From the data up.
Most engagements start with the data rather than the model, because that is usually where the problem is. Everything we build leaves you with documentation, source code, and full control — no licences, no lock-in, and no ongoing dependency on us unless you want one.
Data foundations
Most AI projects fail on the data, not the model. We connect the systems you already run, clean and structure what's in them, and make the result queryable — so the insight you're missing becomes reachable before anyone trains anything.
Learn more →Private LLM deployment
Your own language models on your own hardware. Draft, summarise, and search across material that was never allowed near a public API.
Learn more →AI workflow automation
Document intake, approval routing, internal knowledge search. The repetitive work that eats billable hours, handled on your own systems.
Learn more →AI security & compliance
A structured audit against the EU AI Act, GDPR, and your internal policies — including the classification work: which data may leave your infrastructure, which may not, and the evidence for both. Risk model and remediation plan you can hand to your board.
Learn more →Generative AI pipelines
Image, video, and content generation running locally. Brand-consistent output at scale, with no per-generation cost.
Learn more →Optional, not assumed.
Model updates, monitoring, new workflows, and changes forced by regulation — available as a monthly engagement once your deployment is live. Plenty of teams take the documentation and run it themselves. That outcome is a success, not a lost sale.
Talk it throughCommon questions
What happens if something breaks?
Every deployment ships with a 30-day warranty, full documentation, and runbooks for the failure modes that actually occur. Monitoring is available as an ongoing engagement. Because it's all open source, you're never locked to us to fix it.
Can you work with our existing infrastructure?
Yes. On-premise, OVH, Hetzner, Scaleway, VMware, Proxmox, or bare metal. Deployment is standard Docker, so it lands in whatever you already run.
How long does a typical deployment take?
Private LLM: 2–6 weeks. Security and compliance audit: 1–3 weeks. A single automation pipeline: about a week. Full suite: 6–8 weeks.
What affects the final cost?
Infrastructure complexity, data volume, the integrations you need, and how far your compliance requirements reach. You get a fixed quote after the audit phase — not before, because anyone quoting before the audit is guessing.
Is the first session really free?
Yes. Ninety minutes, no obligation, no pitch. We look at your infrastructure and your use case and tell you what's worth doing. If we're not the right fit, we'll say so and point you somewhere better.
Would you ever tell us to use the cloud?
Yes, and we'd rather say so than sell you infrastructure you don't need. Sovereign is the default because it's the right default for the data our clients hold — but where the data isn't sensitive and the economics clearly favour a hosted model, that's the honest recommendation. What we won't do is make that call implicitly. You get the classification in writing: what may leave, what may not, and why.
Our data is a mess. Is it too early to talk to you?
It's the opposite — that's usually the right time. Most AI projects fail on the data rather than the model, so the first engagement is often getting the systems you already run connected, cleaned, and queryable. Plenty of the value shows up at that stage, before any model is involved.
Why should we trust a young practice?
You shouldn't, on assertion alone. So we publish the evidence instead: the infrastructure running this business is the same architecture we'd build for you, documented publicly down to the configuration. Read the stack and the blog, then decide.