A model in a notebook is the easy part. We design and engineer across the whole stack — from infrastructure to the product surface — so AI is reliable, affordable, and safe to operate.
Most AI projects fail in the gap between a working demo and a system that runs in production. We close that gap by engineering each layer below to a known standard — and wiring it into the systems you already operate.
We treat agents as software, not prompts. Control flow is explicit, tools are typed, and humans stay in the loop where it matters — so behavior is predictable and every run is explainable.
Retrieval is engineered as its own system. Hybrid search balances keyword precision and semantic recall, chunking and indexing are deliberate, and responses are grounded with citations you can trace.
We benchmark agent and LLM outputs against gold standards, track regressions across prompt, model, and logic changes, and flag unsafe or incorrect outputs before they reach users.
AI is only valuable if it’s affordable at scale. We route work to the right model, cut token burn with caching and batching, and instrument cost and latency per feature, tenant, and workload.
Everything we ship is built to be operated: clean APIs, structured logging and observability, multi-tenant architecture, and staged rollouts behind feature flags that integrate with the systems you already run.
We build with governance in mind from the start: clear data handling, documented model risk, and contracts that respect controller / processor boundaries — aligned with the EU AI Act timeline and the NIST AI Risk Management Framework.
Selected experience from prior work across financial services, industry, media, and mobility — the basis for how we engineer today.
Bring your architecture, constraints, and the part that worries you. We’ll tell you what we’d build, what we’d reuse, and where the real risk is.