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updatedAt: 2025-09-22T20:27:35.000Z
agentTools:
projectIndex: https://docs.v7labs.com/llms.txt
---
# Set up your team's workflow
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After you've named your dataset and uploaded your data, you can then generate your team's workflow. If you click '**New workflow**' within the Workflow tag, this will auto-generate a standard Workflow. We encourage you to give your Workflow a **recognizable name** to ensure that work is done in the correct workflow.
In the Classes tab you can configure the relevant Classes for your dataset:
You can always edit these Classes later, and include or exclude them from the dataset as below:
In the Settings tab you can add instructions for your team:
And then you are free to drag and drop any number of Workflow stages from the righthand toolbar.
The available stages are:
| Stage | Description |
| :------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Dataset** | The first stage of any workflow - the **dataset** stage can be linked to the dataset which contains the files that will enter the workflow. |
| **Annotate** | A simple human annotation stage, the **annotate** stage should be followed by at least 1 review stage. |
| **Review** | Similar in functionality to the **annotate** stage, the key difference with a **review** stage is the ability to send files to two workflow stages: One where files will be sent when approved, and one where files will be sent when rejected. |
| **Model** | A stage that can be injected into a workflow to create a model-assisted workflow. When adding a **model** stage, any trained model in the **Neural Networks** page can be used to create annotations. Simply match the classes that the model was trained on to the corresponding classes in the dataset. |
| **Consensus** | Consensus stages enable the quality assurance of annotations by measuring the extent of agreement between different **annotators** (and/or **models**) and allowing the selection of the best annotation when there is a disagreement. |
| **Sampling** | The Sampling Stage allows you to send a percentage of randomly selected images through different paths in your workflow. |
| **Webhook** | Webhooks allow you to relay information in real-time, allowing for higher efficiency and automation. |
| **Archive** | The Archive stage allows you to Archive images that you don't want to send to **complete**. |
| **Complete** | The final stage of a workflow. Once in a **complete** stage, files can’t be edited unless they are moved to a previous stage. |
Once created, assignment rules for the workflow can be set in the **Settings** page of the dataset.
Next up we'll take a look at how annotators can self-assign batches, as well as how you can manually assign tasks from your dataset.