London's concentration is financial services, insurance, law, media and professional services — which means the AI problems that arrive from it are overwhelmingly document and decision problems rather than manufacturing or sensor problems.
Banks, insurers and asset managers arrive with a process where a person reads a long document and then makes a judgement that has to be defensible months later. The interesting engineering is almost never the model; it is the trace — what the system saw, which policy clause it relied on, who approved it. Our risk and compliance architecture is that pattern written down.
Law firms and in-house legal teams arrive with contract review, discovery and research, and with a hard constraint that client material cannot go anywhere it might be retained or trained on. That usually resolves to a deployment inside their own perimeter, which we cover under legal and compliance.
Media and publishing groups arrive with archives — decades of material that is technically online and practically unreachable. Retrieval over that is the most reliably valuable AI project in the sector, and the least glamorous.
Scale-ups arrive needing AI capability inside the product itself, where the constraint is unit economics rather than compliance. That is a different engagement shape, and it is covered under startups.