Task Intelligence for QA engineering teams
Three canonical QA patterns, redesigned at the task level. Test design, regression maintenance, defect triage. Hours freed per engineer, regression suite stays green, defect-to-developer cycle 3x faster. Same test framework, same CI, same release process.
Three patterns. One quality discipline.
A QA engineer's day is read structured content (a PRD, a failing log, a bug report), cross-reference context (the test suite, recent changes, prior defects), draft an output (test cases, root-cause analysis, triage routing). We have redesigned the three highest-volume QA patterns.
Test case design
AI reads the requirements or PRD or design doc, generates the test case matrix (happy path, edge cases, negative cases, boundary conditions). QA reviews and adds domain-specific cases the AI could not infer.
Regression maintenance
AI reads failing test logs and recent code changes, proposes whether the fix belongs in the test or the application code. Engineer confirms and applies. Flake-detection becomes investigative, not janitorial.
Defect triage
AI reads the bug report, classifies severity, component, and likely root-cause area, drafts the developer-facing context. QA lead reviews, routes, and adds release-impact judgment.
Where AI cowork lands first in QA workflows
941 qa tasks where the AI takes the load and the human stays on the decision. Pulled live from our task taxonomy. The top 5 are below. 1,898 more are ready for full automation, expand the list to see them.
1,898 qa tasks ready for full automation›
How we work with QA orgs
Co-sponsored model. QA Manager or Director of Quality plus your VP Engineering or Head of Release in the room together. The Sprint anchors on one team and proves the redesign in 90 days. The Transformation Engagement scales it across the QA org.
Want a QA-org-specific walkthrough?
20 minutes. We pull your top three QA task patterns from the dataset and show you the redesign live, with your stack and team mix.
Book a time