Each case study follows one problem QA teams commonly run into — what it looked like, what the QA team does with QAEverest step by step, and what changes as a result.
The feature was described in a Jira epic, a Figma file, a PDF of underwriting rules and a spreadsheet, and the sources disagreed.
One source-tagged summary with its conflicts listed, a refined story answered in writing, and generated cases with pairwise coverage.
A move to web components, icon-only buttons and an embedded payment frame broke years of Selenium scripts, sprint after sprint.
Import the existing suite, run it on an engine that reads the live page, repair in one place, and keep an exit as real code.
The same few tests failed most nights, real regressions hid among them, and a screenshot showed that a step failed but not why.
Flaky tests separated from regressions, failures replayed with an AI root cause, and seeded defects that show what the suite really catches.
Functional, performance, security and accessibility results sat in separate tools and were compiled by hand before every release meeting.
One ship-confidence score with gates the test lead sets, severities on failures, scheduled scans, and a readiness PDF for the meeting.
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.
Exact-match assertions failed on correct answers, and prompt injection and personal-data leaks had only been spot-checked by hand.
Scenarios and a red team generated from the policy, fixed rules before a judge model, repeated runs, and a drift gate in CI.
API tests checked status codes only; tokens were copied between requests by hand and the ledger row was checked separately in a SQL client.
Chained API requests, read-only database steps in the same test case, and generated integration and database cases for review.
There was no up-to-date map of the portal's pages and flows, validation rules were unknown, and the release was weeks away.
An autonomous exploratory run that maps the app, probes its inputs and files reviewed bugs, then saves the flows it found as a suite.
Bring a story, a suite or a release question of your own to a demo and we will walk through it in the product with your QA team.