Not just tested.
Challenged. Verified. Explained.

The client supplies a high-level idea or plan. The AI Engineering Team develops the detailed specification, decomposes and delegates internally, and owns implementation, developer tests, review and fixes. The independent AI QA Team derives expectations from that specification and user risks, not solely from the code or its tests. The AI QA Team owns its findings and coverage assessment, including disagreements and unresolved gaps, and explicitly approves or rejects staging handoff. Only a candidate approved by the AI QA Team goes to the AI DevOps & MLOps Team for staging. A named human owner separately authorizes production and accepts residual risk.

No. Existing developer tests, CI checks and domain expertise remain valuable inputs. The team adds independent challenge, cross-system verification, exploratory work and a coherent evidence packet. Your experts help define expected behavior, evaluate specialist issues and review consequential findings. Passing developer tests do not establish complete product coverage.

Agree scope around UI and browser journeys, APIs, integrations, regression and exploratory testing. Add explicitly authorized security testing, automated and knowledgeable human accessibility evaluation, and performance testing against agreed workloads and budgets. For AI features, add representative and adversarial evaluation with versioned criteria. Name unsupported environments, excluded areas and missing access in the coverage plan.

The AI QA Team records preconditions, steps, expected and actual behavior, build and environment, user impact and sanitized evidence. Triage distinguishes product, test and environment defects. The AI QA Team rejects staging handoff with detailed findings and returns the work to the AI Engineering Team, which owns the fix. The independent AI QA Team retests the original failure and adjacent regression on the new revision, preserving the original finding and any flakiness instead of closing it on a passing retry alone. The fix/retest loop repeats until the AI QA Team approves the candidate for staging deployment by the AI DevOps & MLOps Team; rejection never bypasses the gate.

Yes to staging handoff, no to production authorization. The AI QA Team explicitly APPROVES or REJECTS the exact candidate against the specification, attaching detailed findings, open defects, coverage gaps and residual risk. Rejected work returns to the AI Engineering Team for fixes and independent retesting by the AI QA Team. Only a candidate approved by the AI QA Team goes to the AI DevOps & MLOps Team, which deploys to staging, verifies staging health and shares the URL, revision and deployment results with the client. Production remains a separate human-authorized decision. Passed, failed, blocked, not run and inconclusive outcomes remain distinct. Flaky behavior and retry history remain visible alongside them. There is no all-green quality score or promise of complete coverage.

Provide a testable revision, agreed requirements, authorized environments, approved synthetic test data and a named product and release owner. Grant least-privilege access for the agreed scope. Restrict evidence access and redact personal data, credentials and sensitive traces. Production-impacting actions, destructive tests, load tests and security probes require explicit authorization and safe limits.

Choose one consequential change: a checkout update, permission model, integration or critical workflow. Bring acceptance criteria, known risks, an authorized QA environment, relevant existing tests and someone who can resolve product questions. Agree scope, evidence expectations and decision criteria before execution. Highly regulated or safety-critical work also needs qualified domain review and its own assurance process.