The useful data is the data you cannot send out

The records that would benefit most from AI are usually the ones a policy, a regulator or a contract prevents you from putting into a third-party API: operational telemetry, case files, citizen records, drawings, incident history.

Sovereign AI resolves that by moving the model to the data. Open-weight models run on your own GPUs, inside your perimeter, tuned on your corpus. Nothing leaves.

Capabilities

  • Open-weight models hosted in your own data centre
  • No data egress to third-party APIs
  • Fine-tuning and retrieval over your own corpus
  • GPU infrastructure sizing, supply and commissioning
  • Edge inference for real-time decisions
  • Role-based access control
  • Full prompt and response audit logging
  • Aligned to Indian data residency requirements
  • Integrates with DCIM, VMS and Helpdesk data

What it involves

Infrastructure sizing

GPU selection, power and cooling for dense racks, and storage throughput for training and retrieval. This is data centre engineering before it is an AI project, which is why it sits alongside our other work.

Model selection

Choosing open-weight models that fit the task and the hardware budget, rather than defaulting to the largest available. Most operational workloads do not need a frontier model.

Domain tuning

Fine-tuning and retrieval over your own documents, tickets, drawings and telemetry, so answers are grounded in your estate rather than in general knowledge.

Edge inference

Smaller models at the edge where a decision has to be made in milliseconds and a round trip to a central cluster is too slow.

Governance

Role-based access, prompt and response logging, and a complete audit trail. Who asked what, what the model returned, and on which version.

Data residency

Everything stays on infrastructure inside India, which removes the residency question from procurement rather than arguing it.

Questions about Sovereign AI

Is a self-hosted model as capable as a commercial API?

For general open-ended reasoning, the largest commercial models are still ahead. For a bounded operational task grounded in your own documents and telemetry, a well-tuned open-weight model is usually indistinguishable in quality and considerably cheaper to run at volume.

What hardware does this need?

It depends entirely on model size and concurrency. A departmental deployment can run on a single GPU server; a heavily used estate-wide system needs a small cluster with the power and cooling to match. We size it as part of the design, not after.

Can you use our existing GPU hardware?

Yes, where the memory and interconnect suit the model. We audit what you have first and tell you plainly whether it is adequate before proposing anything new.

How do we prove to an auditor that no data left?

Through network policy at the perimeter and the system's own audit log, which records every request and response with its user, timestamp and model version. The architecture is designed to be evidenced, not just asserted.

Tell us what you are building

Send a brief, a floor plan or a single-line diagram. We reply with an approach, a rough capacity envelope and an honest view of what it takes to run.