Berlin's economy is software, mobility, public-sector technology and media, and the shape of the work follows that almost mechanically. Very little of it is a factory problem. Most of it is somebody's roadmap.
Software and SaaS companies arrive with a feature that already half exists: a summariser, an assistant, a search box that is meant to answer rather than list. The technical question is rarely whether a model can do it — it usually can, on the ten examples in the demo. The question is what happens on the ten thousand it has not seen, and whether you can tell the difference before a customer does. That is an evaluation problem, and we build the harness with the feature rather than after it. The pattern is described under in-product AI and the measurement side under evaluations.
Mobility companies bring operational data with a geography attached — vehicles, journeys, incidents, support tickets that reference all three. The interesting systems join those together, and the interesting constraint is that a journey is personal data about a person even when your dashboard treats it as a row.
Public-sector and govtech work brings a different bar entirely. The output has to be explainable to someone who did not commission it, the record has to survive a change of supplier, and the transparency duties are not optional extras. That work usually starts with governance rather than a model, which we cover under AI governance.
Earlier-stage companies want something in front of users before the next raise, and that is a genuinely different engagement — smaller, faster, and honest about what has been deferred. It is described under startups.