AI capability without your data leaving your control.
Models deployed inside your tenancy or on your own hardware, for the work where confidentiality, regulation or data residency rules out a hosted service.
When hosted AI is not an option
Some material cannot be sent to a third party — because of regulation, because of a contract with your own client, or because it is the thing your business is actually built on. That constraint is usually discovered after a pilot has already been built on a hosted service.
- Legal or compliance stopped an AI initiative after the technical work had started.
- Data residency requirements rule out the regions your preferred provider operates in.
- Client contracts prohibit disclosure to sub-processors you would have to add.
- The material with the most value is precisely the material you cannot send anywhere.
What we build
The full stack inside your boundary: inference, retrieval, orchestration and monitoring, sized honestly against the workload rather than against a benchmark.
- Model serving on your infrastructure or inside your cloud tenancy
- Hardware and capacity sizing against your real concurrency, not a headline figure
- Retrieval and vector storage held within the same boundary
- Access control, request logging and retention aligned to your policy
- A model update path, so the deployment does not freeze at its launch capability
- An honest capability comparison against the hosted alternative you are giving up
How it runs
The constraint is established first, because it determines everything downstream.
- 01Establish the real constraint
Which data is genuinely restricted, by what rule, and whether it applies to all of the workload or part of it.
- 02Size the workload
Concurrency, latency tolerance and context length determine hardware. Guessing here is how private deployments become expensive.
- 03Deploy the stack
Inference, retrieval and orchestration inside the boundary, with the same operational standards as any production service.
- 04Control and log
Authentication, authorisation, request logging and retention configured to your policy rather than to a default.
- 05Maintain capability
A defined path for adopting newer models, so the deployment improves rather than ossifying.
What changes once it is running
What holding the stack inside your boundary gives and costs.
Restricted material becomes usable
The data you could not send anywhere becomes available to the same capabilities as everything else.
The compliance answer is simple
Nothing leaves. That is a much shorter conversation with a regulator or a client than a sub-processor argument.
Costs become predictable
Capacity is a known infrastructure cost rather than a per-token bill that scales with enthusiasm.
The trade-off is explicit
You will give up some frontier capability. Knowing exactly how much, on your workload, is part of what we deliver.
How an engagement is shaped
Feasibility before procurement. Hardware bought against a guess is expensive to unwind.
Constraint and feasibility
Two to three weeks confirming what is genuinely restricted, benchmarking candidate models on your material, and sizing the infrastructure honestly.
Deploy
The stack built and integrated, with the operational and security standards you apply to any production system.
Operate and refresh
Optional managed operation, including model refresh as capable open-weight models are released.
Common questions
The things buyers ask before they commit. If yours is not here, it is a good first question for the assessment.
- Will a self-hosted model be as good?
- On many enterprise tasks, close enough that the difference does not matter. On the hardest reasoning work, no. We benchmark on your actual material during feasibility so this is a measured decision rather than a hopeful one.
- What hardware does this need?
- It depends almost entirely on concurrency and context length. That is exactly what the feasibility phase sizes, and it is the reason we do it before you buy anything.
- Can we run a mix — private for sensitive work, hosted for the rest?
- Yes, and it is often the right answer. Routing by data classification gives you frontier capability where it is permitted and containment where it is not.
Tell us which data cannot leave.
The constraint determines the architecture. Start there and the rest of the design follows.