Representative datasets
Real tasks, edge cases, adversarial inputs, and production failures become durable test material.
If quality cannot be measured, it cannot be released responsibly. Testing turns opinions into evidence.
Testing for LLM applications, RAG, and AI agents: real example cases, clear scoring rules, automatic checks before each release, human review, and feedback from live use.
- Designed for your stack
- Tested before release
- Monitored 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.
Testing in layers
Retrieval, generation, tool use, safety, latency, and task success are scored independently.
Release gates
Changes must meet defined thresholds before prompts, models, or workflows reach users.
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 measurable criteria and clear failure categories.
Build the test setup
Run repeatable tests before release and on live traffic, with every input and output recorded.
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.