                    self._openapi_service.order_api.order_order_id_download_results_get(
-                        order_id=self.id
-                    )
-                )
-                return RapidataResults(json.loads(results))
-            except (ApiException, json.JSONDecodeError) as e:
-                raise Exception(f"Failed to get order results: {str(e)}") from e
-
-    def _get_preliminary_results(self) -> RapidataResults:
-        """Fetches preliminary results for an in-progress order."""
-        from rapidata.api_client.models.preliminary_download_model import (
-            PreliminaryDownloadModel,
-        )
-        from rapidata.api_client.exceptions import ApiException
-        from rapidata.rapidata_client.order.rapidata_results import RapidataResults
-
-        try:
-            pipeline_id = self.__get_pipeline_id()
-            download_id = self._openapi_service.pipeline_api.pipeline_pipeline_id_preliminary_download_post(
-                pipeline_id, PreliminaryDownloadModel(sendEmail=False)
-            ).download_id
-
-            def check_results():
-                results = self._openapi_service.pipeline_api.pipeline_preliminary_download_preliminary_download_id_get(
-                    preliminary_download_id=download_id
-                )
-                return RapidataResults(json.loads(results))
-
-            return self._retry_operation(
-                check_results,
-                max_retries=60,
-                retry_delay=1,
-            )
-
-        except (ApiException, json.JSONDecodeError) as e:
-            raise Exception(f"Failed to get preliminary results: {str(e)}") from e
-
-    def view(self) -> None:
-        """Opens the order details page in the browser."""
-        logger.info("Opening order details page in browser...")
-        if not webbrowser.open(self.order_details_page):
-            encoded_url = urllib.parse.quote(
-                self.order_details_page, safe="%/:=&?~#+!$,;'@()*[]"
-            )
-            managed_print(
-                Fore.RED
-                + f"Please open this URL in your browser: '{encoded_url}'"
-                + Fore.RESET
-            )
-
-    def preview(self) -> None:
-        """Opens a preview of the order in the browser."""
-        from rapidata.api_client.models.order_state import OrderState
-        from rapidata.api_client.models.preview_order_model import PreviewOrderModel
-
-        logger.info("Opening order preview in browser...")
-
-        if self.get_status() == OrderState.CREATED:
-            logger.info("Order is still in state created. Setting it to preview.")
-            self._openapi_service.order_api.order_order_id_preview_post(
-                self.id, PreviewOrderModel(ignoreFailedDatapoints=True)
-            )
-            logger.info("Order is now in preview state.")
-
-        campaign_id = self.__get_campaign_id()
-        auth_url = f"https://app.{self._openapi_service.environment}/order/detail/{self.id}/preview?campaignId={campaign_id}"
-
-        if not webbrowser.open(auth_url):
-            encoded_url = urllib.parse.quote(auth_url, safe="%/:=&?~#+!$,;'@()*[]")
-            managed_print(
-                Fore.RED
-                + f"Please open this URL in your browser: '{encoded_url}'"
-                + Fore.RESET
-            )
-
-    def __str__(self) -> str:
-        return f"RapidataOrder(name='{self.name}', order_id='{self.id}')"
-
-    def __repr__(self) -> str:
-        return f"RapidataOrder(name='{self.name}', order_id='{self.id}')"
diff --git a/src/rapidata/rapidata_client/rapidata_client.py b/src/rapidata/rapidata_client/rapidata_client.py
index 29e8f48ce..e3f3fde6f 100644
--- a/src/rapidata/rapidata_client/rapidata_client.py
+++ b/src/rapidata/rapidata_client/rapidata_client.py
@@ -7,13 +7,11 @@
 import random
 from rapidata.service.openapi_service import OpenAPIService
 
-from rapidata.rapidata_client.order.rapidata_order_manager import RapidataOrderManager
 from rapidata.rapidata_client.benchmark.rapidata_benchmark_manager import (
     RapidataBenchmarkManager,
 )
-
-from rapidata.rapidata_client.validation.validation_set_manager import (
-    ValidationSetManager,
+from rapidata.rapidata_client.audience.rapidata_audience_manager import (
+    RapidataAudienceManager,
 )
 
 from rapidata.rapidata_client.demographic.demographic_manager import DemographicManager
@@ -25,6 +23,9 @@
     rapidata_config,
 )
 
+from rapidata.rapidata_client.datapoints._asset_uploader import AssetUploader
+from rapidata.rapidata_client.job.job_manager import JobManager
+
 
 class RapidataClient:
     """The Rapidata client is the main entry point for interacting with the Rapidata API. It allows you to create orders and validation sets."""
@@ -53,8 +54,7 @@ def __init__(
 
         Attributes:
             order (RapidataOrderManager): The RapidataOrderManager instance.
-            validation (ValidationSetManager): The ValidationSetManager instance.
-            demographic (DemographicManager): The DemographicManager instance.
+            audience (RapidataAudienceManager): The RapidataAudienceManager instance.
             mri (RapidataBenchmarkManager): The RapidataBenchmarkManager instance.
         """
         tracer.set_session_id(
@@ -78,31 +78,35 @@ def __init__(
                 leeway=leeway,
             )
 
-            logger.debug("Initializing RapidataOrderManager")
-            self.order = RapidataOrderManager(openapi_service=self._openapi_service)
+            self._asset_uploader = AssetUploader(openapi_service=self._openapi_service)
+
+            logger.debug("Initializing JobManager")
+            self.job = JobManager(openapi_service=self._openapi_service)
 
-            logger.debug("Initializing ValidationSetManager")
-            self.validation = ValidationSetManager(
+            logger.debug("Initializing RapidataBenchmarkManager")
+            self.mri = RapidataBenchmarkManager(openapi_service=self._openapi_service)
+
+            logger.debug("Initializing RapidataAudienceManager")
+            self.audience = RapidataAudienceManager(
                 openapi_service=self._openapi_service
             )
 
-            logger.debug("Initializing DemographicManager")
+            logger.debug("Initializing RapidataDemographicManager")
             self._demographic = DemographicManager(
                 openapi_service=self._openapi_service
             )
 
-            logger.debug("Initializing RapidataBenchmarkManager")
-            self.mri = RapidataBenchmarkManager(openapi_service=self._openapi_service)
-
         self._check_beta_features()  # can't be in the trace for some reason
 
     def reset_credentials(self):
         """Reset the credentials saved in the configuration file for the current environment."""
+        logger.info("Resetting credentials")
         self._openapi_service.reset_credentials()
+        logger.info("Credentials reset")
 
     def clear_all_caches(self):
         """Clear all caches for the client."""
-        self.order._asset_uploader.clear_cache()
+        self._asset_uploader.clear_cache()
         logger.info("All caches cleared")
 
     def _check_beta_features(self):
diff --git a/src/rapidata/rapidata_client/referee/_base_referee.py b/src/rapidata/rapidata_client/referee/_base_referee.py
index 8a4b4abf7..99e5a9675 100644
--- a/src/rapidata/rapidata_client/referee/_base_referee.py
+++ b/src/rapidata/rapidata_client/referee/_base_referee.py
@@ -1,5 +1,6 @@
 from abc import ABC, abstractmethod
 from typing import Mapping
+import json
 
 from rapidata.api_client.models.i_referee_model import IRefereeModel
 
@@ -22,3 +23,9 @@ def _to_model(self) -> IRefereeModel:
         Convert the referee to a referee configuration model.
         """
         pass
+
+    def __str__(self) -> str:
+        return json.dumps(self._to_dict())
+
+    def __repr__(self) -> str:
+        return self.__str__()
diff --git a/src/rapidata/rapidata_client/referee/_early_stopping_referee.py b/src/rapidata/rapidata_client/referee/_early_stopping_referee.py
index 7feb05005..513a86129 100644
--- a/src/rapidata/rapidata_client/referee/_early_stopping_referee.py
+++ b/src/rapidata/rapidata_client/referee/_early_stopping_referee.py
@@ -23,28 +23,28 @@ class EarlyStoppingReferee(Referee):
     Args:
         threshold (float, optional): The confidence threshold for early stopping.
             Defaults to 0.999.
-        max_vote_count (int, optional): The maximum number of votes allowed
+        max_responses (int, optional): The maximum number of votes allowed
             before stopping. Defaults to 100.
 
     Attributes:
         threshold (float): The confidence threshold for early stopping.
-        max_vote_count (int): The maximum number of votes allowed before stopping.
+        max_responses (int): The maximum number of votes allowed before stopping.
     """
 
-    def __init__(self, threshold: float = 0.999, max_vote_count: int = 100):
+    def __init__(self, threshold: float = 0.999, max_responses: int = 100):
         if threshold <= 0 or threshold >= 1:
             raise ValueError("The threshold must be between 0 and 1.")
-        if max_vote_count < 1:
+        if max_responses < 1:
             raise ValueError("The number of responses must be greater than 0.")
 
         self.threshold = threshold
-        self.max_vote_count = max_vote_count
+        self.max_responses = max_responses
 
     def _to_dict(self):
         return {
             "_t": "ProbabilisticAttachCategoryRefereeConfig",
             "threshold": self.threshold,
-            "maxVotes": self.max_vote_count,
+            "maxVotes": self.max_responses,
         }
 
     def _to_model(self) -> IRefereeModel:
@@ -57,6 +57,12 @@ def _to_model(self) -> IRefereeModel:
             actual_instance=IRefereeModelEarlyStoppingRefereeModel(
                 _t="EarlyStoppingReferee",
                 threshold=self.threshold,
-                maxVotes=self.max_vote_count,
+                maxVotes=self.max_responses,
             )
         )
+
+    def __str__(self) -> str:
+        return f"EarlyStoppingReferee(threshold={self.threshold}, max_responses={self.max_responses})"
+
+    def __repr__(self) -> str:
+        return self.__str__()
diff --git a/src/rapidata/rapidata_client/referee/_naive_referee.py b/src/rapidata/rapidata_client/referee/_naive_referee.py
index 1387aaf6c..b276f4a80 100644
--- a/src/rapidata/rapidata_client/referee/_naive_referee.py
+++ b/src/rapidata/rapidata_client/referee/_naive_referee.py
@@ -46,3 +46,9 @@ def _to_model(self) -> IRefereeModel:
                 totalVotes=self.responses,
             )
         )
+
+    def __str__(self) -> str:
+        return f"NaiveReferee(responses={self.responses})"
+
+    def __repr__(self) -> str:
+        return self.__str__()
diff --git a/src/rapidata/rapidata_client/selection/rapidata_selections.py b/src/rapidata/rapidata_client/selection/rapidata_selections.py
index 06b0ecd3c..dfc525682 100644
--- a/src/rapidata/rapidata_client/selection/rapidata_selections.py
+++ b/src/rapidata/rapidata_client/selection/rapidata_selections.py
@@ -33,15 +33,15 @@ class RapidataSelections:
         This will require annotators to complete one validation task followed by two labeling tasks.
     """
 
-    labeling = LabelingSelection
-    validation = ValidationSelection
-    conditional_validation = ConditionalValidationSelection
-    demographic = DemographicSelection
-    capped = CappedSelection
-    shuffling = ShufflingSelection
+    Labeling = LabelingSelection
+    Validation = ValidationSelection
+    Conditional_validation = ConditionalValidationSelection
+    Demographic = DemographicSelection
+    Capped = CappedSelection
+    Shuffling = ShufflingSelection
 
     def __str__(self) -> str:
-        return f"RapidataSelections(labeling={self.labeling}, validation={self.validation}, conditional_validation={self.conditional_validation}, demographic={self.demographic}, capped={self.capped}, shuffling={self.shuffling})"
+        return f"RapidataSelections(Labeling={self.Labeling}, Validation={self.Validation}, Conditional_validation={self.Conditional_validation}, Demographic={self.Demographic}, Capped={self.Capped}, Shuffling={self.Shuffling})"
 
     def __repr__(self) -> str:
         return self.__str__()
diff --git a/src/rapidata/rapidata_client/settings/_rapidata_setting.py b/src/rapidata/rapidata_client/settings/_rapidata_setting.py
index e58c84964..b3bf0efaa 100644
--- a/src/rapidata/rapidata_client/settings/_rapidata_setting.py
+++ b/src/rapidata/rapidata_client/settings/_rapidata_setting.py
@@ -3,7 +3,7 @@
 from typing import Any, TYPE_CHECKING
 
 if TYPE_CHECKING:
-    from rapidata.api_client.models.feature_flag_model import FeatureFlagModel
+    from rapidata.api_client.models.feature_flag import FeatureFlag
 
 
 class RapidataSetting(BaseModel):
@@ -12,10 +12,8 @@ class RapidataSetting(BaseModel):
     key: str
     value: Any
 
-    def _to_feature_flag(self) -> FeatureFlagModel:
-        from rapidata.api_client.models.feature_flag_model import FeatureFlagModel
-
-        return FeatureFlagModel(key=self.key, value=str(self.value))
+    def _to_feature_flag(self) -> FeatureFlag:
+        return FeatureFlag(key=self.key, value=str(self.value))
 
     def __str__(self) -> str:
         return f"{self.__class__.__name__}(key='{self.key}', value={self.value})"
diff --git a/src/rapidata/rapidata_client/settings/rapidata_settings.py b/src/rapidata/rapidata_client/settings/rapidata_settings.py
index 54abff7da..336de1242 100644
--- a/src/rapidata/rapidata_client/settings/rapidata_settings.py
+++ b/src/rapidata/rapidata_client/settings/rapidata_settings.py
@@ -35,17 +35,17 @@ class RapidataSettings:
         This can be used in a free text order to set the minimum number of characters required to submit the task.
     """
 
-    alert_on_fast_response = AlertOnFastResponse
-    translation_behaviour = TranslationBehaviour
-    free_text_minimum_characters = FreeTextMinimumCharacters
-    no_shuffle = NoShuffle
-    play_video_until_the_end = PlayVideoUntilTheEnd
-    allow_neither_both = AllowNeitherBoth
-    swap_context_instruction = SwapContextInstruction
-    mute_video = MuteVideo
+    Alert_on_fast_response = AlertOnFastResponse
+    Translation_behaviour = TranslationBehaviour
+    Free_text_minimum_characters = FreeTextMinimumCharacters
+    No_shuffle = NoShuffle
+    Play_video_until_the_end = PlayVideoUntilTheEnd
+    Allow_neither_both = AllowNeitherBoth
+    Swap_context_instruction = SwapContextInstruction
+    Mute_video = MuteVideo
 
     def __str__(self) -> str:
-        return f"RapidataSettings(alert_on_fast_response={self.alert_on_fast_response}, translation_behaviour={self.translation_behaviour}, free_text_minimum_characters={self.free_text_minimum_characters}, no_shuffle={self.no_shuffle}, play_video_until_the_end={self.play_video_until_the_end}, allow_neither_both={self.allow_neither_both}, swap_context_instruction={self.swap_context_instruction}, mute_video={self.mute_video})"
+        return f"RapidataSettings(Alert_on_fast_response={self.Alert_on_fast_response}, Translation_behaviour={self.Translation_behaviour}, Free_text_minimum_characters={self.Free_text_minimum_characters}, No_shuffle={self.No_shuffle}, Play_video_until_the_end={self.Play_video_until_the_end}, Allow_neither_both={self.Allow_neither_both}, Swap_context_instruction={self.Swap_context_instruction}, Mute_video={self.Mute_video})"
 
     def __repr__(self) -> str:
         return self.__str__()
diff --git a/src/rapidata/rapidata_client/utils/__init__.py b/src/rapidata/rapidata_client/utils/__init__.py
new file mode 100644
index 000000000..db492e3a1
--- /dev/null
+++ b/src/rapidata/rapidata_client/utils/__init__.py
@@ -0,0 +1,3 @@
+from .threaded_uploader import ThreadedUploader
+
+__all__ = ["ThreadedUploader"]
diff --git a/src/rapidata/rapidata_client/order/dataset/_rapidata_dataset.py b/src/rapidata/rapidata_client/utils/threaded_uploader.py
similarity index 52%
rename from src/rapidata/rapidata_client/order/dataset/_rapidata_dataset.py
rename to src/rapidata/rapidata_client/utils/threaded_uploader.py
index d476712c9..438d0e912 100644
--- a/src/rapidata/rapidata_client/order/dataset/_rapidata_dataset.py
+++ b/src/rapidata/rapidata_client/utils/threaded_uploader.py
@@ -1,63 +1,72 @@
-from rapidata.rapidata_client.datapoints._datapoint import Datapoint
-from rapidata.service.openapi_service import OpenAPIService
+from typing import TypeVar, Generic, Callable
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from tqdm import tqdm
+import time
 
-from typing import Generator
 from rapidata.rapidata_client.config import logger
-import time
-from rapidata.rapidata_client.api.rapidata_api_client import (
-    suppress_rapidata_error_logging,
-)
 from rapidata.rapidata_client.config.rapidata_config import rapidata_config
-from rapidata.rapidata_client.datapoints._datapoint_uploader import DatapointUploader
 
-# Add OpenTelemetry context imports for thread propagation
 from opentelemetry import context as otel_context
 
+T = TypeVar("T")
 
-def chunk_list(lst: list, chunk_size: int) -> Generator:
+
+def _chunk_list(lst: list[T], chunk_size: int):
+    """Split a list into chunks of specified size."""
     for i in range(0, len(lst), chunk_size):
         yield lst[i : i + chunk_size]
 
 
-class RapidataDataset:
-    def __init__(self, dataset_id: str, openapi_service: OpenAPIService):
-        self.id = dataset_id
-        self.openapi_service = openapi_service
-        self.datapoint_uploader = DatapointUploader(openapi_service)
+class ThreadedUploader(Generic[T]):
+    """
+    A generic multi-threaded uploader that handles retries, progress tracking,
+    and OpenTelemetry context propagation.
+
+    Type Parameters:
+        T: The type of items being uploaded.
+    """
 
-    def add_datapoints(
+    def __init__(
         self,
-        datapoints: list[Datapoint],
-    ) -> tuple[list[Datapoint], list[Datapoint]]:
+        upload_fn: Callable[[T, int], None],
+        description: str = "Uploading items",
+    ):
+        """
+        Initialize the threaded uploader.
+
+        Args:
+            upload_fn: A function that uploads a single item. Takes (item, index) as arguments.
+            description: Description shown in the progress bar.
         """
-        Process uploads in chunks with a ThreadPoolExecutor.
+        self.upload_fn = upload_fn
+        self.description = description
+
+    def upload(self, items: list[T]) -> tuple[list[T], list[T]]:
+        """
+        Upload items in parallel using multiple threads.
 
         Args:
-            media_paths: List of assets to upload
-            multi_metadata: Optional sequence of sequences of metadata
-            chunk_size: Number of items to process in each batch
+            items: List of items to upload.
 
         Returns:
-            tuple[list[str], list[str]]: Lists of successful and failed uploads
+            tuple[list[T], list[T]]: Lists of successful and failed uploads.
         """
-        successful_uploads: list[Datapoint] = []
-        failed_uploads: list[Datapoint] = []
+        successful_uploads: list[T] = []
+        failed_uploads: list[T] = []
 
         with tqdm(
-            total=len(datapoints),
-            desc="Uploading datapoints",
+            total=len(items),
+            desc=self.description,
             disable=rapidata_config.logging.silent_mode,
         ) as progress_bar:
 
             def process_upload_with_context(
-                context: otel_context.Context, datapoint: Datapoint, index: int
-            ) -> tuple[list[Datapoint], list[Datapoint]]:
-                """Wrapper function that runs _process_single_upload with the provided context."""
+                context: otel_context.Context, item: T, index: int
+            ) -> tuple[list[T], list[T]]:
+                """Wrapper function that runs upload with the provided context."""
                 token = otel_context.attach(context)
                 try:
-                    return self._process_single_upload(datapoint, index)
+                    return self._process_single_upload(item, index)
                 finally:
                     otel_context.detach(token)
 
@@ -69,16 +78,16 @@ def process_upload_with_context(
             ) as executor:
                 # Process uploads in chunks to avoid overwhelming the system
                 for chunk_idx, chunk in enumerate(
-                    chunk_list(datapoints, rapidata_config.upload.chunkSize)
+                    _chunk_list(items, rapidata_config.upload.chunkSize)
                 ):
                     futures = [
                         executor.submit(
                             process_upload_with_context,
                             current_context,
-                            datapoint,
+                            item,
                             chunk_idx * rapidata_config.upload.chunkSize + i,
                         )
-                        for i, datapoint in enumerate(chunk)
+                        for i, item in enumerate(chunk)
                     ]
 
                     # Wait for this chunk to complete before starting the next one
@@ -87,13 +96,15 @@ def process_upload_with_context(
                             chunk_successful, chunk_failed = future.result()
                             successful_uploads.extend(chunk_successful)
                             failed_uploads.extend(chunk_failed)
-                            progress_bar.update(len(chunk_successful))
+                            progress_bar.update(
+                                len(chunk_successful) + len(chunk_failed)
+                            )
                         except Exception as e:
                             logger.error("Future execution failed: %s", str(e))
 
         if failed_uploads:
             logger.error(
-                "Upload failed for %s datapoints: %s",
+                "Upload failed for %s items: %s",
                 len(failed_uploads),
                 failed_uploads,
             )
@@ -102,38 +113,35 @@ def process_upload_with_context(
 
     def _process_single_upload(
         self,
-        datapoint: Datapoint,
+        item: T,
         index: int,
-    ) -> tuple[list[Datapoint], list[Datapoint]]:
+    ) -> tuple[list[T], list[T]]:
         """
         Process single upload with retry logic and error tracking.
 
         Args:
-            media_asset: MediaAsset or MultiAsset to upload
-            meta_list: Optional sequence of metadata for the asset
-            index: Sort index for the upload
-            max_retries: Maximum number of retry attempts (default: 3)
+            item: Item to upload.
+            index: Sort index for the upload.
 
         Returns:
-            tuple[list[Datapoint], list[Datapoint]]: Lists of successful and failed datapoints
+            tuple[list[T], list[T]]: Lists of successful and failed items.
         """
-        logger.debug("Processing single upload for %s with index %s", datapoint, index)
+        logger.debug("Processing single upload for %s with index %s", item, index)
 
-        local_successful: list[Datapoint] = []
-        local_failed: list[Datapoint] = []
+        local_successful: list[T] = []
+        local_failed: list[T] = []
 
         last_exception = None
         for attempt in range(rapidata_config.upload.maxRetries):
             try:
-                with suppress_rapidata_error_logging():
-                    self.datapoint_uploader.upload_datapoint(
-                        dataset_id=self.id,
-                        datapoint=datapoint,
-                        index=index,
-                    )
+                from rapidata.rapidata_client.api.rapidata_api_client import (
+                    suppress_rapidata_error_logging,
+                )
 
-                local_successful.append(datapoint)
+                with suppress_rapidata_error_logging():
+                    self.upload_fn(item, index)
 
+                local_successful.append(item)
                 return local_successful, local_failed
 
             except Exception as e:
@@ -150,15 +158,9 @@ def _process_single_upload(
                     )
 
         # If we get here, all retries failed
-        local_failed.append(datapoint)
+        local_failed.append(item)
         tqdm.write(
-            f"Upload failed for {datapoint} after {rapidata_config.upload.maxRetries} attempts. \nFinal error: \n{str(last_exception)}"
+            f"Upload failed for {item} after {rapidata_config.upload.maxRetries} attempts. \nFinal error: \n{str(last_exception)}"
         )
 
         return local_successful, local_failed
-
-    def __str__(self) -> str:
-        return f"RapidataDataset(id={self.id})"
-
-    def __repr__(self) -> str:
-        return self.__str__()
diff --git a/src/rapidata/rapidata_client/validation/__init__.py b/src/rapidata/rapidata_client/validation/__init__.py
deleted file mode 100644
index cecba38fe..000000000
--- a/src/rapidata/rapidata_client/validation/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .rapids import Box
diff --git a/src/rapidata/rapidata_client/validation/rapidata_validation_set.py b/src/rapidata/rapidata_client/validation/rapidata_validation_set.py
deleted file mode 100644
index 16790ee39..000000000
--- a/src/rapidata/rapidata_client/validation/rapidata_validation_set.py
+++ /dev/null
@@ -1,141 +0,0 @@
-import webbrowser
-import urllib.parse
-from colorama import Fore
-from rapidata.rapidata_client.validation.rapids.rapids import Rapid
-from rapidata.service.openapi_service import OpenAPIService
-from rapidata.rapidata_client.config import logger, managed_print, tracer
-from rapidata.api_client.models.update_validation_set_model import (
-    UpdateValidationSetModel,
-)
-from rapidata.api_client.models.update_should_alert_model import UpdateShouldAlertModel
-from rapidata.rapidata_client.validation.rapids._validation_rapid_uploader import (
-    ValidationRapidUploader,
-)
-
-
-class RapidataValidationSet:
-    """A class for interacting with a Rapidata validation set.
-
-    Represents a set of all the validation tasks that can be added to an order.
-
-    When added to an order, the tasks will be selected randomly from the set.
-
-    Attributes:
-        id (str): The ID of the validation set.
-        name (str): The name of the validation set.
-    """
-
-    def __init__(
-        self,
-        validation_set_id,
-        name: str,
-        dimensions: list[str],
-        openapi_service: OpenAPIService,
-    ):
-        self.id = validation_set_id
-        self.name = name
-        self.dimensions = dimensions
-        self.validation_set_details_page = (
-            f"https://app.{openapi_service.environment}/validation-set/detail/{self.id}"
-        )
-        self._openapi_service = openapi_service
-        self.validation_rapid_uploader = ValidationRapidUploader(openapi_service)
-
-    def add_rapid(self, rapid: Rapid):
-        """Add a Rapid to the validation set.
-
-        Args:
-            rapid (Rapid): The Rapid to add to the validation set.
-        """
-        with tracer.start_as_current_span("RapidataValidationSet.add_rapid"):
-            logger.debug("Adding rapid %s to validation set %s", rapid, self.id)
-            self.validation_rapid_uploader.upload_rapid(rapid, self.id)
-        return self
-
-    def update_dimensions(self, dimensions: list[str]):
-        """Update the dimensions of the validation set.
-
-        Args:
-            dimensions (list[str]): The new dimensions of the validation set.
-        """
-        with tracer.start_as_current_span("RapidataValidationSet.update_dimensions"):
-            logger.debug(
-                "Updating dimensions for validation set %s to %s", self.id, dimensions
-            )
-            self._openapi_service.validation_api.validation_set_validation_set_id_patch(
-                self.id, UpdateValidationSetModel(dimensions=dimensions)
-            )
-            self.dimensions = dimensions
-            return self
-
-    def update_should_alert(self, should_alert: bool):
-        """Determines whether users should be alerted if they answer incorrectly.
-
-        Args:
-            should_alert (bool): Specifies whether users should be alerted for incorrect answers. Defaults to True if not specifically overridden by this method.
-
-        Note:
-            The userScore dimensions which are updated when a user answers a validation task are updated regardless of the value of `should_alert`.
-        """
-        with tracer.start_as_current_span("RapidataValidationSet.update_should_alert"):
-            logger.debug(
-                "Setting shouldAlert for validation set %s to %s", self.id, should_alert
-            )
-            self._openapi_service.validation_api.validation_set_validation_set_id_patch(
-                self.id, UpdateValidationSetModel(shouldAlert=should_alert)
-            )
-            return self
-
-    def update_can_be_flagged(self, can_be_flagged: bool):
-        """Update if tasks in the validation set can be flagged for bad accuracy.
-
-        Args:
-            can_be_flagged (bool): Specifies whether tasks in the validation set can be flagged for bad accuracy. Defaults to True if not specifically overridden by this method.
-        """
-        with tracer.start_as_current_span(
-            "RapidataValidationSet.update_can_be_flagged"
-        ):
-            logger.debug(
-                "Setting canBeFlagged for validation set %s to %s",
-                self.id,
-                can_be_flagged,
-            )
-            self._openapi_service.validation_api.validation_set_validation_set_id_patch(
-                self.id, UpdateValidationSetModel(isFlagOverruled=(not can_be_flagged))
-            )
-            return self
-
-    def view(self) -> None:
-        """
-        Opens the validation set details page in the browser.
-
-        Raises:
-            Exception: If the order is not in processing state.
-        """
-        logger.info("Opening validation set details page in browser...")
-        could_open_browser = webbrowser.open(self.validation_set_details_page)
-        if not could_open_browser:
-            encoded_url = urllib.parse.quote(
-                self.validation_set_details_page, safe="%/:=&?~#+!$,;'@()*[]"
-            )
-            managed_print(
-                Fore.RED
-                + f"Please open this URL in your browser: '{encoded_url}'"
-                + Fore.RESET
-            )
-
-    def delete(self) -> None:
-        """Deletes the validation set"""
-        with tracer.start_as_current_span("RapidataValidationSet.delete"):
-            logger.info("Deleting ValidationSet '%s'", self)
-            self._openapi_service.validation_api.validation_set_validation_set_id_delete(
-                self.id
-            )
-            logger.debug("ValidationSet '%s' has been deleted.", self)
-            managed_print(f"ValidationSet '{self}' has been deleted.")
-
-    def __str__(self):
-        return f"name: '{self.name}' id: {self.id} dimensions: {self.dimensions}"
-
-    def __repr__(self):
-        return f"name: '{self.name}' id: {self.id} dimensions: {self.dimensions}"
diff --git a/src/rapidata/rapidata_client/validation/rapids/__init__.py b/src/rapidata/rapidata_client/validation/rapids/__init__.py
deleted file mode 100644
index 50d0046ac..000000000
--- a/src/rapidata/rapidata_client/validation/rapids/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .box import Box
diff --git a/src/rapidata/rapidata_client/validation/rapids/_validation_rapid_uploader.py b/src/rapidata/rapidata_client/validation/rapids/_validation_rapid_uploader.py
deleted file mode 100644
index cd90fba14..000000000
--- a/src/rapidata/rapidata_client/validation/rapids/_validation_rapid_uploader.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from rapidata.rapidata_client.validation.rapids.rapids import Rapid
-from rapidata.service.openapi_service import OpenAPIService
-from rapidata.api_client.models.add_validation_rapid_model import (
-    AddValidationRapidModel,
-)
-from rapidata.api_client.models.add_validation_rapid_model_truth import (
-    AddValidationRapidModelTruth,
-)
-from rapidata.rapidata_client.datapoints._asset_uploader import AssetUploader
-from rapidata.api_client.models.add_validation_rapid_model_asset import (
-    AddValidationRapidModelAsset,
-)
-from rapidata.api_client.models.add_validation_rapid_model_context_asset import (
-    AddValidationRapidModelContextAsset,
-)
-from rapidata.api_client.models.i_asset_input import IAssetInput
-from rapidata.api_client.models.i_rapid_payload import IRapidPayload
-from rapidata.api_client.models.i_validation_truth import IValidationTruth
-
-
-class ValidationRapidUploader:
-    def __init__(self, openapi_service: OpenAPIService):
-        self.openapi_service = openapi_service
-        self.asset_uploader = AssetUploader(openapi_service)
-
-    def upload_rapid(self, rapid: Rapid, validation_set_id: str) -> None:
-        uploaded_asset: IAssetInput = (
-            self.asset_uploader.get_uploaded_asset_input(rapid.asset)
-            if rapid.data_type == "media"
-            else self.asset_uploader.get_uploaded_text_input(rapid.asset)
-        )
-
-        self.openapi_service.validation_api.validation_set_validation_set_id_rapid_post(
-            validation_set_id=validation_set_id,
-            add_validation_rapid_model=AddValidationRapidModel(
-                asset=uploaded_asset,
-                payload=self._get_payload(rapid),
-                context=rapid.context,
-                contextAsset=(
-                    self.asset_uploader.get_uploaded_asset_input(rapid.media_context)
-                    if rapid.media_context
-                    else None
-                ),
-                truth=(
-                    IValidationTruth(actual_instance=rapid.truth)
-                    if rapid.truth
-                    else None
-                ),
-                randomCorrectProbability=rapid.random_correct_probability,
-                explanation=rapid.explanation,
-                featureFlags=(
-                    [setting._to_feature_flag() for setting in rapid.settings]
-                    if rapid.settings
-                    else None
-                ),
-            ),
-        )
-
-    def _get_payload(self, rapid: Rapid) -> IRapidPayload:
-        if isinstance(rapid.payload, dict):
-            return IRapidPayload(actual_instance=rapid.payload)
-        return IRapidPayload(actual_instance=rapid.payload.to_dict())
diff --git a/src/rapidata/rapidata_client/validation/rapids/box.py b/src/rapidata/rapidata_client/validation/rapids/box.py
deleted file mode 100644
index 161708229..000000000
--- a/src/rapidata/rapidata_client/validation/rapids/box.py
+++ /dev/null
@@ -1,42 +0,0 @@
-from rapidata.api_client.models.box_shape import BoxShape
-from pydantic import BaseModel, field_validator, model_validator
-
-
-class Box(BaseModel):
-    """
-    Used in the Locate and Draw Validation sets. All coordinates are in ratio of the image size (0.0 to 1.0).
-
-    Args:
-        x_min (float): The minimum x value of the box in ratio of the image size.
-        y_min (float): The minimum y value of the box in ratio of the image size.
-        x_max (float): The maximum x value of the box in ratio of the image size.
-        y_max (float): The maximum y value of the box in ratio of the image size.
-    """
-
-    x_min: float
-    y_min: float
-    x_max: float
-    y_max: float
-
-    @field_validator("x_min", "y_min", "x_max", "y_max")
-    @classmethod
-    def coordinates_between_zero_and_one(cls, v: float) -> float:
-        if not (0.0 <= v <= 1.0):
-            raise ValueError("Box coordinates must be between 0 and 1")
-        return v
-
-    @model_validator(mode="after")
-    def check_min_less_than_max(self) -> "Box":
-        if self.x_min >= self.x_max:
-            raise ValueError("x_min must be less than x_max")
-        if self.y_min >= self.y_max:
-            raise ValueError("y_min must be less than y_max")
-        return self
-
-    def to_model(self) -> BoxShape:
-        return BoxShape(
-            xMin=self.x_min * 100,
-            yMin=self.y_min * 100,
-            xMax=self.x_max * 100,
-            yMax=self.y_max * 100,
-        )
diff --git a/src/rapidata/rapidata_client/validation/rapids/rapids.py b/src/rapidata/rapidata_client/validation/rapids/rapids.py
deleted file mode 100644
index 5b2a371fa..000000000
--- a/src/rapidata/rapidata_client/validation/rapids/rapids.py
+++ /dev/null
@@ -1,29 +0,0 @@
-from rapidata.rapidata_client.settings._rapidata_setting import RapidataSetting
-from typing import Literal, Any, Sequence
-from pydantic import BaseModel, model_validator, ConfigDict
-from rapidata.api_client.models.i_rapid_payload import IRapidPayload
-
-
-class Rapid(BaseModel):
-    asset: str | list[str]
-    payload: IRapidPayload
-    data_type: Literal["media", "text"] = "media"
-    truth: Any | None = None
-    context: str | None = None
-    media_context: str | None = None
-    sentence: str | None = None
-    random_correct_probability: float | None = None
-    explanation: str | None = None
-    settings: Sequence[RapidataSetting] | None = None
-
-    model_config = ConfigDict(
-        arbitrary_types_allowed=True, populate_by_name=True, extra="allow"
-    )
-
-    @model_validator(mode="after")
-    def check_sentence_and_context(self) -> "Rapid":
-        if isinstance(self.sentence, str) and isinstance(self.context, str):
-            raise ValueError(
-                "Both 'sentence' and 'context' cannot be strings at the same time."
-            )
-        return self
diff --git a/src/rapidata/rapidata_client/validation/rapids/rapids_manager.py b/src/rapidata/rapidata_client/validation/rapids/rapids_manager.py
deleted file mode 100644
index af22dfe59..000000000
--- a/src/rapidata/rapidata_client/validation/rapids/rapids_manager.py
+++ /dev/null
@@ -1,469 +0,0 @@
-import os
-from typing import Literal
-
-from rapidata.api_client.models.i_validation_truth_attach_category_truth import (
-    IValidationTruthAttachCategoryTruth,
-)
-from rapidata.api_client.models.i_validation_truth_bounding_box_truth import (
-    IValidationTruthBoundingBoxTruth,
-)
-from rapidata.api_client.models.i_rapid_payload_classify_payload import (
-    IRapidPayloadClassifyPayload,
-)
-from rapidata.api_client.models.classify_payload_category import ClassifyPayloadCategory
-from rapidata.api_client.models.i_rapid_payload_compare_payload import (
-    IRapidPayloadComparePayload,
-)
-from rapidata.api_client.models.i_validation_truth_compare_truth import (
-    IValidationTruthCompareTruth,
-)
-from rapidata.api_client.models.i_rapid_payload_line_payload import (
-    IRapidPayloadLinePayload,
-)
-from rapidata.api_client.models.i_validation_truth_locate_box_truth import (
-    IValidationTruthLocateBoxTruth,
-)
-from rapidata.api_client.models.i_rapid_payload_locate_payload import (
-    IRapidPayloadLocatePayload,
-)
-from rapidata.api_client.models.i_rapid_payload_scrub_payload import (
-    IRapidPayloadScrubPayload,
-)
-from rapidata.api_client.models.scrub_payload import ScrubPayload
-from rapidata.api_client.models.scrub_range import ScrubRange
-from rapidata.api_client.models.i_validation_truth_scrub_truth import (
-    IValidationTruthScrubTruth,
-)
-from rapidata.api_client.models.scrub_truth import ScrubTruth
-from rapidata.api_client.models.i_rapid_payload_transcription_payload import (
-    IRapidPayloadTranscriptionPayload,
-)
-from rapidata.api_client.models.i_validation_truth_transcription_truth import (
-    IValidationTruthTranscriptionTruth,
-)
-from rapidata.api_client.models.transcription_word import TranscriptionWord
-from rapidata.rapidata_client.validation.rapids.box import Box
-from rapidata.rapidata_client.validation.rapids.rapids import Rapid
-from rapidata.service.openapi_service import OpenAPIService
-from rapidata.api_client.models.i_rapid_payload import IRapidPayload
-
-
-class RapidsManager:
-    """
-    Can be used to build different types of rapids. That can then be added to Validation sets
-    """
-
-    def __init__(self, openapi_service: OpenAPIService):
-        self._openapi_service = openapi_service
-
-    def classification_rapid(
-        self,
-        instruction: str,
-        answer_options: list[str],
-        datapoint: str,
-        truths: list[str],
-        data_type: Literal["media", "text"] = "media",
-        context: str | None = None,
-        media_context: str | None = None,
-        explanation: str | None = None,
-    ) -> Rapid:
-        """Build a classification rapid
-
-        Args:
-            instruction (str): The instruction/question to be shown to the labeler.
-            answer_options (list[str]): The options that the labeler can choose from to answer the question.
-            datapoint (str): The datapoint that the labeler will be labeling.
-            truths (list[str]): The correct answers to the question.
-            data_type (str, optional): The type of the datapoint. Defaults to "media" (any form of image, video or audio).
-            context (str, optional): The context is text that will be shown in addition to the instruction. Defaults to None.
-            media_context (str, optional): The media context is a link to an image / video that will be shown in addition to the instruction (can be combined with context). Defaults to None.
-            explanation (str, optional): The explanation that will be shown to the labeler if the answer is wrong. Defaults to None.
-        """
-        if not isinstance(truths, list):
-            raise ValueError("Truths must be a list of strings")
-
-        if not all(truth in answer_options for truth in truths):
-            raise ValueError("Truths must be part of the answer options")
-
-        payload = IRapidPayload(
-            actual_instance=IRapidPayloadClassifyPayload(
-                _t="ClassifyPayload",
-                categories=[
-                    ClassifyPayloadCategory(label=option, value=option)
-                    for option in answer_options
-                ],
-                title=instruction,
-            )
-        )
-        model_truth = IValidationTruthAttachCategoryTruth(
-            correctCategories=truths, _t="AttachCategoryTruth"
-        )
-
-        return Rapid(
-            asset=datapoint,
-            data_type=data_type,
-            context=context,
-            media_context=media_context,
-            explanation=explanation,
-            payload=payload,
-            truth=model_truth,
-            random_correct_probability=len(truths) / len(answer_options),
-        )
-
-    def compare_rapid(
-        self,
-        instruction: str,
-        truth: str,
-        datapoint: list[str],
-        data_type: Literal["media", "text"] = "media",
-        context: str | None = None,
-        media_context: str | None = None,
-        explanation: str | None = None,
-    ) -> Rapid:
-        """Build a compare rapid
-
-        Args:
-            instruction (str): The instruction that the labeler will be comparing the assets on.
-            truth (str): The correct answer to the comparison. (has to be one of the assets)
-            datapoint (list[str]): The two assets that the labeler will be comparing.
-            data_type (str, optional): The type of the datapoint. Defaults to "media" (any form of image, video or audio).
-            context (str, optional): The context is text that will be shown in addition to the instruction. Defaults to None.
-            media_context (str, optional): The media context is a link to an image / video that will be shown in addition to the instruction (can be combined with context). Defaults to None.
-            explanation (str, optional): The explanation that will be shown to the labeler if the answer is wrong. Defaults to None.
-        """
-
-        payload = IRapidPayload(
-            actual_instance=IRapidPayloadComparePayload(
-                _t="ComparePayload", criteria=instruction
-            )
-        )
-        truth = os.path.basename(truth)
-        model_truth = IValidationTruthCompareTruth(_t="CompareTruth", winnerId=truth)
-
-        if len(datapoint) != 2:
-            raise ValueError("Compare rapid requires exactly two media paths")
-
-        return Rapid(
-            asset=datapoint,
-            data_type=data_type,
-            truth=model_truth,
-            context=context,
-            media_context=media_context,
-            payload=payload,
-            explanation=explanation,
-            random_correct_probability=0.5,
-        )
-
-    def select_words_rapid(
-        self,
-        instruction: str,
-        truths: list[int],
-        datapoint: str,
-        sentence: str,
-        required_precision: float = 1,
-        required_completeness: float = 1,
-        explanation: str | None = None,
-    ) -> Rapid:
-        """Build a select words rapid
-
-        Args:
-            instruction (str): The instruction for the labeler.
-            truths (list[int]): The indices of the words that are the correct answers.
-            datapoint (str): The asset that the labeler will be selecting words from.
-            sentence (str): The sentence that the labeler will be selecting words from. (split up by spaces)
-            required_precision (float): The required precision for the labeler to get the rapid correct (minimum ratio of the words selected that need to be correct). defaults to 1. (no wrong words can be selected)
-            required_completeness (float): The required completeness for the labeler to get the rapid correct (miminum ratio of total correct words selected). defaults to 1. (all correct words need to be selected)
-            explanation (str, optional): The explanation that will be shown to the labeler if the answer is wrong. Defaults to None.
-        """
-
-        transcription_words = [
-            TranscriptionWord(word=word, wordIndex=i)
-            for i, word in enumerate(sentence.split(" "))
-        ]
-
-        correct_transcription_words: list[TranscriptionWord] = []
-        for index in truths:
-            correct_transcription_words.append(
-                TranscriptionWord(word=transcription_words[index].word, wordIndex=index)
-            )
-
-        payload = IRapidPayload(
-            actual_instance=IRapidPayloadTranscriptionPayload(
-                _t="TranscriptionPayload",
-                title=instruction,
-                transcription=transcription_words,
-            )
-        )
-
-        model_truth = IValidationTruthTranscriptionTruth(
-            _t="TranscriptionTruth",
-            correctWords=correct_transcription_words,
-            requiredPrecision=required_precision,
-            requiredCompleteness=required_completeness,
-        )
-
-        return Rapid(
-            payload=payload,
-            truth=model_truth,
-            asset=datapoint,
-            sentence=sentence,
-            explanation=explanation,
-            random_correct_probability=len(correct_transcription_words)
-            / len(transcription_words),
-        )
-
-    def locate_rapid(
-        self,
-        instruction: str,
-        truths: list[Box],
-        datapoint: str,
-        context: str | None = None,
-        media_context: str | None = None,
-        explanation: str | None = None,
-    ) -> Rapid:
-        """Build a locate rapid
-
-        Args:
-            instruction (str): The instruction on what the labeler should do.
-            truths (list[Box]): The bounding boxes of the object that the labeler ought to be locating.
-            datapoint (str): The asset that the labeler will be locating the object in.
-            context (str, optional): The context is text that will be shown in addition to the instruction. Defaults to None.
-            media_context (str, optional): The media context is a link to an image / video that will be shown in addition to the instruction (can be combined with context). Defaults to None.
-            explanation (str, optional): The explanation that will be shown to the labeler if the answer is wrong. Defaults to None.
-        """
-
-        payload = IRapidPayload(
-            actual_instance=IRapidPayloadLocatePayload(
-                _t="LocatePayload", target=instruction
-            )
-        )
-
-        model_truth = IValidationTruthLocateBoxTruth(
-            _t="LocateBoxTruth",
-            boundingBoxes=[truth.to_model() for truth in truths],
-        )
-
-        coverage = self._calculate_boxes_coverage(
-            truths,
-        )
-
-        return Rapid(
-            payload=payload,
-            truth=model_truth,
-            asset=datapoint,
-            context=context,
-            media_context=media_context,
-            explanation=explanation,
-            random_correct_probability=coverage,
-        )
-
-    def draw_rapid(
-        self,
-        instruction: str,
-        truths: list[Box],
-        datapoint: str,
-        context: str | None = None,
-        media_context: str | None = None,
-        explanation: str | None = None,
-    ) -> Rapid:
-        """Build a draw rapid
-
-        Args:
-            instruction (str): The instructions on what the labeler
-            truths (list[Box]): The bounding boxes of the object that the labeler ought to be drawing.
-            datapoint (str): The asset that the labeler will be drawing the object in.
-            context (str, optional): The context is text that will be shown in addition to the instruction. Defaults to None.
-            media_context (str, optional): The media context is a link to an image / video that will be shown in addition to the instruction (can be combined with context). Defaults to None.
-            explanation (str, optional): The explanation that will be shown to the labeler if the answer is wrong. Defaults to None.
-        """
-
-        payload = IRapidPayload(
-            actual_instance=IRapidPayloadLinePayload(
-                _t="LinePayload", target=instruction
-            )
-        )
-
-        model_truth = IValidationTruthBoundingBoxTruth(
-            _t="BoundingBoxTruth",
-            xMax=truths[0].x_max * 100,
-            xMin=truths[0].x_min * 100,
-            yMax=truths[0].y_max * 100,
-            yMin=truths[0].y_min * 100,
-        )
-
-        coverage = self._calculate_boxes_coverage(
-            truths,
-        )
-
-        return Rapid(
-            payload=payload,
-            truth=model_truth,
-            asset=datapoint,
-            context=context,
-            media_context=media_context,
-            explanation=explanation,
-            random_correct_probability=coverage,
-        )
-
-    def timestamp_rapid(
-        self,
-        instruction: str,
-        truths: list[tuple[int, int]],
-        datapoint: str,
-        context: str | None = None,
-        media_context: str | None = None,
-        explanation: str | None = None,
-    ) -> Rapid:
-        """Build a timestamp rapid
-
-        Args:
-            instruction (str): The instruction for the labeler.
-            truths (list[tuple[int, int]]): The possible accepted timestamps intervals for the labeler (in miliseconds).
-                The first element of the tuple is the start of the interval and the second element is the end of the interval.
-            datapoint (str): The asset that the labeler will be timestamping.
-            context (str, optional): The context is text that will be shown in addition to the instruction. Defaults to None.
-            media_context (str, optional): The media context is a link to an image / video that will be shown in addition to the instruction (can be combined with context). Defaults to None.
-            explanation (str, optional): The explanation that will be shown to the labeler if the answer is wrong. Defaults to None.
-        """
-
-        for truth in truths:
-            if len(truth) != 2:
-                raise ValueError(
-                    "The truths per datapoint must be a tuple of exactly two integers."
-                )
-            if truth[0] > truth[1]:
-                raise ValueError(
-                    "The start of the interval must be smaller than the end of the interval."
-                )
-
-        payload = IRapidPayload(
-            actual_instance=IRapidPayloadScrubPayload(
-                _t="ScrubPayload", target=instruction
-            )
-        )
-
-        model_truth = IValidationTruthScrubTruth(
-            _t="ScrubTruth",
-            validRanges=[ScrubRange(start=truth[0], end=truth[1]) for truth in truths],
-        )
-
-        return Rapid(
-            payload=payload,
-            truth=model_truth,
-            asset=datapoint,
-            context=context,
-            media_context=media_context,
-            explanation=explanation,
-            random_correct_probability=0.5,  # TODO: implement coverage ratio
-        )
-
-    def _calculate_boxes_coverage(self, boxes: list[Box]) -> float:
-        """
-        Calculate the ratio of area covered by a list of boxes.
-
-        Args:
-            boxes: List of Box objects with coordinates in range [0, 1]
-
-        Returns:
-            float: Coverage ratio between 0.0 and 1.0
-        """
-        if not boxes:
-            return 0.0
-
-        # Convert boxes to intervals for sweep line algorithm
-        events = []
-
-        # Create events for x-coordinates
-        for i, box in enumerate(boxes):
-            events.append((box.x_min, "start", i, box))
-            events.append((box.x_max, "end", i, box))
-
-        # Sort events by x-coordinate
-        events.sort(key=lambda x: (x[0], x[1] == "end"))
-
-        total_area = 0.0
-        active_boxes = set()
-        prev_x = 0.0
-
-        for x, event_type, box_id, box in events:
-            # Calculate area for the previous x-interval
-            if active_boxes and x > prev_x:
-                # Merge y-intervals for active boxes
-                y_intervals = [(boxes[i].y_min, boxes[i].y_max) for i in active_boxes]
-                y_intervals.sort()
-
-                # Merge overlapping y-intervals
-                merged_intervals = []
-                for start, end in y_intervals:
-                    if merged_intervals and start <= merged_intervals[-1][1]:
-                        # Overlapping intervals - merge them
-                        merged_intervals[-1] = (
-                            merged_intervals[-1][0],
-                            max(merged_intervals[-1][1], end),
-                        )
-                    else:
-                        # Non-overlapping interval
-                        merged_intervals.append((start, end))
-
-                # Calculate total y-coverage for this x-interval
-                y_coverage = sum(end - start for start, end in merged_intervals)
-                total_area += (x - prev_x) * y_coverage
-
-            # Update active boxes
-            if event_type == "start":
-                active_boxes.add(box_id)
-            else:
-                active_boxes.discard(box_id)
-
-            prev_x = x
-
-        return total_area
-
-    @staticmethod
-    def _calculate_coverage_ratio(
-        total_duration: int, subsections: list[tuple[int, int]]
-    ) -> float:
-        """
-        Calculate the ratio of total_duration that is covered by subsections, handling overlaps.
-
-        Args:
-            total_duration: The total duration to consider
-            subsections: List of tuples containing (start, end) times
-
-        Returns:
-            float: Ratio of coverage (0 to 1)
-        """
-        if not subsections:
-            return 0.0
-
-        # Sort subsections by start time and clamp to valid range
-        sorted_ranges = sorted(
-            (max(0, start), min(end, total_duration)) for start, end in subsections
-        )
-
-        # Merge overlapping ranges
-        merged_ranges = []
-        current_range = list(sorted_ranges[0])
-
-        for next_start, next_end in sorted_ranges[1:]:
-            current_start, current_end = current_range
-
-            # If ranges overlap or are adjacent
-            if next_start <= current_end:
-                current_range[1] = max(current_end, next_end)
-            else:
-                merged_ranges.append(current_range)
-                current_range = [next_start, next_end]
-
-        merged_ranges.append(current_range)
-
-        # Calculate total coverage
-        total_coverage = sum(end - start for start, end in merged_ranges)
-
-        return total_coverage / total_duration
-
-    def __str__(self) -> str:
-        return "RapidsManager"
-
-    def __repr__(self) -> str:
-        return self.__str__()
diff --git a/src/rapidata/rapidata_client/validation/validation_set_manager.py b/src/rapidata/rapidata_client/validation/validation_set_manager.py
deleted file mode 100644
index bab6fa9f0..000000000
--- a/src/rapidata/rapidata_client/validation/validation_set_manager.py
+++ /dev/null
@@ -1,814 +0,0 @@
-from __future__ import annotations
-
-import time
-import urllib.parse
-import webbrowser
-from colorama import Fore
-from typing import Literal, TYPE_CHECKING
-from rapidata.api_client import QueryModel
-from rapidata.rapidata_client.validation.rapidata_validation_set import (
-    RapidataValidationSet,
-)
-from rapidata.api_client.models.create_validation_set_model import (
-    CreateValidationSetModel,
-)
-from rapidata.service.openapi_service import OpenAPIService
-from rapidata.rapidata_client.validation.rapids.rapids_manager import RapidsManager
-
-from rapidata.api_client.models.page_info import PageInfo
-from rapidata.api_client.models.root_filter import RootFilter
-from rapidata.api_client.models.filter import Filter
-from rapidata.api_client.models.sort_criterion import SortCriterion
-from rapidata.api_client.models.sort_direction import SortDirection
-from rapidata.api_client.models.filter_operator import FilterOperator
-
-from rapidata.rapidata_client.validation.rapids.box import Box
-
-from rapidata.rapidata_client.config import (
-    logger,
-    managed_print,
-    rapidata_config,
-    tracer,
-)
-from tqdm import tqdm
-from rapidata.rapidata_client.validation.rapids.rapids import Rapid
-
-if TYPE_CHECKING:
-    from rapidata.rapidata_client.workflow import Workflow
-    from rapidata.rapidata_client.datapoints._datapoint import Datapoint
-    from rapidata.rapidata_client.settings._rapidata_setting import RapidataSetting
-    from typing import Sequence
-
-
-class ValidationSetManager:
-    """
-    Responsible for everything related to validation sets. From creation to retrieval.
-
-    Attributes:
-        rapid (RapidsManager): The RapidsManager instance.
-    """
-
-    def __init__(self, openapi_service: OpenAPIService) -> None:
-        self._openapi_service = openapi_service
-        self.rapid = RapidsManager(openapi_service)
-        logger.debug("ValidationSetManager initialized")
-
-    def _get_total_and_labeled_rapids_count(
-        self, validation_set_id: str
-    ) -> tuple[int, int]:
-        uploaded_rapids = self._openapi_service.validation_api.validation_set_validation_set_id_rapids_get(
-            validation_set_id=validation_set_id
-        ).items
-        return len(uploaded_rapids), sum(1 for rapid in uploaded_rapids if rapid.truth)
-
-    def create_classification_set(
-        self,
-        name: str,
-        instruction: str,
-        answer_options: list[str],
-        datapoints: list[str],
-        truths: list[list[str]],
-        data_type: Literal["media", "text"] = "media",
-        contexts: list[str] | None = None,
-        media_contexts: list[str] | None = None,
-        explanations: list[str | None] | None = None,
-        dimensions: list[str] = [],
-    ) -> RapidataValidationSet:
-        """Create a classification validation set.
-
-        Args:
-            name (str): The name of the validation set. (will not be shown to the labeler)
-            instruction (str): The instruction by which the labeler will answer.
-            answer_options (list[str]): The options to choose from when answering.
-            datapoints (list[str]): The datapoints that will be used for validation.
-            truths (list[list[str]]): The truths for each datapoint. Outer list is for each datapoint, inner list is for each truth.\n
-                example:
-                    options: ["yes", "no", "maybe"]
-                    datapoints: ["datapoint1", "datapoint2"]
-                    truths: [["yes"], ["no", "maybe"]] -> first datapoint correct answer is "yes", second datapoint is "no" or "maybe"
-            data_type (str, optional): The type of data. Defaults to "media" (any form of image, video or audio). Other option: "text".
-            contexts (list[str], optional): The contexts for each datapoint. Defaults to None.\n
-                If provided has to be the same length as datapoints and will be shown in addition to the instruction and answer options. (Therefore will be different for each datapoint)
-                Will be match up with the datapoints using the list index.
-            media_contexts (list[str], optional): The list of media contexts i.e. links to the images / videos for the comparison. Defaults to None.\n
-                If provided has to be the same length as datapoints and will be shown in addition to the instruction. (Therefore will be different for each datapoint)
-                Will be matched up with the datapoints using the list index.
-            explanations (list[str | None], optional): The explanations for each datapoint. Will be given to the annotators in case the answer is wrong. Defaults to None.
-            dimensions (list[str], optional): The dimensions to add to the validation set accross which users will be tracked. Defaults to [] which is the default dimension.
-
-        Example:
-            ```python
-            options: ["yes", "no", "maybe"]
-            datapoints: ["datapoint1", "datapoint2"]
-            truths: [["yes"], ["no", "maybe"]]
-            ```
-            This would mean: first datapoint correct answer is "yes", second datapoint is "no" or "maybe"
-        """
-        with tracer.start_as_current_span(
-            "ValidationSetManager.create_classification_set"
-        ):
-            if not datapoints:
-                raise ValueError("Datapoints cannot be empty")
-
-            if len(datapoints) != len(truths):
-                raise ValueError("The number of datapoints and truths must be equal")
-
-            if not all([isinstance(truth, (list, tuple)) for truth in truths]):
-                raise ValueError("Truths must be a list of lists or tuples")
-
-            if contexts and len(contexts) != len(datapoints):
-                raise ValueError("The number of contexts and datapoints must be equal")
-
-            if media_contexts and len(media_contexts) != len(datapoints):
-                raise ValueError(
-                    "The number of media contexts and datapoints must be equal"
-                )
-
-            if explanations and len(explanations) != len(datapoints):
-                raise ValueError(
-                    "The number of explanations and datapoints must be equal, the index must align, but can be padded with None"
-                )
-
-            logger.debug("Creating classification rapids")
-            rapids: list[Rapid] = []
-            for i in range(len(datapoints)):
-                rapids.append(
-                    self.rapid.classification_rapid(
-                        instruction=instruction,
-                        answer_options=answer_options,
-                        datapoint=datapoints[i],
-                        truths=truths[i],
-                        data_type=data_type,
-                        context=contexts[i] if contexts != None else None,
-                        media_context=(
-                            media_contexts[i] if media_contexts != None else None
-                        ),
-                        explanation=explanations[i] if explanations != None else None,
-                    )
-                )
-
-            logger.debug("Submitting classification rapids")
-            return self._submit(name=name, rapids=rapids, dimensions=dimensions)
-
-    def create_compare_set(
-        self,
-        name: str,
-        instruction: str,
-        datapoints: list[list[str]],
-        truths: list[str],
-        data_type: Literal["media", "text"] = "media",
-        contexts: list[str] | None = None,
-        media_contexts: list[str] | None = None,
-        explanation: list[str | None] | None = None,
-        dimensions: list[str] = [],
-    ) -> RapidataValidationSet:
-        """Create a comparison validation set.
-
-        Args:
-            name (str): The name of the validation set. (will not be shown to the labeler)
-            instruction (str): The instruction to compare against.
-            truths (list[str]): The truths for each comparison. List is for each comparison.\n
-                example:
-                    instruction: "Which image has a cat?"
-                    datapoints = [["image1.jpg", "image2.jpg"], ["image3.jpg", "image4.jpg"]]
-                    truths: ["image1.jpg", "image4.jpg"] -> first comparison image1.jpg has a cat, second comparison image4.jpg has a cat
-            datapoints (list[list[str]]): The compare datapoints to create the validation set with.
-                Outer list is for each comparison, inner list the two images/texts that will be compared.
-            data_type (str, optional): The type of data. Defaults to "media" (any form of image, video or audio). Other option: "text".
-            contexts (list[str], optional): The contexts for each datapoint. Defaults to None.\n
-                If provided has to be the same length as datapoints and will be shown in addition to the instruction and truth. (Therefore will be different for each datapoint)
-                Will be match up with the datapoints using the list index.
-            media_contexts (list[str], optional): The list of media contexts i.e. links to the images / videos for the comparison. Defaults to None.\n
-                If provided has to be the same length as datapoints and will be shown in addition to the instruction. (Therefore will be different for each datapoint)
-                Will be matched up with the datapoints using the list index.
-            explanation (list[str | None], optional): The explanations for each datapoint. Will be given to the annotators in case the answer is wrong. Defaults to None.
-            dimensions (list[str], optional): The dimensions to add to the validation set accross which users will be tracked. Defaults to [] which is the default dimension.
-
-        Example:
-            ```python
-            instruction: "Which image has a cat?"
-            datapoints = [["image1.jpg", "image2.jpg"], ["image3.jpg", "image4.jpg"]]
-            truths: ["image1.jpg", "image4.jpg"]
-            ```
-            This would mean: first comparison image1.jpg has a cat, second comparison image4.jpg has a cat
-        """
-        with tracer.start_as_current_span("ValidationSetManager.create_compare_set"):
-            if not datapoints:
-                raise ValueError("Datapoints cannot be empty")
-
-            if len(datapoints) != len(truths):
-                raise ValueError("The number of datapoints and truths must be equal")
-
-            if not all([isinstance(truth, str) for truth in truths]):
-                raise ValueError("Truths must be a list of strings")
-
-            if contexts and len(contexts) != len(datapoints):
-                raise ValueError("The number of contexts and datapoints must be equal")
-
-            if media_contexts and len(media_contexts) != len(datapoints):
-                raise ValueError(
-                    "The number of media contexts and datapoints must be equal"
-                )
-
-            if explanation and len(explanation) != len(datapoints):
-                raise ValueError(
-                    "The number of explanations and datapoints must be equal, the index must align, but can be padded with None"
-                )
-
-            logger.debug("Creating comparison rapids")
-            rapids: list[Rapid] = []
-            for i in range(len(datapoints)):
-                rapids.append(
-                    self.rapid.compare_rapid(
-                        instruction=instruction,
-                        truth=truths[i],
-                        datapoint=datapoints[i],
-                        data_type=data_type,
-                        context=contexts[i] if contexts != None else None,
-                        media_context=(
-                            media_contexts[i] if media_contexts != None else None
-                        ),
-                        explanation=explanation[i] if explanation != None else None,
-                    )
-                )
-
-            logger.debug("Submitting comparison rapids")
-            return self._submit(name=name, rapids=rapids, dimensions=dimensions)
-
-    def create_select_words_set(
-        self,
-        name: str,
-        instruction: str,
-        truths: list[list[int]],
-        datapoints: list[str],
-        sentences: list[str],
-        required_precision: float = 1.0,
-        required_completeness: float = 1.0,
-        explanation: list[str | None] | None = None,
-        dimensions: list[str] = [],
-    ) -> RapidataValidationSet:
-        """Create a select words validation set.
-
-        Args:
-            name (str): The name of the validation set. (will not be shown to the labeler)
-            instruction (str): The instruction to show to the labeler.
-            truths (list[list[int]]): The truths for each datapoint. Outer list is for each datapoint, inner list is for each truth.\n
-                example:
-                    datapoints: ["datapoint1", "datapoint2"]
-                    sentences: ["this example 1", "this example with another text"]
-                    truths: [[0, 1], [2]] -> first datapoint correct words are "this" and "example", second datapoint is "with"
-            datapoints (list[str]): The datapoints that will be used for validation.
-            sentences (list[str]): The sentences that will be used for validation. The sentece will be split up by spaces to be selected by the labeler.
-                Must be the same length as datapoints.
-            required_precision (float, optional): The required precision for the labeler to get the rapid correct (minimum ratio of the words selected that need to be correct). Defaults to 1.0 (no wrong word can be selected).
-            required_completeness (float, optional): The required completeness for the labeler to get the rapid correct (miminum ratio of total correct words selected). Defaults to 1.0 (all correct words need to be selected).
-            explanation (list[str | None], optional): The explanations for each datapoint. Will be given to the annotators in case the answer is wrong. Defaults to None.
-            dimensions (list[str], optional): The dimensions to add to the validation set accross which users will be tracked. Defaults to [] which is the default dimension.
-
-        Example:
-            ```python
-            datapoints: ["datapoint1", "datapoint2"]
-            sentences: ["this example 1", "this example with another text"]
-            truths: [[0, 1], [2]]
-            ```
-            This would mean: first datapoint the correct words are "this" and "example", second datapoint is "with"
-        """
-        with tracer.start_as_current_span(
-            "ValidationSetManager.create_select_words_set"
-        ):
-            if not datapoints:
-                raise ValueError("Datapoints cannot be empty")
-
-            if not all([isinstance(truth, (list, tuple)) for truth in truths]):
-                raise ValueError("Truths must be a list of lists or tuples")
-
-            if len(datapoints) != len(truths) or len(datapoints) != len(sentences):
-                raise ValueError(
-                    "The number of datapoints, truths, and sentences must be equal"
-                )
-
-            if explanation and len(explanation) != len(datapoints):
-                raise ValueError(
-                    "The number of explanations and datapoints must be equal, the index must align, but can be padded with None"
-                )
-
-            logger.debug("Creating select words rapids")
-            rapids: list[Rapid] = []
-            for i in range(len(datapoints)):
-                rapids.append(
-                    self.rapid.select_words_rapid(
-                        instruction=instruction,
-                        truths=truths[i],
-                        datapoint=datapoints[i],
-                        sentence=sentences[i],
-                        required_precision=required_precision,
-                        required_completeness=required_completeness,
-                        explanation=explanation[i] if explanation != None else None,
-                    )
-                )
-
-            logger.debug("Submitting select words rapids")
-            return self._submit(name=name, rapids=rapids, dimensions=dimensions)
-
-    def create_locate_set(
-        self,
-        name: str,
-        instruction: str,
-        truths: list[list[Box]],
-        datapoints: list[str],
-        contexts: list[str] | None = None,
-        media_contexts: list[str] | None = None,
-        explanation: list[str | None] | None = None,
-        dimensions: list[str] = [],
-    ) -> RapidataValidationSet:
-        """Create a locate validation set.
-
-        Args:
-            name (str): The name of the validation set. (will not be shown to the labeler)
-            instruction (str): The instruction to show to the labeler.
-            truths (list[list[Box]]): The truths for each datapoint. Outer list is for each datapoint, inner list is for each truth.\n
-                example:
-                    datapoints: ["datapoint1", "datapoint2"]
-                    truths: [[Box(0, 0, 100, 100)], [Box(50, 50, 150, 150)]] -> first datapoint the object is in the top left corner, second datapoint the object is in the center
-            datapoints (list[str]): The datapoints that will be used for validation.
-            contexts (list[str], optional): The contexts for each datapoint. Defaults to None.
-            media_contexts (list[str], optional): The list of media contexts i.e. links to the images / videos for the comparison. Defaults to None.\n
-                If provided has to be the same length as datapoints and will be shown in addition to the instruction. (Therefore will be different for each datapoint)
-                Will be matched up with the datapoints using the list index.
-            explanation (list[str | None], optional): The explanations for each datapoint. Will be given to the annotators in case the answer is wrong. Defaults to None.
-            dimensions (list[str], optional): The dimensions to add to the validation set accross which users will be tracked. Defaults to [] which is the default dimension.
-
-        Example:
-            ```python
-            datapoints: ["datapoint1", "datapoint2"]
-            truths: [[Box(0, 0, 100, 100)], [Box(50, 50, 150, 150)]]
-            ```
-            This would mean: first datapoint the object is in the top left corner, second datapoint the object is in the center
-        """
-        with tracer.start_as_current_span("ValidationSetManager.create_locate_set"):
-            if not datapoints:
-                raise ValueError("Datapoints cannot be empty")
-
-            if len(datapoints) != len(truths):
-                raise ValueError("The number of datapoints and truths must be equal")
-
-            if not all([isinstance(truth, (list, tuple)) for truth in truths]):
-                raise ValueError("Truths must be a list of lists or tuples")
-
-            if contexts and len(contexts) != len(datapoints):
-                raise ValueError("The number of contexts and datapoints must be equal")
-
-            if media_contexts and len(media_contexts) != len(datapoints):
-                raise ValueError(
-                    "The number of media contexts and datapoints must be equal"
-                )
-
-            if explanation and len(explanation) != len(datapoints):
-                raise ValueError(
-                    "The number of explanations and datapoints must be equal, the index must align, but can be padded with None"
-                )
-
-            logger.debug("Creating locate rapids")
-            rapids = []
-            rapids: list[Rapid] = []
-            for i in range(len(datapoints)):
-                rapids.append(
-                    self.rapid.locate_rapid(
-                        instruction=instruction,
-                        truths=truths[i],
-                        datapoint=datapoints[i],
-                        context=contexts[i] if contexts != None else None,
-                        media_context=(
-                            media_contexts[i] if media_contexts != None else None
-                        ),
-                        explanation=explanation[i] if explanation != None else None,
-                    )
-                )
-
-            logger.debug("Submitting locate rapids")
-            return self._submit(name=name, rapids=rapids, dimensions=dimensions)
-
-    def create_draw_set(
-        self,
-        name: str,
-        instruction: str,
-        truths: list[list[Box]],
-        datapoints: list[str],
-        contexts: list[str] | None = None,
-        media_contexts: list[str] | None = None,
-        explanation: list[str | None] | None = None,
-        dimensions: list[str] = [],
-    ) -> RapidataValidationSet:
-        """Create a draw validation set.
-
-        Args:
-            name (str): The name of the validation set. (will not be shown to the labeler)
-            instruction (str): The instruction to show to the labeler.
-            truths (list[list[Box]]): The truths for each datapoint. Outer list is for each datapoint, inner list is for each truth.\n
-                example:
-                    datapoints: ["datapoint1", "datapoint2"]
-                    truths: [[Box(0, 0, 100, 100)], [Box(50, 50, 150, 150)]] -> first datapoint the object is in the top left corner, second datapoint the object is in the center
-            datapoints (list[str]): The datapoints that will be used for validation.
-            contexts (list[str], optional): The contexts for each datapoint. Defaults to None.
-            media_contexts (list[str], optional): The list of media contexts i.e. links to the images / videos for the comparison. Defaults to None.\n
-                If provided has to be the same length as datapoints and will be shown in addition to the instruction. (Therefore will be different for each datapoint)
-                Will be matched up with the datapoints using the list index.
-            explanation (list[str | None], optional): The explanations for each datapoint. Will be given to the annotators in case the answer is wrong. Defaults to None.
-            dimensions (list[str], optional): The dimensions to add to the validation set accross which users will be tracked. Defaults to [] which is the default dimension.
-
-        Example:
-            ```python
-            datapoints: ["datapoint1", "datapoint2"]
-            truths: [[Box(0, 0, 100, 100)], [Box(50, 50, 150, 150)]]
-            ```
-            This would mean: first datapoint the object is in the top left corner, second datapoint the object is in the center
-        """
-        with tracer.start_as_current_span("ValidationSetManager.create_draw_set"):
-            if not datapoints:
-                raise ValueError("Datapoints cannot be empty")
-
-            if len(datapoints) != len(truths):
-                raise ValueError("The number of datapoints and truths must be equal")
-
-            if not all([isinstance(truth, (list, tuple)) for truth in truths]):
-                raise ValueError("Truths must be a list of lists or tuples")
-
-            if contexts and len(contexts) != len(datapoints):
-                raise ValueError("The number of contexts and datapoints must be equal")
-
-            if media_contexts and len(media_contexts) != len(datapoints):
-                raise ValueError(
-                    "The number of media contexts and datapoints must be equal"
-                )
-
-            if explanation and len(explanation) != len(datapoints):
-                raise ValueError(
-                    "The number of explanations and datapoints must be equal, the index must align, but can be padded with None"
-                )
-
-            logger.debug("Creating draw rapids")
-            rapids: list[Rapid] = []
-            for i in range(len(datapoints)):
-                rapids.append(
-                    self.rapid.draw_rapid(
-                        instruction=instruction,
-                        truths=truths[i],
-                        datapoint=datapoints[i],
-                        context=contexts[i] if contexts != None else None,
-                        media_context=(
-                            media_contexts[i] if media_contexts != None else None
-                        ),
-                        explanation=explanation[i] if explanation != None else None,
-                    )
-                )
-
-            logger.debug("Submitting draw rapids")
-            return self._submit(name=name, rapids=rapids, dimensions=dimensions)
-
-    def create_timestamp_set(
-        self,
-        name: str,
-        instruction: str,
-        truths: list[list[tuple[int, int]]],
-        datapoints: list[str],
-        contexts: list[str] | None = None,
-        media_contexts: list[str] | None = None,
-        explanation: list[str | None] | None = None,
-        dimensions: list[str] = [],
-    ) -> RapidataValidationSet:
-        """Create a timestamp validation set.
-
-        Args:
-            name (str): The name of the validation set. (will not be shown to the labeler)
-            instruction (str): The instruction to show to the labeler.
-            truths (list[list[tuple[int, int]]]): The truths for each datapoint defined as start and endpoint based on miliseconds.
-                Outer list is for each datapoint, inner list is for each truth.\n
-                example:
-                    datapoints: ["datapoint1", "datapoint2"]
-                    truths: [[(0, 10)], [(20, 30)]] -> first datapoint the correct interval is from 0 to 10, second datapoint the correct interval is from 20 to 30
-            datapoints (list[str]): The datapoints that will be used for validation.
-            contexts (list[str], optional): The contexts for each datapoint. Defaults to None.
-            media_contexts (list[str], optional): The list of media contexts i.e. links to the images / videos for the comparison. Defaults to None.\n
-                If provided has to be the same length as datapoints and will be shown in addition to the instruction. (Therefore will be different for each datapoint)
-                Will be matched up with the datapoints using the list index.
-            explanation (list[str | None], optional): The explanations for each datapoint. Will be given to the annotators in case the answer is wrong. Defaults to None.
-            dimensions (list[str], optional): The dimensions to add to the validation set accross which users will be tracked. Defaults to [] which is the default dimension.
-
-        Example:
-            ```python
-            datapoints: ["datapoint1", "datapoint2"]
-            truths: [[(0, 10)], [(20, 30)]]
-            ```
-            This would mean: first datapoint the correct interval is from 0 to 10, second datapoint the correct interval is from 20 to 30
-        """
-        with tracer.start_as_current_span("ValidationSetManager.create_timestamp_set"):
-            if not datapoints:
-                raise ValueError("Datapoints cannot be empty")
-
-            if len(datapoints) != len(truths):
-                raise ValueError("The number of datapoints and truths must be equal")
-
-            if not all([isinstance(truth, (list, tuple)) for truth in truths]):
-                raise ValueError("Truths must be a list of lists or tuples")
-
-            if contexts and len(contexts) != len(datapoints):
-                raise ValueError("The number of contexts and datapoints must be equal")
-
-            if media_contexts and len(media_contexts) != len(datapoints):
-                raise ValueError(
-                    "The number of media contexts and datapoints must be equal"
-                )
-
-            if explanation and len(explanation) != len(datapoints):
-                raise ValueError(
-                    "The number of explanations and datapoints must be equal, the index must align, but can be padded with None"
-                )
-
-            logger.debug("Creating timestamp rapids")
-            rapids: list[Rapid] = []
-            for i in range(len(datapoints)):
-                rapids.append(
-                    self.rapid.timestamp_rapid(
-                        instruction=instruction,
-                        truths=truths[i],
-                        datapoint=datapoints[i],
-                        context=contexts[i] if contexts != None else None,
-                        media_context=(
-                            media_contexts[i] if media_contexts != None else None
-                        ),
-                        explanation=explanation[i] if explanation != None else None,
-                    )
-                )
-
-            logger.debug("Submitting timestamp rapids")
-            return self._submit(name=name, rapids=rapids, dimensions=dimensions)
-
-    def create_mixed_set(
-        self,
-        name: str,
-        rapids: list[Rapid],
-        dimensions: list[str] = [],
-    ) -> RapidataValidationSet:
-        """Create a validation set with a list of rapids.
-
-        Args:
-            name (str): The name of the validation set. (will not be shown to the labeler)
-            rapids (list[Rapid]): The list of rapids to add to the validation set.
-            dimensions (list[str], optional): The dimensions to add to the validation set accross which users will be tracked. Defaults to [] which is the default dimension.
-        """
-        with tracer.start_as_current_span("ValidationSetManager.create_mixed_set"):
-            if not rapids:
-                raise ValueError("Rapids cannot be empty")
-
-            return self._submit(name=name, rapids=rapids, dimensions=dimensions)
-
-    def _submit(
-        self, name: str, rapids: list[Rapid], dimensions: list[str]
-    ) -> RapidataValidationSet:
-        logger.debug("Creating validation set")
-        validation_set_id = (
-            self._openapi_service.validation_api.validation_set_post(
-                create_validation_set_model=CreateValidationSetModel(name=name)
-            )
-        ).validation_set_id
-
-        logger.debug("Validation set created with ID: %s", validation_set_id)
-
-        if validation_set_id is None:
-            raise ValueError("Failed to create validation set")
-
-        logger.debug("Creating validation set instance")
-
-        validation_set = RapidataValidationSet(
-            name=name,
-            validation_set_id=validation_set_id,
-            dimensions=dimensions,
-            openapi_service=self._openapi_service,
-        )
-        with tracer.start_as_current_span("Adding rapids to validation set"):
-            logger.debug("Adding rapids to validation set")
-            failed_rapids = []
-
-            progress_bar = tqdm(
-                total=len(rapids),
-                desc="Uploading validation tasks",
-                disable=rapidata_config.logging.silent_mode,
-            )
-
-            for rapid in rapids:
-                try:
-                    validation_set.add_rapid(rapid)
-                    progress_bar.update(1)
-                except Exception as e:
-                    logger.error(
-                        "Failed to add rapid %s to validation set.\nError: %s",
-                        rapid.asset,
-                        str(e),
-                    )
-                    failed_rapids.append(rapid.asset)
-
-            progress_bar.close()
-
-            if failed_rapids:
-                logger.error(
-                    "Failed to add %s datapoints to validation set: %s",
-                    len(failed_rapids),
-                    failed_rapids,
-                )
-                raise RuntimeError(
-                    f"Failed to add {len(failed_rapids)} datapoints to validation set: {failed_rapids}"
-                )
-
-        managed_print()
-        managed_print(
-            f"Validation set '{name}' created with ID {validation_set_id}\n",
-            f"Now viewable under: {validation_set.validation_set_details_page}",
-            sep="",
-        )
-
-        if dimensions:
-            validation_set.update_dimensions(dimensions)
-
-        return validation_set
-
-    def _create_order_validation_set(
-        self,
-        workflow: Workflow,
-        name: str,
-        datapoints: list[Datapoint],
-        required_amount: int,
-        settings: Sequence[RapidataSetting] | None = None,
-        dimensions: list[str] = [],
-    ) -> RapidataValidationSet:
-        with tracer.start_as_current_span(
-            "ValidationSetManager._create_order_validation_set"
-        ):
-            rapids: list[Rapid] = []
-            for datapoint in workflow._format_datapoints(datapoints):
-                rapids.append(
-                    Rapid(
-                        asset=datapoint.asset,
-                        payload=workflow._to_payload(datapoint),
-                        context=datapoint.context,
-                        media_context=datapoint.media_context,
-                        data_type=datapoint.data_type,
-                        settings=settings,
-                    )
-                )
-            validation_set = RapidataValidationSet(
-                validation_set_id=self._openapi_service.validation_api.validation_set_post(
-                    create_validation_set_model=CreateValidationSetModel(name=name)
-                ).validation_set_id,
-                name=name,
-                dimensions=dimensions,
-                openapi_service=self._openapi_service,
-            )
-
-            managed_print()
-            managed_print(
-                Fore.YELLOW
-                + f"A new validation set was created. Please annotate {required_amount} datapoint{('s' if required_amount != 1 else '')} before the order can run."
-                + Fore.RESET
-            )
-
-            link = f"https://app.{self._openapi_service.environment}/validation-set/detail/{validation_set.id}/annotate?maxSize={len(datapoints)}&required={required_amount}"
-            could_open_browser = webbrowser.open(link)
-            if not could_open_browser:
-                encoded_url = urllib.parse.quote(link, safe="%/:=&?~#+!$,;'@()*[]")
-                managed_print(
-                    Fore.RED
-                    + f"Please open this URL in your browser to annotate the validation set: '{encoded_url}'"
-                    + Fore.RESET
-                )
-            else:
-                managed_print(
-                    Fore.YELLOW
-                    + f"Please annotate the validation set. \n'{link}'"
-                    + Fore.RESET
-                )
-
-            with tracer.start_as_current_span("Annotating validation set"):
-                progress_bar = tqdm(
-                    total=required_amount,
-                    desc="Annotate the validation set",
-                    disable=rapidata_config.logging.silent_mode,
-                )
-
-                rapid_index = 0
-                while True:
-                    total_rapids, labeled_rapids = (
-                        self._get_total_and_labeled_rapids_count(validation_set.id)
-                    )
-
-                    progress_bar.n = labeled_rapids
-                    progress_bar.refresh()
-
-                    if labeled_rapids >= required_amount:
-                        break
-
-                    if total_rapids < required_amount and rapid_index >= len(rapids):
-                        managed_print(
-                            Fore.RED
-                            + f"""Warning: An order can only be started with at least {required_amount} annotated validation tasks. But only {labeled_rapids}/{required_amount} were annotated.
-Either add clearer examples or turn off the 'autoValidationSetCreation' with:
-
-from rapidata import rapidata_config
-rapidata_config.order.autoValidationSetCreation = False"""
-                            + Fore.RESET
-                        )
-                        raise RuntimeError(
-                            f"Not enough rapids annotated. Required: {required_amount}, Annotated: {labeled_rapids}"
-                        )
-
-                    if (
-                        rapid_index < len(rapids)
-                        and total_rapids - labeled_rapids <= required_amount * 2
-                    ):
-                        validation_set.add_rapid(rapids[rapid_index])
-                        rapid_index += 1
-
-                    time.sleep(2)
-
-                progress_bar.close()
-
-            validation_set.update_dimensions(dimensions)
-
-            return validation_set
-
-    def get_validation_set_by_id(self, validation_set_id: str) -> RapidataValidationSet:
-        """Get a validation set by ID.
-
-        Args:
-            validation_set_id (str): The ID of the validation set.
-
-        Returns:
-            RapidataValidationSet: The ValidationSet instance.
-        """
-
-        with tracer.start_as_current_span(
-            "ValidationSetManager.get_validation_set_by_id"
-        ):
-            logger.debug("Getting validation set by ID: %s", validation_set_id)
-            validation_set = self._openapi_service.validation_api.validation_set_validation_set_id_get(
-                validation_set_id=validation_set_id
-            )
-
-            return RapidataValidationSet(
-                validation_set_id,
-                str(validation_set.name),
-                validation_set.dimensions,
-                self._openapi_service,
-            )
-
-    def find_validation_sets(
-        self, name: str = "", amount: int = 10
-    ) -> list[RapidataValidationSet]:
-        """Find validation sets by name.
-
-        Args:
-            name (str, optional): The name to search for. Defaults to "" to match with any set.
-            amount (int, optional): The amount of validation sets to return. Defaults to 10.
-
-        Returns:
-            list[RapidataValidationSet]: The list of validation sets.
-        """
-        with tracer.start_as_current_span("ValidationSetManager.find_validation_sets"):
-            logger.debug(
-                "Finding validation sets with name: %s and amount: %s", name, amount
-            )
-
-            validation_page_result = (
-                self._openapi_service.validation_api.validation_sets_get(
-                    QueryModel(
-                        page=PageInfo(index=1, size=amount),
-                        filter=RootFilter(
-                            filters=[
-                                Filter(
-                                    field="Name",
-                                    operator=FilterOperator.CONTAINS,
-                                    value=name,
-                                )
-                            ]
-                        ),
-                        sortCriteria=[
-                            SortCriterion(
-                                direction=SortDirection.DESC, propertyName="CreatedAt"
-                            )
-                        ],
-                    )
-                )
-            )
-
-            logger.debug("Validation sets found: %s", validation_page_result.items)
-
-            validation_sets = [
-                self.get_validation_set_by_id(str(validation_set.id))
-                for validation_set in validation_page_result.items
-            ]
-            return validation_sets
-
-    def __str__(self) -> str:
-        return "ValidationSetManager"
-
-    def __repr__(self) -> str:
-        return self.__str__()
diff --git a/src/rapidata/service/openapi_service.py b/src/rapidata/service/openapi_service.py
index 2a810cf92..7295eb151 100644
--- a/src/rapidata/service/openapi_service.py
+++ b/src/rapidata/service/openapi_service.py
@@ -7,9 +7,11 @@
 from rapidata.api_client.configuration import Configuration
 from rapidata.service.credential_manager import CredentialManager
 from rapidata.rapidata_client.api.rapidata_api_client import RapidataApiClient
-from rapidata.rapidata_client.config import logger
+from rapidata.rapidata_client.config import logger, managed_print
+from authlib.integrations.httpx_client import OAuthError
 
 if TYPE_CHECKING:
+    from rapidata.api_client.api.job_api import JobApi
     from rapidata.api_client import CustomerRapidApi
     from rapidata.api_client.api.campaign_api import CampaignApi
     from rapidata.api_client.api.asset_api import AssetApi
@@ -21,6 +23,7 @@
     from rapidata.api_client.api.validation_set_api import ValidationSetApi
     from rapidata.api_client.api.workflow_api import WorkflowApi
     from rapidata.api_client.api.participant_api import ParticipantApi
+    from rapidata.api_client.api.audience_api import AudienceApi
 
 
 class OpenAPIService:
@@ -84,17 +87,42 @@ def __init__(
                 raise ValueError("Failed to fetch client credentials")
             client_id = credentials.client_id
             client_secret = credentials.client_secret
+        try:
+            self.api_client.rest_client.setup_oauth_client_credentials(
+                client_id=client_id,
+                client_secret=client_secret,
+                token_endpoint=f"{auth_endpoint}/connect/token",
+                scope=oauth_scope,
+            )
+        except OAuthError as e:
+            if e.error != "invalid_client":
+                raise
+            logger.warning(
+                "Invalid client credentials detected, resetting and retrying: %s", e
+            )
+            self.reset_credentials()
+
+            # Retry with fresh credentials
+            credentials = self.credential_manager.get_client_credentials()
+            if not credentials:
+                raise ValueError(
+                    "Failed to fetch client credentials after reset"
+                ) from e
+
+            self.api_client.rest_client.setup_oauth_client_credentials(
+                client_id=credentials.client_id,
+                client_secret=credentials.client_secret,
+                token_endpoint=f"{auth_endpoint}/connect/token",
+                scope=oauth_scope,
+            )
+            managed_print("Credentials were reset and re-authenticated successfully")
 
-        self.api_client.rest_client.setup_oauth_client_credentials(
-            client_id=client_id,
-            client_secret=client_secret,
-            token_endpoint=f"{auth_endpoint}/connect/token",
-            scope=oauth_scope,
-        )
         logger.debug("Client credentials authentication setup complete")
 
     def reset_credentials(self):
+        logger.info("Resetting credentials in OpenAPIService")
         self.credential_manager.reset_credentials()
+        logger.info("Credentials reset in OpenAPIService")
 
     @property
     def order_api(self) -> OrderApi:
@@ -102,6 +130,12 @@ def order_api(self) -> OrderApi:
 
         return OrderApi(self.api_client)
 
+    @property
+    def job_api(self) -> JobApi:
+        from rapidata.api_client.api.job_api import JobApi
+
+        return JobApi(self.api_client)
+
     @property
     def asset_api(self) -> AssetApi:
         from rapidata.api_client.api.asset_api import AssetApi
@@ -162,6 +196,12 @@ def participant_api(self) -> ParticipantApi:
 
         return ParticipantApi(self.api_client)
 
+    @property
+    def audience_api(self) -> AudienceApi:
+        from rapidata.api_client.api.audience_api import AudienceApi
+
+        return AudienceApi(self.api_client)
+
     def _get_rapidata_package_version(self):
         """
         Returns the version of the currently installed rapidata package.
diff --git a/src/rapidata/types/__init__.py b/src/rapidata/types/__init__.py
index adb1f1085..7c7a467ae 100644
--- a/src/rapidata/types/__init__.py
+++ b/src/rapidata/types/__init__.py
@@ -1,21 +1,11 @@
 # Main Client and Core Types
 from rapidata.rapidata_client.rapidata_client import RapidataClient
 
-# Order Types
-from rapidata.rapidata_client.order.rapidata_order import RapidataOrder
-from rapidata.rapidata_client.order.rapidata_order_manager import RapidataOrderManager
-from rapidata.rapidata_client.order.rapidata_results import RapidataResults
-
-# Validation Types
-from rapidata.rapidata_client.validation.rapidata_validation_set import (
-    RapidataValidationSet,
-)
-from rapidata.rapidata_client.validation.validation_set_manager import (
-    ValidationSetManager,
+# Audience Types
+from rapidata.rapidata_client.audience.rapidata_audience import RapidataAudience
+from rapidata.rapidata_client.audience.rapidata_audience_manager import (
+    RapidataAudienceManager,
 )
-from rapidata.rapidata_client.validation.rapids.rapids import Rapid
-from rapidata.rapidata_client.validation.rapids.box import Box
-from rapidata.rapidata_client.validation.rapids.rapids_manager import RapidsManager
 
 # Benchmark Types
 from rapidata.rapidata_client.benchmark.rapidata_benchmark import RapidataBenchmark
@@ -100,16 +90,9 @@
 __all__ = [
     # Main Client and Core Types
     "RapidataClient",
-    # Order Types
-    "RapidataOrder",
-    "RapidataOrderManager",
-    "RapidataResults",
-    # Validation Types
-    "RapidataValidationSet",
-    "ValidationSetManager",
-    "Rapid",
-    "Box",
-    "RapidsManager",
+    # Audience Types
+    "RapidataAudience",
+    "RapidataAudienceManager",
     # Benchmark Types
     "RapidataBenchmark",
     "RapidataBenchmarkManager",
diff --git a/uv.lock b/uv.lock
index d9bec2cf2..0a2f8bcf6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
 version = 1
-revision = 1
+revision = 3
 requires-python = ">=3.10, <4"
 resolution-markers = [
     "python_full_version >= '3.14'",
@@ -13,9 +13,9 @@ resolution-markers = [
 name = "annotated-types"
 version = "0.7.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081 }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643 },
+    { url = "https://files.pythonhosted.org/packages/78/b6/6307fbef88d9b5ee7421e68d78a9f162e0da4900bc5f5793f6d3d0e34fb8/annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53", size = 13643, upload-time = "2024-05-20T21:33:24.1Z" },
 ]
 
 [[package]]
@@ -27,18 +27,18 @@ dependencies = [
     { name = "idna" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/16/ce/8a777047513153587e5434fd752e89334ac33e379aa3497db860eeb60377/anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0", size = 228266 }
+sdist = { url = "https://files.pythonhosted.org/packages/16/ce/8a777047513153587e5434fd752e89334ac33e379aa3497db860eeb60377/anyio-4.12.0.tar.gz", hash = "sha256:73c693b567b0c55130c104d0b43a9baf3aa6a31fc6110116509f27bf75e21ec0", size = 228266, upload-time = "2025-11-28T23:37:38.911Z" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/7f/9c/36c5c37947ebfb8c7f22e0eb6e4d188ee2d53aa3880f3f2744fb894f0cb1/anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb", size = 113362 },
+    { url = "https://files.pythonhosted.org/packages/7f/9c/36c5c37947ebfb8c7f22e0eb6e4d188ee2d53aa3880f3f2744fb894f0cb1/anyio-4.12.0-py3-none-any.whl", hash = "sha256:dad2376a628f98eeca4881fc56cd06affd18f659b17a747d3ff0307ced94b1bb", size = 113362, upload-time = "2025-11-28T23:36:57.897Z" },
 ]
 
 [[package]]
 name = "appnope"
 version = "0.1.4"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170 }
+sdist = { url = "https://files.pythonhosted.org/packages/35/5d/752690df9ef5b76e169e68d6a129fa6d08a7100ca7f754c89495db3c6019/appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee", size = 4170, upload-time = "2024-02-06T09:43:11.258Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/81/29/5ecc3a15d5a33e31b26c11426c45c501e439cb865d0bff96315d86443b78/appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c", size = 4321, upload-time = "2024-02-06T09:43:09.663Z" },
 ]
 
 [[package]]
@@ -48,9 +48,9 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
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 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/0e/89/ce5af8a7d472a67cc819d5d998aa8c82c5d860608c4db9f46f1162d7dab9/argon2_cffi-25.1.0.tar.gz", hash = "sha256:694ae5cc8a42f4c4e2bf2ca0e64e51e23a040c6a517a85074683d3959e1346c1", size = 45706, upload-time = "2025-06-03T06:55:32.073Z" }
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+    { url = "https://files.pythonhosted.org/packages/4f/d3/a8b22fa575b297cd6e3e3b0155c7e25db170edf1c74783d6a31a2490b8d9/argon2_cffi-25.1.0-py3-none-any.whl", hash = "sha256:fdc8b074db390fccb6eb4a3604ae7231f219aa669a2652e0f20e16ba513d5741", size = 14657, upload-time = "2025-06-03T06:55:30.804Z" },
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 [[package]]
@@ -60,33 +60,33 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "cffi" },
 ]
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