I design and build AI systems and the platforms they run on

Most AI projects stall in the gap between a working prototype and a system people rely on. Closing that gap is mostly engineering: knowledge graphs that turn scattered documents into something queryable, agent pipelines with clear responsibilities and fallbacks, evaluation that tells you whether a change made things better, and observability that shows what the system did and why. The demo is the easy part. The hard part is a system that still behaves at 2am on a Friday, six months after launch, when nobody remembers the details of how it was built.