> For the complete documentation index, see [llms.txt](https://jtheta-ai.gitbook.io/docs.jtheta.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://jtheta-ai.gitbook.io/docs.jtheta.ai/create-workspace.md).

# Create Workspace

After logging in to JTheta.ai, you will arrive at the Workspace Selection screen.\
A workspace acts as your team environment where:

* Datasets are stored
* Projects are created
* Roles & permissions are managed

You can choose from:

* Existing Workspaces (e.g., testing123, lidar\_project01)
* Create Workspace (to start a new team environment)

User permissions (Admin / Annotator / Reviewer) are assigned at the workspace level.

<figure><img src="/files/LXaUHcZqrA7cdRNNoFwt" alt=""><figcaption></figcaption></figure>

### Creating and Managing Workspaces

Within the **Project Dashboard**, JTheta.ai allows users to create and manage multiple workspaces. Workspaces help organize projects, datasets, and users for different teams, clients, or use cases.

#### Accessing the Workspace Menu

On the **top-right corner of the dashboard**, you will see the **Workspace selector** displaying the current workspace name (for example: `Workspace: t1234`).

Clicking this dropdown opens the **Workspace menu**, where you can:

* View the **currently active workspace**
* Switch between **existing workspaces**
* Create a **new workspace**

#### Creating a New Workspace

You can create a new workspace directly from the **Project Dashboard**.

To create a workspace:

1. Click the **Workspace selector** in the top-right corner of the dashboard.
2. Select **Add New Workspace** from the dropdown menu.
3. Enter the **workspace name**.
4. Confirm to create the workspace.

Once created, the new workspace will become available in the **workspace list**, and you can switch to it anytime.

<figure><img src="/files/A2Pfr7Yk8gyC7b2oK10v" alt=""><figcaption></figcaption></figure>

#### Why Use Multiple Workspaces

Workspaces help you keep projects organized by separating them based on:

* **Teams**
* **Clients**
* **Departments**
* **Different AI projects**

Each workspace can contain its own:

* Projects
* Datasets
* Users and permissions

This allows teams to manage annotation workflows efficiently without mixing unrelated projects.

#### Example Use Case

For example, an organization might create separate workspaces for:

* **Autonomous Driving Dataset Projects**
* **Agricultural Robotics LiDAR Annotation**
* **Research or Experimental Datasets**

This structure keeps project environments **clean, organized, and easier to manage**.