Most AI projects fail the same way: a demo that never survives contact with production. We design and operate data and AI systems the other way round — production constraints first, model second. The result is infrastructure that works on Monday morning, not just in the pitch deck.
Pipelines, warehouses and governance that your team can actually operate after we leave. Boring where it should be boring, clever only where it pays.
LLM and ML systems designed around latency, cost and failure modes from day one — evaluated, monitored, and reversible.
A second opinion on an AI or data build before you commit the budget. We tell you which parts are load-bearing and which are theatre.
Before any architecture, we pin down what production actually demands: data volumes, latency, compliance, who's on call. Then we design to those, not to a trend.
We put a thin slice into production early — real data, real users, real failures — then harden it. No six-month discovery phases.
Documentation, runbooks and handover are part of the build, not an afterthought. If it needs us to keep running, we did it wrong.