Solutions · Apps + AI Development

Your Business Apps and Your AI Workbench, on One Private Platform.

Run your business on open-source apps, then build AI on top of them — with notebooks, version control, CI/CD, and safe test environments already connected to your own data.

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Who This Is For

You Have Ideas for AI, and Data You Won’t Hand Over

You want the savings and privacy of open-source business apps. You also have developers, analysts, or a technical founder who want to build with AI: a support-triage model, a sales assistant that knows your CRM, automations that save the team hours a week.

The hard part usually isn’t the model. It’s getting clean, current access to your own data without copying it into a third-party cloud, and having a safe way to test what you build. This path gives you both: the Software Library for running the business, and Data & Controls for building on it.

Your AI Toolkit

Everything You Need to Build, Already Wired to Your Data

  • MCP in every appClaude, ChatGPT, or a private local model can read and act inside your apps through scoped connections — no custom integration code.
  • The Organizational BrainA live, searchable index of your files, forum threads, and support tickets, so anything you build starts with full context.
  • Jupyter notebooksA safe place to explore your own data and prototype ideas with local AI.
  • Gitea + CI/CDEvery change, human- or AI-written, is tracked, tested, and deployed automatically.
  • Digital twin environmentsAn exact, safe copy of your live systems for testing risky changes with zero danger to real data.
  • Grafana & PrometheusReal-time monitoring that catches problems, including automations stuck in a loop, before they get expensive.
  • Federated learningTrain models locally on your own data. Only encrypted learnings are ever shared, and only if you choose to take part.

How Work Flows

From Idea to Shipped, Without Your Data Leaving Home

  1. Explore

    001

    Open a Jupyter notebook and ask questions of your real data through the Organizational Brain.

  2. Build

    002

    Turn the prototype into code. Commit it to Gitea, where every change is tracked.

  3. Test

    003

    CI/CD runs it against a digital twin of your systems, not the real thing.

  4. Ship and watch

    004

    Deploy automatically, then monitor it in Grafana so you know it’s behaving.

In Practice

A Support Assistant, Built In-House

A developer prototypes a reply assistant in a notebook, using past FreeScout tickets and the policy pages in Discourse. It goes into Gitea, gets tested against a digital twin, and ships. Now every new ticket arrives with a drafted answer and links to the right documents — and no ticket ever left the company’s own install.

Where Agents Run

Run What You Build Wherever You Like — Until You Want It Here

On this path, the AI you build runs where you choose: your own machines, your existing infrastructure, or your model provider of choice. When you’re ready to run a whole team of agents next to your data, add the Agent Sandbox to the same install.
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Questions

Common Questions About This Path

Which AI models can I use?

The built-in connectors work with Claude, ChatGPT, or a private local model. You choose, and you can change your mind.

Will my data be used to train anyone's model?

No. Your install is single-tenant and we can’t see your data once you change your admin password. Federated learning is opt-in, and even then only encrypted model updates leave your servers — never raw data.

Do I need a data team to get value from this?

No, but it helps to have at least one person comfortable writing code or working in notebooks. If you’d rather not build, the Open-Source Apps path may fit better for now.

Do I need the Agent Sandbox?

Not to build. You need it when you want to run many agents at once on Federated’s infrastructure, next to your data.