Custom private AI, built for your business.
We design the system around how your business runs and build it inside your own infrastructure, so your data and your keys stay with you.
When a private deployment is the right answer.
Most AI work does not need this, and we'll tell you if yours doesn't.
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The data cannot leave the country, the building, or your control
By law, or by the contracts you have signed.
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A regulator or client contract forbids third-party models
Content cannot be sent to an API you do not run.
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“Who can see this?” needs an answer at every moment
Every access has to be recorded and provable.
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You already run your own infrastructure
Building on it beats replacing it.
If your data can sit in a UK region under the right contracts
A governed cloud deployment is usually quicker and cheaper, and we’ll tell you so.
What we build inside your boundary.
Private retrieval over your own content
The model reads only the sources you point it at, inside your network.
An open-weight model on your own hardware
The model runs on GPUs you own, so capacity is a fixed cost you control.
A platform you own
Orchestrated on your own infrastructure; nothing runs anywhere you cannot reach.
A person in the review path
Outputs pass through review before they count.
An immutable audit trail
Every run logged, so compliance can retrace what happened.
Where the data lives.
We choose between them with you, against the constraints you actually work under.
On-premises
Your hardware and your keys: the work stays inside your own network.
The model and the retrieval index run on machines you already own, so no request leaves your network and your existing access controls still apply.
Your cloud
A single-tenant enclave inside your own tenancy.
You choose the region, and your team keeps the admin rights and the keys. We work inside that account, and nothing is copied out of it.
Hybrid
The sensitive core inside; the rest where it already lives.
The sensitive workloads run on your own infrastructure, and what passes between the two is limited to what you have approved.
In each of these, your prompts, outputs and weights stay inside the boundary you set for that deployment, and every access is logged and provable.
How the work runs.
Feasibility first
We assess whether private deployment is genuinely required, and say plainly if it is not.
Pilot inside your boundary
We run a scoped pilot on your own infrastructure and benchmark it against your current baseline.
Production and handover
We harden it, document it, and hand it to your team with the training to run it. You hold the keys.
Most of this work starts inside an AI Audit and Transformation, which settles feasibility and specifies the pilot. Where the pilot earns its place, we take it through to production under the same Charter. For an International Oil and Gas Service Provider we wrote the on-premises specification precisely enough that the client’s own team took the build forward without us in the room.
Our success stories.
“The study was focussed and really gave us a validation of our concept, a technical roadmap and prioritisation of the developments.”
Tell us what has to stay in-house. Get in touch
FAQ
How do you build a private AI system?
It starts with the AI Audit and Transformation, which tests whether a private deployment is genuinely required and scopes what would be built, followed by a pilot inside your own boundary.
What does a private deployment cost?
We scope it after the audit, because the number depends on where the system runs and what it has to do. The audit itself is a fixed fee from £10,000, and everything that follows is quoted from what it finds.
Is private AI worse than the big APIs?
Open-weight models are behind the frontier but close enough for most bounded tasks; the pilot benchmarks yours against your own baseline, so you know before committing.
Do we need our own GPUs?
Not necessarily. “Private” means inside your boundary: your hardware, your tenancy, or an air-gapped rack, whichever your constraints demand.
Can it still use our documents?
Yes: private retrieval over your own content is usually the core of the build.
What about updates?
Model and platform upgrades are part of handover planning; your team learns to run them, or we stay on at a lighter touch.
Who else sees our data?
Nobody outside the boundary you set. On a hybrid deployment only what you have approved crosses it, and every access is logged and provable.
Not ready to talk? The free AI readiness check scores where you stand in about five minutes.
Find your first workflow.
We start with a conversation, audit where AI actually pays back, and leave you with a costed plan for what to build first.
We reply within one working day.