00Service
LLM-integrated products, agents, and the infrastructure that keeps them honest.
01What we build
- LLM-integrated product features
- Multi-step agent workflows with tool use
- Retrieval pipelines (Postgres pgvector, Pinecone, Qdrant)
- Eval harnesses and regression suites
- Voice and multimodal pipelines
- Cost, latency, and safety guardrails
02How we approach it
A demo is not a product. We invest in evals, telemetry, and reproducibility from the start so the team can keep shipping after the launch demo gets old.
We resist the agency cliché. No glow gradients, no "AI sparkle". An AI product earns trust by being correct, fast, and legible.
Cost and latency are design problems. We architect the prompt path, the retrieval path, and the fallback path before we write the first system message.
03Process
- 01
Frame
What does the model do well? What must it never do?
- 02
Evals
A graded test set written before the prompt.
- 03
Build
Retrieval, tools, guardrails, observability.
- 04
Ship
Staged rollout, telemetry, weekly model bake-offs.