Private AI
On infrastructure you control, in a jurisdiction you choose, with no client data leaving your environment.
What this is for
Forget AI as an abstract idea. This is about the specific, repetitive work that fills your team's week — the work they'd hand to a public AI tool in a heartbeat, if only they were allowed to. Here's what changes when they finally can.
A 1,400-page statute, a 300-page contract, a patent filing you can't afford to skim — your own AI has read all of it. Ask it the question buried on page 900 and it answers in seconds, with the source. The work that used to mean an afternoon of searching becomes a sentence and a reply.
Voice mode, the part nobody expects. Dictate the task while your hands are busy with something else — summarise this, draft that, pull the figures from this report — and let it work while you carry on. It understands plain speech, accents and all. The fastest interface is the one where you don't type at all.
Hand it the workbook and ask the question — the calculation, the reconciliation, the figure three tabs deep — and it works it out, showing how it got there. The analysis that used to mean an hour of formulas takes a sentence, and not one number leaves your environment.
And every one of these happens inside your own system. The document you'd never paste into ChatGPT, the figures you can't expose, the matter that's privileged — all of it stays in an environment only your organisation can reach. The point isn't that AI can do these things. It's that you finally can — on the work that mattered too much to risk.
Independent research puts the time saved at around 7.5 hours per person per week. The number lands differently once you can see where it comes from.
Most organisations are further into this than they think — staff reach for public AI because it helps, long before anyone's approved it. If you want a clear read on your own position before you go further, the assessment takes a few minutes.
See where you stand →Two surfaces, one infrastructure
Most AI work is conversational: ask, draft, summarise, refine. Some work is structured: review a stack of documents for one specific thing, work across a set of files that all belong to the same case, draft from your own approved templates. Both happen on the same infrastructure, behind the same sign-on, inside the same boundary.
The everyday workspace
The day-to-day surface. Familiar from the first second — anyone who's used a public chatbot will recognise it instantly. The difference is entirely behind the screen.
The structured workspace
For the work the everyday surface isn't built for: reviewing a stack of documents for one specific thing, working across a set of files that all belong together, drafting from your own approved templates.
Two surfaces, one environment — the same access boundary, the same audit trail, on the same infrastructure your organisation contracts for directly.
How it's deployed
The same system runs in two configurations — the right one depends on where your data needs to live and how your organisation wants to hold it.
Deployed on dedicated bare-metal infrastructure in a UK/EEA data centre — your own server, not a shared platform. You get full logical isolation, encrypted persistent storage, and a system that runs under your contractual control. No other client shares your compute. Suited to organisations that want sovereign infrastructure without managing physical hardware.
The system runs entirely on hardware inside your own walls — your office, your server room, your building. No internet connection required. Nothing leaves the perimeter by design. Suited to organisations where the physical location of data is a non-negotiable, or where air-gap is the only acceptable architecture.
In both cases: the same model, the same surfaces, the same access controls, the same audit trail. The difference is where the hardware sits and who holds the keys to the room.
Configured around you
A public AI tool is the same for everyone who uses it. Yours isn't. Every deployment is configured for the organisation it serves: the way it's instructed, the skills it can call on, the policies that apply, the knowledge base it draws on — all shaped around how your people actually work. The model itself stays exactly as it is — we never train it on your data — but everything around it is built for you.
That includes the rules. Public AI wraps your work in a vendor's filters, tuned to their risk appetite, and refuses perfectly legitimate tasks that happen to trip one. Here, the only rules that apply are the ones that make sense for your work — set by you, for your context, not a lowest-common-denominator default written for millions of strangers.
How it actually works underneath
We've kept this page about what the system does for your team. If you want the mechanics — the data flow, the retrieval (RAG) that grounds answers in your own documents, the model, and why there's no path out of the perimeter — it's all laid out, with a diagram, on the architecture page.
See the architecture →Who this is for
The infrastructure is the same for everyone. What changes is the work each sector hands to it first. Here's where private AI earns its place across the most strictly regulated fields — and the kind of organisation that doesn't appear on any sector list but knows exactly why it's here.
Reviewing a stack of documents for the one thing that matters. Point it at the bundle, ask the question — which of these mention the indemnity clause, where does this term appear, what changed between these two drafts — and get the answer with the exact source, every time. The afternoon of reading becomes a question and a reply.
Getting through the documents nobody has time to read closely. Hand it a report, a filing, a set of statements and ask what you actually need to know — where the figures don't agree, what this clause commits you to, what stands out. The reading that used to eat the morning takes minutes.
Clearing the correspondence that piles up. Summarise an incoming referral, draft the standard letter, pull the history out of a long record — the administrative weight that pulls staff away from patients, handled in a fraction of the time. None of it touches clinical decisions; all of it touches the paperwork around them.
Finding the answer buried across years of documents. Trust deeds, estate plans, statements, correspondence — ask where something is, what a provision says, what the position is, and let it search the whole archive in seconds. The work that meant opening twenty files becomes one question.
Asking the ledger and the data room directly. Point it at the books, the bundle, the data room and ask the question — what's in here, where's the entry, what supports this figure — with the source returned alongside the answer. The hours of hunting through documents become a conversation.
The work that fills the day in any office that handles things it can't expose. Caseworkers writing up notes while it's fresh. Officers answering a backlog of correspondence and complaints. Teams pulling a board report together out of scattered documents. Housing associations, charities, councils, practices of every kind — the routine is the same, and so is the reason it can't go to a public chatbot.
Different sectors, different first task — one thing in common. Every one of these is work you'd hand to a public AI tool in a second, if the data inside it weren't the exact thing you're not allowed to expose. That's the whole point of doing it on a system that's yours: the work gets done, and nothing leaves the room.
Trust, in this market, is more usefully described by what a provider will not do than by what it claims to guarantee.
We do not touch your data.
Your documents, your prompts, your conversations, your outputs — we have no access to any of it. We build the system and keep it running; what happens inside it is yours alone.
We do not train any model on your data.
Nothing your people put into the system is used to train, tune, or improve anything. The model is static. Your work is not a training input — not ours, not anyone's.
We do not hold a key to your infrastructure.
No persistent access, no always-on connection, no back-channel. When maintenance is needed, your own IT lead opens the door, and it closes when the work is done.
We do not run a shared platform.
Your environment runs on dedicated compute — your own CPU and GPU, not a slice of someone else's. No multi-tenant SaaS where data could co-mingle.
The simplest way to put it: your data sits inside this system exactly as safely as it sits in the cloud storage your organisation already trusts. Encrypted, access-controlled, and going nowhere — except that here, no outside vendor can read it, and no model learns from it. It's the security posture you already rely on, with the one gap that public AI opens closed off entirely.
Deployment Methodology
From unsecured public AI to UK data sovereignty in 14 working days.
Sovereignty
The architecture, the live compliance posture, and the regulatory framework are all published in full. your technical and compliance people can evaluate every claim before any conversation with us.
Bare-metal deployment topology, zero-egress perimeter, and the Zero Standing Privileges support model.
Open →Compliance framework matrix mapped to UK GDPR, DUAA 2025 and sector rules, plus NDA-gated security artefacts.
Open →UK GDPR, DUAA 2025, ICO enforcement, CLOUD Act, SRA and Law Society of Scotland obligations.
Open →Once you've had a look at the published materials, a conversation is where it gets specific to you — your work, your data, and what a deployment would actually look like for your organisation.
Let's talk it through