AI that ships, governs and pays back.

We benchmark candidates against your eval set on quality, latency and cost. We re-run benchmarks quarterly because the frontier moves. Model choice is a calibration decision, not a brand decision.

Yes. We deploy to your AWS / Azure / GCP account, integrate with your IAM and key management, and route to either hosted or self-hosted models per your data classification.

RAG with citations, structured outputs validated by schema, eval harnesses that flag regressions on every change, and clear UI affordances when the model is uncertain. There is no silver bullet — but there is rigorous practice.

Threat model up-front, segregate trust boundaries between user input and tool calls, audit every tool invocation, and red-team the whole system before launch.

Most enterprise use cases are RAG-shaped — the model is fine, you just need clean retrieval over your existing knowledge. Fine-tuning is rarely required and almost never the first move.