What we deliver
Traditional QA proves your application does what the spec says. AI evaluation proves your model outputs deserve to be trusted. Most shops need both now — we staff both from one engagement.
Risk-based coverage plans: what to test, how deeply, and what to consciously skip — so QA effort lands where defects cost the most.
Automated UI and API suites wired into your CI pipeline, so every release candidate is checked before a human ever looks at it.
Regression cycles and structured exploratory sessions that find what scripts can't — the edge cases real users trip over.
Golden datasets, scoring rubrics, and red-team probes that measure your AI's accuracy, groundedness, and safety — before launch and continuously after.
Go/no-go reporting your leadership can act on: what was tested, what broke, what shipped anyway and why.
An embedded QA function on retainer — we own quality for your releases so your developers can own the building.
Why independent QA
The team that built a feature is the worst-placed team to break it. An outside QA function tests what users will actually do, not what the code was written to handle — and for AI systems, an independent evaluator is the difference between "the demo worked" and "we measured it." Building bots as well as testing them, our AI Bots practice keeps our evaluation methods honest from both sides.
Next step
Give us one release candidate or one AI workflow to evaluate. You'll get a defect list, a coverage map, and a straight answer on whether it's ready.