Agents, pipelines and evaluation you can put in production.
The gap between an impressive demo and a system you can put in front of customers is mostly evaluation, cost control and failure handling. That gap is where we work. We build AI features that behave predictably enough to depend on.
What this covers.
LLM application engineering
Production systems built on frontier models — prompt architecture, structured output, streaming, retries and graceful degradation.
Retrieval and RAG
Chunking, embedding and retrieval tuned against your corpus, then measured — because untested retrieval quietly returns the wrong thing.
Agents and tool use
Multi-step workflows with real tool calls, bounded autonomy, and an audit trail of what the system did and why.
Evaluation and guardrails
Eval suites that catch regressions before your users do, plus the guardrails that keep output inside acceptable bounds.
Cost and latency control
Model routing, caching and context discipline. Most AI features are two decisions away from costing a fraction of what they do.
How an engagement runs.
- 01
Frame
What the system must get right, how often, and what happens when it does not.
- 02
Prototype
The narrowest version that proves the approach, measured against a real eval set.
- 03
Harden
Failure paths, guardrails, observability, cost ceilings.
- 04
Integrate
Into your product and your stack, behind your auth, inside your data boundaries.
- 05
Operate
Monitoring, drift detection, and a plan for the next model release.
Things people ask first.
Which models do you build on?+
Whichever the workload justifies. We benchmark candidates against your evaluation set rather than committing to a vendor before there is evidence.
Does our data train anyone else’s model?+
No. We work inside your accounts and data boundaries, on providers whose terms exclude training on your inputs.
Can you improve an AI feature we already shipped?+
Often the highest-value engagement. We start by building the evaluation you are missing, which usually locates the problem within days.