Private knowledge
Ask approved internal documents while sensitive data stays off cloud AI APIs.
Sovereign AI Platform beta
Local AI workspace for teams that cannot use cloud AI.
Private knowledge, workflow automation, model/runtime controls, operator diagnostics, offline updates, and benchmark proof inside the customer boundary.
- Current proof
- Lab baseline + documented Docker Compose deployment.
- Next proof
- Sanitized walkthrough media + benchmark harness evidence.
Customer-controlled boundary
Workspace
Chat + documents + workflows
01
Private knowledge
02
Workflow automation
03
Model/runtime controls
04
Operator diagnostics
Beta capability stack
The product layer above local models.
The wedge is not “local chat.” It is a deployable workspace that business users can use and IT can inspect before sensitive workflows reach a team.
Workflow automation
Turn classify, extract, route, summarize, and generate steps into repeatable local workflows.
Model/runtime controls
Show active models, pinned versions, rollback paths, and runtime state to operators.
Operator diagnostics
Expose health, logs, offline update status, and exportable diagnostic bundles.
Benchmark proof
Compare local output quality and throughput against fixed non-sensitive eval inputs.
Air-gapped deployment
Run on customer-controlled infrastructure, including environments with no outbound AI calls.
Proof boundary
Current evidence is narrow on purpose.
The public beta should be believable. Current proof is the running lab prototype baseline and documented single-node Docker Compose deployment. The next proof is product-layer evidence from a sanitized Alpha 1 flow.
Now
Lab prototype baseline
Ollama + OpenWebUI baseline, local deployment documentation, and no public customer data.
Alpha 1
Product-layer proof
Private knowledge retrieval, workflow automation, model controls, diagnostics, and benchmark harness.
Roadmap
Expanded local surfaces
Agents, voice, observability, extension packaging, and richer media workflows after the core proof holds.
Client workflow
From blocked AI pilot to controlled local operation.
- Deploy locally Single-node first, with air-gapped operation once images and models are local.
- Connect approved knowledge Use sanitized or approved internal material without sending it to cloud AI APIs.
- Automate repeatable work Summarize, extract, classify, route, and generate outputs through measurable workflows.
- Prove and operate Track benchmark outputs, active model/runtime state, rollback paths, and diagnostics.