Built for the long game.
Named for what matters.

Three children. Three reasons to do work worth doing.

lerad-ai is an anagram of their names. It is a reminder, every day, that the work has to be worth doing.

After more than a decade leading product at Google — across health tech, Android developer platforms, connected sensors, and telecoms — Fabien made a decision that surprises most people: he left.

Not because the work was not extraordinary. It was. But the most interesting AI challenges were not inside a large company. They were in the thousands of mid-market businesses trying to figure out how to compete in a world moving faster than they can hire.

The other reason: three children and a life worth designing. lerad-ai is deliberately structured around that. Four clients at a time. One day per week per engagement. A business that generates real value without requiring everything.

Ten years of shipping AI-powered products at Google changes how you see problems. Not because of the brand. Because of the reps.

Hundreds of millions of users. Decisions made under real pressure. Cross-functional teams with competing priorities. The moment where a technically correct solution fails because nobody asked what trust looks like for the person using it.

That experience surfaces in the first conversation with a new client. We hear the stated problem, and we hear the unstated one underneath it — the misalignment between long-term company objectives and short-term team incentives, the pivot nobody inside has permission to name, the data that will not be usable until significant governance work is done.

That is not a framework. It is pattern recognition trained across two decades.

We spend more time upfront understanding the actual problem than most firms spend on the entire engagement.

We ask uncomfortable questions. We push back on assumptions. We tell you when AI is not the right answer — and we tell you what the right answer is instead.

We map your organisation across multiple dimensions simultaneously: long-term strategic objectives, short-term team incentives, technical constraints, data readiness, and the external competitive forces pushing the timeline.

Then we build.