Regulated releases need evidence: every requirement tested, and acceptance signed by the right people against the right results. Here is how a bank's QA team produces that evidence as a by-product of the work.
Scenario: A QA team supporting releases under regulatory audit
Requirement coverage lived in spreadsheets and UAT approvals in email, and data had to stay inside infrastructure the bank controls.
A live traceability matrix, UAT cycles with sign-offs pinned to the results they approved, server-enforced roles, and on-premise deployment.
Before each audit the QA team rebuilt the same evidence by hand: a spreadsheet mapping requirements to tests, UAT approvals forwarded from email, and screenshots of results. Security review also needed answers on sign-in, access control and where the data lives.
Evidence is produced as the QA team works, so an audit becomes a download rather than a project.
Every requirement is linked to the test cases that cover it, with each test's latest pass or fail alongside, and anything uncovered is shown. An AI pass explains the gaps and says whether each answer came from the AI or from rules. The matrix shows UAT status per requirement and downloads as Excel or PDF.
A QA lead plans a cycle, copies in test cases and assigns them, including to testers whose role lets them run only their assigned UAT cases and read reports. Testers record pass, fail, blocked or skipped; a failed case gets a severity and can be filed to Jira or ClickUp from the cycle.
Approvers, in the organisation or outside it by email confirmed with a one-time code, approve, approve with conditions or reject. A sign-off counts only for the results it was given against, every one is kept with the counts it saw, and the cycle report downloads as PDF or Excel, with a separate sign-off certificate PDF once someone has signed. By default the Quality Command Center blocks a release while a cycle in progress lacks a complete, current sign-off.
Five ready-made roles plus custom ones built from twenty-eight permissions, enforced on the server. Single sign-on with SAML, an audit log of who did what and when, and accounts that lock for fifteen minutes after five failed logins.
The platform ships as containers to run in the customer's own network or VPC, with AI inference in the customer's own AWS account and region, optionally over a private endpoint. Before a test-generation prompt reaches the model, recognisable secrets are swapped for placeholders and restored in the reply.
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