> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tracebloc.io/llms.txt
> Use this file to discover all available pages before exploring further.

# tracebloc

> Build better AI together. Without moving data.

<Frame>
  <img src="https://mintcdn.com/tracebloc/EVB0IGskWPJcKhjR/images/traceblocfederatedlearning.png?fit=max&auto=format&n=EVB0IGskWPJcKhjR&q=85&s=25bcf5a575b409f8d337be15cefde5cc" alt="Traceblocfederatedlearning" width="1320" height="631" data-path="images/traceblocfederatedlearning.png" />
</Frame>

tracebloc is **your collaborative AI workspace** you deploy on your own infrastructure. Invite friends, peers, researchers, partners, vendors — anyone — to train, fine-tune, and benchmark models on your private data. Your data never moves.

<CardGroup cols={3}>
  <Card title="Quick Setup" icon="rocket" href="/environment-setup/setup-guide">
    Zero to a running workspace in under 10 minutes.
  </Card>

  <Card title="Create a Use Case" icon="plus" href="/create-use-case/prerequisites">
    Bring your data, set metrics, invite contributors.
  </Card>

  <Card title="Join a Use Case" icon="users" href="/join-use-case/join-use-case">
    Train and submit models on someone else's data.
  </Card>
</CardGroup>

<br />

## What is tracebloc?

tracebloc is a **collaborative AI workspace** you deploy on your own infrastructure. Anyone you invite — researchers, partners, vendors, startups — can train, fine-tune, and benchmark models on your data. Your data never moves. Compliance is solved by architecture.

**Who is it for?** Anyone who asks "which model works best on MY data?" and wants external input without the nightmare of NDAs, data-sharing agreements, and security reviews.

**What makes it different?**

* <Icon icon="server" /> **Your infrastructure:** runs on your Mac, Linux box, GPU server, or any Kubernetes cluster
* <Icon icon="lock" /> **Your data:** stays inside your network. Contributors never see raw records.
* <Icon icon="user-plus" /> **Invite anyone:** whitelist contributors by email. They see EDA and metadata. Never raw data.
* <Icon icon="ranking-star" /> **One leaderboard:** every submission benchmarked under identical conditions. Ship the winner.
* <Icon icon="shield-check" /> **Compliance by architecture:** data never moves. Sign off once on the architecture, not once per partner.

<br />

## How It Works

<Steps>
  <Step title="Create your AI workspace">
    One script. Your Mac, a Linux workstation, an NVIDIA GPU box, or any Kubernetes cluster.

    <CodeGroup>
      ```shellscript Mac / Linux theme={null}
      bash <(curl -fsSL https://tracebloc.io/i.sh)
      ```

      ```powershell Windows theme={null}
      irm https://tracebloc.io/i.ps1 | iex
      ```
    </CodeGroup>
  </Step>

  <Step title="Bring your data">
    Validate and stage your datasets on your cluster. Metadata syncs to the web app — raw data stays put.
  </Step>

  <Step title="Define a use case">
    Pick from your prepared datasets, set evaluation metrics. Use a template or build your own.
  </Step>

  <Step title="Invite anyone">
    Whitelist contributors by email. Assign compute budgets. They never see raw data.
  </Step>

  <Step title="Build together">
    Contributors submit models and train inside your environment. PyTorch, TensorFlow, custom containers.
  </Step>

  <Step title="Compare and decide">
    Every submission benchmarked under identical conditions. One leaderboard. Ship the winner.
  </Step>
</Steps>

<br />

## When Should You Use tracebloc

<CardGroup cols={2}>
  <Card title="Vendor Benchmarking" icon="ranking-star">
    5 vendors claim they have the best model. Invite all five to submit. One leaderboard. One week. Decisions based on measured performance — not claims.
  </Card>

  <Card title="Cross-Org Research" icon="building">
    Research teams want to improve your model — but the data is regulated. They train on your data without ever seeing it.
  </Card>

  <Card title="Internal Competitions" icon="trophy">
    Teams across offices and time zones compete on the same dataset. Same metrics. Same holdout set. Best model wins.
  </Card>

  <Card title="Pre-Production Validation" icon="check-double">
    Before shipping a model, benchmark it against 4 alternatives on real production data. Know you're shipping the best option.
  </Card>
</CardGroup>

<br />

## Next Steps

<CardGroup cols={2}>
  <Card title="Create a Use Case" icon="plus" href="/create-use-case/prerequisites">
    Pick from prepared datasets, define metrics, invite contributors.
  </Card>

  <Card title="Join a Use Case" icon="users" href="/join-use-case/join-use-case">
    Train and submit models on a workspace owner's infrastructure.
  </Card>

  <Card title="Explore Workspaces" icon="compass" href="https://ai.tracebloc.io/explore">
    See what builders are creating on tracebloc.
  </Card>

  <Card title="Advanced Setup" icon="gear" href="/environment-setup/setup-guide">
    GPU configuration, Helm deployment, EKS.
  </Card>
</CardGroup>