Every research line starts with a problem we ran into building real products. We prototype, measure, and decide. Ship it, iterate it, or kill it. Nothing survives just because it sounds good on paper.
AI systems built for specific tasks. Routing queries to the right model, processing text at volume, automating decisions that follow rules. Not general-purpose demos that break in production.
Systems that remove repetitive steps without removing human control. Override buttons on every automation. Audit trails on every decision. The operator can always intervene.
Deployment pipelines, observability stacks, cost-aware scaling. The infrastructure that lets AI products run reliably without burning budget on idle compute.
Methodology
We start with a specific, answerable question. Build the smallest thing that could answer it. Run it against real data, measure, and compare. Then decide: ship, iterate, or kill. No zombie experiments. No research that exists only on paper.