Representative datasets
Real tasks, edge cases, adversarial inputs, and production failures become durable test material.
If quality cannot be measured, it cannot bereleased responsibly.Evals turn preference into engineering evidence.
Evaluation systems for LLM applications, RAG, and agents using datasets, rubrics, regression gates, human review, and production feedback.
- Designed for your stack
- Evaluated before release
- Observable in production
- Owned by your team
What production-ready means here
You get a working system with explicit quality boundaries — not a model call wrapped in a polished interface.
How we take it from uncertain to dependable
We start with the behavior and operating constraints that matter, then make each release measurable and reversible.
Define quality
Translate product expectations into observable criteria and clear failure categories.
Build the harness
Run repeatable offline and online evaluations with traceable inputs and outputs.
Close the loop
Turn user feedback and incidents into new tests, not temporary anecdotes.
Show us what you built.
Bring the current build, the workflow, or the production problem. We will map the shortest responsible path forward.