Now deploying private AI for healthcare, legal, and finance teams across the EU
For teams blocked from using cloud AI
We deploy private AI systems on your own hardware or a dedicated machine only you can reach. No third-party API, no data egress, no vendor holding your documents.
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model: online · host: your-rack-01
Typical pilot to production
Bytes leaving your network
Infrastructure you control
The difference
AI runs inside your network.
Your data never leaves.
Cloud AI
Sensitive data sent to third-party APIs
With SRI
AI deployed on infrastructure you control
Built for teams in
The wall everyone hits
The blocker is almost never the model. It is that using it means uploading the exact documents you are contractually and legally required to keep in house.
Client files, patient records, and contracts cannot be processed by a third party without a chain of agreements nobody wants to sign.
A cloud API means an outbound path for your most sensitive data, with retention and training terms you do not control.
Shadow AI on personal accounts is the outcome of saying no without offering an alternative. That is the real risk you are carrying today.
What we actually do
You buy a machine, or we rent you a dedicated one. We install the model, wire it to your documents, and manage it over an SSH channel you control. Your team gets a search bar and a chat box. Nothing leaves your network.
FIG.1
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One call, then a spec instead of a slide deck.
We look at the work your team actually does and which parts are blocked today. You get back a shortlist of use cases worth building, the model that fits them, and the exact hardware it needs, priced.
FIG.2
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Your hardware, or a dedicated GPU we rent for you.
Buy a box and keep it in your own rack, or let us put a single-tenant GPU in an EU data centre in your name. Either way we install the model, the document search, and the internal API, and we manage it remotely over SSH.
FIG.3
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No third-party API anywhere in the request path.
Inference happens on your machine. There is no call out to a model provider, so there is no processor agreement, no transfer, and no retention question. Every query is logged inside your own perimeter.
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Monitoring, patches, and model upgrades on one monthly fee.
AI systems are not set and forget. We watch performance, apply updates, and swap in better open models as they ship. When something breaks, the alert goes to us and not to your service desk.
The decision on one page
This is the table your security officer will build anyway. Here it is up front.
| Criterion | Cloud AI | SRI |
|---|---|---|
| Where inference runs | Vendor infrastructure, region of their choosing | Hardware you own or rent in your own name |
| Who holds your documents | A third party, under their retention policy | You. The index never leaves your disk |
| Processor agreement needed | Yes, plus transfer assessment | No third-party processor in the request path |
| Works with no internet | No | Yes, on-premise and air-gapped |
| Audit log location | Vendor console, exportable at best | Your own log stack, queryable like any other service |
| Model choice | Whatever the vendor ships and deprecates | Open weights you pin, upgrade, or roll back |
| Cost shape | Per token, unbounded, scales with success | Fixed hardware or fixed monthly, flat under load |
| If the relationship ends | Access stops, data export on their terms | The system keeps running. You already own it |
How it works
A fixed, boring sequence. You always know what happens next and what your security team needs to look at.
We look at the work your team actually does and which parts are blocked today. If local AI is the wrong answer for you, we say so on this call.
Model selection, hardware sizing, integration points, and a written data-flow diagram your security team can review and sign off.
We deploy on your hardware or a dedicated machine, wire it to a real document set, and put it in front of a small group of real users.
Access control, audit logging, monitoring, runbooks, and training. Your IT team can operate it. We stay on for support if you want us to.
Deployment options
The right one depends on how strict your data rules are and whether you would rather own hardware or rent it.
A machine in your own rack or office.
Best for: Strictest data rules, offline sites, long horizons
Dedicated GPU hosting in an EU data centre.
Best for: Fast start, heavier models, no hardware purchase
No network path in or out. At all.
Best for: Classified, clinical, or regulator-facing environments
Governance
We are not your compliance officer. We build the architecture that makes these answers short.
You do, on your own infrastructure. No third-party processor and no international transfer to assess.
Model version, weights, prompts, and retrieval sources are all pinned and versioned in your repository.
It never leaves. Air-gapped deployment removes the network path entirely, not just the policy.
Open weights, open components, documented configuration, and your team holds the runbooks.
What it actually gets you
Ask a question in plain language and get an answer with citations from your contracts, policies, and archives.
First-pass drafting, summarising, and clause comparison on documents that were never allowed near a cloud tool.
Classify and route tickets, mail, and forms automatically, with a human check where it matters.
Every prompt and answer logged inside your network, attributable to a user, retained on your terms.
What it costs
The reason cloud AI budgets explode is that the bill scales with adoption. This does not. You pay to have it built, you pay for the machine, and you pay a flat fee to keep it running.
Workflow review, model and hardware sizing, integration plan, and the written data flow your security team signs off on. Credited against the deployment if you go ahead.
Fixed fee, one off
Everything installed, wired to your documents and systems, access control and audit logging in place, runbooks handed to your IT team. Quoted before you commit.
Fixed fee, one off
Buy the hardware and own it outright, or rent the same shape from us as a single-tenant GPU in an EU data centre. We tell you honestly which is cheaper for your volume.
Your capex, or our monthly
Monitoring, patching, model upgrades, and capacity planning. Flat under load, because the cost of an extra thousand queries on hardware you already own is zero.
Flat monthly
Both fixed fees are quoted after the discovery call and before you commit to anything. There is no per-seat licence and nothing that expires.
Who is behind it
We are early as a private AI company and we are not going to pretend otherwise. What is not early is the pair building it. Both names below are public, and so is the work.
Co-founder, Lead Engineer
Founder of tegra.io. Directed engineering at Satoshi, senior roles at Chaincode Labs and Injective Labs.
Low-latency systems, decentralised platforms, cloud-native infrastructure
Founder, Engineer
Founded Webvork in 2017 and runs operations. Previously at Blinqx and Tradecast.
Real-time applications, data-heavy platforms, retrieval and integrations
What we will not claim
Read our backgroundFor your security team
Whoever has to sign it off will want the architecture, the data flow, and the governance answers in one place they can read without you in the room. This page is that document. Print it to PDF and send it on.
Questions
No. The model runs on hardware you control, and inference happens locally. There is no outbound call to a model provider. In an air-gapped deployment there is no outbound path at all, and we document the full data flow so your security team can verify it rather than take our word for it.
For the work most companies want, document search, summarising, drafting, classification, and internal Q&A, open models running on modest hardware are already strong enough to be useful every day. We test candidate models on your own documents during the pilot, so you judge quality on your work rather than on a benchmark.
It depends on model size and how many people use it at once. A small team assistant can run on a single workstation-class machine. Heavier workloads want a dedicated GPU server. We size it during the assessment and tell you honestly when renting a private GPU is cheaper than buying.
Keeping processing inside your own infrastructure removes the third-party processor, the international transfer, and the retention question in one move. We are not your compliance officer, but we give you the architecture documentation and audit logging your compliance officer needs to sign off.
There are two parts: a fixed-price engagement to design and deploy the system, and a running cost that is either hardware you own or a monthly private GPU. We quote both after the discovery call, before you commit to anything.
You keep everything. The system runs on your infrastructure with open models and documented configuration, and your IT team gets the runbooks during handover. There is no licence to expire and nothing that stops working when we stop.
No. We do the part they have not spent the last two years on, model selection, retrieval, deployment, and tuning, and we hand it over in a shape they can operate. Most of our work is alongside an internal team, not instead of one.
A working pilot on your own documents usually takes two to three weeks after the architecture is agreed. The discovery call itself takes 45 minutes and costs nothing.
Next step
A 45-minute call. We look at your workflows, your data rules, and whether local AI is worth it for you. If it is not, we will tell you that instead of selling you a project.
Not ready for a call? Send the security brief to whoever has to approve it, or just reply to an email. All three work.
Work with us
We are not hiring right now, and we would rather say that than run a careers page full of roles that do not exist. If you build the kind of systems we build, introduce yourself anyway.