AI agents and LLM-Ops, made boring.
The infrastructure under the magic
A demo agent is a notebook. A production agent is a system. Between those two is several quarters of unglamorous work: evaluation harnesses, retrieval design, guardrails, traceable execution, cost ceilings, human review queues, and a control plane that does not melt under load.
That is the work we do.
What we build
- Retrieval pipelines with grounded sources, version-pinned indices, and replayable retrieval traces
- Evaluation harnesses that measure task success, escalation quality, cost per decision, and failure-mode distribution — not benchmark scores
- Guardrails and policy boundaries expressed in code, not in prompts
- Observability on every prompt, response, tool call, and token spent
- Cost ceilings at the budget, agent, and tenant level
The control plane is the product
When agents start chaining actions — calling tools, mutating records, triggering workflows — the control plane stops being optional. Kill switches, approvals, rate limits, and replay paths become part of what you ship.
Model routing without religion
We pick models per task. Cheap models for structured extraction, capable models for ambiguous reasoning, local models when latency or data residency demands it.
Where we have done this
Tax research and filing evidence. Customs and tariff document agents. Care coordination and PHI-safe workflows. DAO governance simulation. Algorithmic-trading execution control planes. Public-sector FOIA redaction.
The boring infrastructure is what makes the magic reliable. We ship both, in that order.