AI framing and sovereignty
AI is already in your teams, often without anyone deciding it. Where the data goes, which uses hold up: nobody really knows. That is exactly the problem.
What I do
I audit actual usage, map the risks and weigh cloud, local and hybrid options with you. Realistic architectures and genuine usage sovereignty, not over-engineering.
You leave with
A clear usage policy, tested scenarios, and recommendations your teams apply without becoming data scientists.
This is for you if…
- AI tools run in the company, but not in your internal policy.
- Someone wants "an AI strategy" by next week.
- You're torn between an off-the-shelf tool and a self-hosted solution.
The method, applied
Why.
Inventory actual uses, not imagined ones, and the data they touch.
What.
Test two or three concrete scenarios, cloud, local or hybrid, on your own use cases.
Which way.
A usage policy, a target architecture and a roll-out at your pace.
Frequently asked questions
Do we need existing AI use first?
No. Framing also works upstream, to start on the right foot rather than fixing things later.
Sovereignty, concretely?
Knowing where your data lives, who accesses it, and being able to switch tools without rebuilding everything.
Project stalling? Let's talk.
Two or three lines of context are enough: what's stuck, what you're aiming for. We'll see together whether a conversation makes sense, and in what shape.
Get in touch