01
Costs go down, not up
The more you use it, the more your AI responds locally: marginal cost trends toward zero within a fixed subscription. With external LLMs, it is the opposite: the more you use, the more you pay.
The benefits of your own Private AI
Why make AI your own.
Rented AI has a structural flaw: you pay forever, and in the end nothing remains yours.
02
Independence from providers
Reliance on external LLMs decreases month by month: no one else's price increase can catch you by surprise.
03
Your data stays yours
Your employees' questions do not train someone else's models: know-how stays in the company.
04
An asset that stays with you
The knowledge built is a company asset: it grows with use, and if you change direction one day, it is yours.
05
Answers in your context
Your AI knows your price lists, contracts, processes and history: it answers for your case, not in general.
06
Control and compliance
A defined perimeter, roles and permissions, servers in the area you choose: the simplest path to GDPR and the AI Act.
How it is built
Private AI means two things at once. Protection: the router identifies sensitive data — PII and company rules configurable on the prompt — and keeps it on local inference, on your machines or in a private tenant with private models. Knowledge: from the prompts that pass through, the system extracts, classifies and connects information in a self-growing knowledge base; documentation is added through ingestion. Your AI is built on your data — documentation, history, resolved cases — never on frontier model outputs.
Honestly
We do not promise your AI will replace frontier models from day one: at first it will do little, then more and more. The router decides case by case, and you see the curve rise.
