Custom private AI, built for your business
We design the system around how your business runs and build it inside the environment you control, so you keep the data and the keys. We become part of your team to build it. The AI Audit finds and costs the first one; building it is a separate decision.
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.
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 own hardware, 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. We work inside that account, so 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.
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.
Most of this work starts with an AI Audit, which settles feasibility and specifies the pilot. Deploying it is a separate decision.
A deployment is agreed on its own scope, or inside an Embedded AI Lead engagement. Where you want the rules settled first, an AI Charter can be agreed alongside the audit.
For an international oil and gas service provider we wrote the on-premises specification precisely enough that their own team took the build forward. Where you would rather we stayed involved after handover, we can.
Our success stories
International Oil and Gas Service Provider Energy · AI audit AI audit of pipeline gap analysis A two-stage on-premises pilot specified with a go/no-go gate between the stages. 
Confidential UK fintech Financial services · Production Agentic controls assurance platform Cutting audit testing from hours to minutes per control, every control tested where teams sampled before. “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. Discuss your AI plans
FAQ
How do you build a private AI system?
Most of this work starts with an AI Audit, which tests whether a private deployment is genuinely required and scopes what would be built. A pilot then runs inside your own boundary before anything goes to production.
What does a private deployment cost?
It depends on where the system runs and what it has to do, so we scope and agree it separately. The AI Audit is £10,000 excluding VAT at standard scope. A deployment is then either its own agreed scope, or work we implement inside an Embedded AI Lead allocation where it fits.
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?
The system runs inside the controlled environment you set for that deployment. Access to it is logged. On a hybrid deployment, only what you have approved crosses the boundary.
Not ready to talk? The free AI readiness check scores where you stand in about five minutes.
What could your business do with AI?
Find out with experts who become part of your team. We uncover opportunities, get ideas working, and help your people build on the results.
We reply within one working day.