Your own AI.
On your servers.
Your data never leaves.

It depends on the tier, and on how much you trust a policy you can't verify. Consumer tools may retain your inputs and can use them for training; enterprise tiers promise otherwise, but you're trusting a document, not something you can audit. A model running on your own hardware removes the question entirely: there is no third party to trust.

Every build is scoped and quoted individually. Model size, hardware, and how deeply it integrates with your systems drive the cost; two businesses rarely land on the same number. The comparison that matters is against your monthly API spend, which never stops. Builds start from €4,000; the assessment puts a real number on your case.

Not always. If you have no compliance driver and a small API bill, staying on the cloud is the sensible choice, and we’ll say so in the assessment. It becomes worth it when your data is sensitive, your usage is heavy, or a regulator or auditor is asking questions about where that data goes. We wrote down the criteria we use: when not to self-host an LLM.

Yes. Open-source models run fully offline once installed on your hardware. For environments that require it, we deliver air-gapped deployments with no connection in or out.

For scoped business tasks like answering from your documents, drafting in your formats, or sorting your tickets, a fine-tuned open model is competitive. For open-ended frontier work, no, not yet. Most business use is scoped, which is why this works.

We work under NDA and a data processing agreement. Training happens on your infrastructure, or on isolated hardware that is wiped after handover. Your data never trains anyone else's model.

No. We support what we build, or hand over runbooks to whoever already runs your servers. If a model needs a team of engineers to keep it alive, we built it wrong.