AI quality engineering
Machines scale. Experts decide.
Probabilistic systems can't be tested like deterministic ones. Every AI release verdict we build combines three layers, and leaves evidence a reviewer can act on.
Three layers, one verdict
How we test an AI system before your users do.
Code rules
Permissions, formats, latency and hard constraints. Deterministic, fast and unforgiving: the checks that never need a judgment call.
Model graders
Grounding, relevance, faithfulness, tone and multi-turn coherence, scored per response with DeepEval metrics and MLflow LLM judges and tracked per release.
Domain experts
Risk, nuance and the definition of a useful answer. People own the bar. AI never decides what counts as verified.
Agentic systems
Test the path, not just the answer.
A reliable agent doesn't just produce a good final answer. It chooses the right tools, respects permission boundaries, keeps its memory straight, and recovers cleanly when a step fails. We validate the whole trajectory.
Every verdict leaves evidence
What was tested, on which model and prompt versions
Each identified risk traced to the check that covers it
A traceable reason to release, fix or stop
What must change before the next attempt
Aligned to NIST AI RMF · OWASP Agentic Top 10 · ISO/IEC 42001 · Section 508