Private AI
Private AI
Use AI on your company knowledge and your infrastructure — without feeding a public chatbot. Two clear approaches; we help you choose the right one.
Engagement
Readiness → pilot → handover or AI Ops
Time frame
Starts with a compact readiness check · scope depends on use case
The situation
- Teams paste company data into public AI tools — and nobody knows where it goes.
- People hunt the same answers across folders, wikis, and old emails.
- Generic chatbots do not know your products, policies, or terminology.
- Your data protection officer has concerns. Rightly so.
Private AI — chosen for the job
Private AI means answers and processing stay under your control — on your servers, in the EU, or on infrastructure you approve. We start with a readiness check: the use case, the data, permissions, and whether it is worth building.
Then we pick one of two approaches. A knowledge assistant fits when your team needs answers from approved documents with visible sources. A specialised model fits a recurring, well-defined task that should run fast, privately, and under your ownership. You do not need both on day one.
Two approaches to private AI
Knowledge assistant
Your team asks everyday questions; the assistant searches approved documents and grounds its answers in them. Sources stay visible, access rights stay intact, and new documents can be added without retraining a model. Technically this is often called RAG — for your team it simply means: answers from your knowledge, with receipts.
Best for
Policies, product docs, handbooks, and internal know-how — when answers must stay current and citeable.
Specialised model
We teach a smaller model on your examples for a repeating job — for example classification, extraction, or domain-specific wording. It runs on infrastructure you approve, is handed over to you, and stays scoped to that task instead of open-ended public chat.
Best for
Narrow, recurring tasks where privacy, speed, and model ownership matter more than general conversation.
What's included
How it works
AI Readiness
Clarify the job, data, rights, risks, and which approach — if any — makes sense.
Pilot
Build a bounded version and test it with real questions or tasks from your team.
Roll out or stop
Expand only when value is clear; then hand over ownership or operate with AI Ops.
Why cognatio labs
We do not start with a model name. We start with a task and a quality bar. If a use case is not a fit, the readiness check says so. Architecture and hosting stay replaceable — your data and deliverables stay yours.
Proposal
Proposal after the readiness check
Scope depends on use case, data situation, and approach. You receive a clear recommendation before a larger build.
Frequently asked questions
Which approach do we need?
If your team needs answers from changing company documents with sources, start with a knowledge assistant. If you have a repeating, well-defined task with examples, a specialised model may fit better. The readiness check decides — you do not have to guess.
Is this the same as ChatGPT?
No. Public chat tools are general-purpose and usually process data outside your control. Private AI is scoped to your approved knowledge or your specific task, and runs where you decide.
Does our data leave the company?
In a self-hosted or EU-controlled setup, your data stays on infrastructure you approve. We state clearly which components run where.
Can the assistant still be wrong?
Yes. Language models can make mistakes. That is why we cite sources for knowledge answers, test against real work, and define cases that hand off to people.
Let's check whether private AI fits.
Bring one concrete use case. The readiness check tells you which approach makes sense — or when to wait.