How to choose an AI implementation partner in Europe.

Every consultancy in Europe now sells "AI transformation". Most of it is advice about software, not software. If you are picking a partner to put AI to work inside your organization, the selection comes down to a handful of checks you can run in one call.

Start from a use case, not a platform

A good partner asks where your team loses hours before they mention any tool. If the first conversation is about a platform license, you are buying their product, not your outcome. The first engagement should be one process, named, with a number on it: hours saved per month, error rate down, response time cut.

Five questions that separate builders from advisors

• When do I see something working? The answer should be measured in days. A working demo on your data beats any deck.
• Who owns the code and the data? You should. Walk away from anything that locks your process inside someone else's platform.
• What does it cost, and when is that decided? Before the work starts. An estimate that "will firm up during discovery" is not a price.
• Who trains my team? A system nobody uses is a cost, not an asset. Training and handover belong in the scope, not in a change order.
• What happens after go-live? Someone has to watch the system, tune it, and extend it. Ask who, and at what price.

Red flags

Hourly billing with an open end. A discovery phase measured in months. Demo videos where a live system should be. No concrete answer on GDPR, data residency, or where the model runs. References that are all logos and no numbers.

What good looks like

A fixed price agreed before you commit. A first use case in production inside a month, in your stack, not a sandbox. EU data handling by default. And a reference with numbers attached: for 100in, a Belgian pre-seed VC, our first system went live 9 days after kickoff.

That is the bar we hold ourselves to. If you want to test us against it, book a free 30-minute call or read how we run an AI agent implementation.