LLM / RAG / evaluation
AI systems are only useful when you can measure them.
We design retrieval systems, evaluation harnesses, regression tests, and scorecards that make LLM behavior visible to engineering and leadership at the same time.
Evaluation before scale
LLM systems often look promising in demos and become fragile in production. The difference is usually evaluation: representative tasks, clear scoring, regression coverage, and reporting that shows whether a change made the system better or worse.
We help teams create retrieval and evaluation layers around real workflows, business constraints, and edge cases. The goal is a system your team can inspect, improve, and defend.
Useful for RAG, vendors, and internal AI tools
This work fits teams building RAG systems, comparing LLM vendors, improving answer quality, evaluating agent workflows, or preparing an AI system for broader internal or customer-facing use.
Next step
Need evidence before the next rollout or vendor choice?
Bring the system, task set, or evaluation question that needs to become measurable.