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.
Eleven years leading IT for a large secondary school: thousands of client endpoints and a student body that doubles as a permanent red team — every control weakness gets found and shared. That's a live-fire education in what actually holds: least privilege, sensible defaults, layered controls.
The carrier-grade years — NZ, Australia, the Pacific — taught the discipline we still build to: redundancy, failover, monitoring, and the assumption that something will break at 3am. Uptime was the job. It still is.
Every system touches someone's information. Access is scoped, secrets are vaulted, data flows are documented — we can tell you where your data lives and who can reach it. If a proposal can't answer that, it doesn't ship.