Private and on-prem LLM deployment
Open-weight models served on your hardware or your private cloud, sized against the workload rather than the marketing.
AI Platform and Operations
When the data is contracts, HR records or customer information, the blocker is rarely capability. It is approval. We deploy models inside your network, with the access control and logging that makes the answer to security a yes.
Talk to usThe programme stalled on approval, not on capability.
The use case was agreed and the pilot worked. Then legal and security looked at what would be sent to a public API and said no. Nothing was wrong with the model. The data simply could not leave.
The DPDP Act sharpens this. Where personal data is involved, where it physically sits and who can reach it stops being an architecture preference and becomes a compliance question you have to answer.
DPDP enforcement and cross-border transfer restrictions are moving from policy discussion to dated obligation, and significant data fiduciaries face the shortest runway.
What we do.
AI security
Access control, model risk, prompt and output handling, and the logging you will need when someone asks what the system saw.
Data engineering for AI
The pipelines, chunking, embeddings and refresh that decide whether retrieval finds the right document or the nearly-right one.
How it works.
Five steps. Nothing hidden in the middle.
- Your infrastructureOn-premise or your own private cloud tenancy. You keep the keys.
- The model runs thereAn open-weight model sized to the task, served behind your network boundary.
- Access control and loggingWho asked, what was retrieved, what was returned. Auditable after the fact.
- The applicationThe assistant or the extraction pipeline sits on top and calls the model locally.
- Nothing leavesNo prompt, no document and no output crosses the boundary unless you decide it should.
What it does not do.
- Running privately does not make an open-weight model as capable as the largest frontier models. On some tasks you will trade a few points of quality for control, and we will tell you which tasks those are before you commit.
- It does not remove your obligations under the DPDP Act. It makes them satisfiable.
- It is not free. Hardware, capacity planning and someone to run it are real costs, and we will model them against the public API bill you are comparing against.
What we measure.
Where the data physically sitsNamed, documented, and checkable. The first question your DPO will ask.
p95 latencyMeasured against the public API baseline it replaces, on your workload.
Cost per thousand requestsAgainst that same baseline, including the infrastructure, not just the inference.
Task accuracy against a frontier baselineOn your own evaluation set, so the trade-off is a number rather than a feeling.
Who buys this
Tell us where the work is.
One conversation, no pressure. We will tell you what we would build, and what we would not.
Talk to us