AI-native QA insights for modern teams.
Release discipline, test strategy, and AI workflow design — opinionated writing for teams that want stronger quality systems, not more noise.
Our risk pill almost shipped a lie. Our own eval caught it.
We built a Gantt board that flags its own slipping groups and drafts a catch-up plan from your team's methodology. Then our own A/B eval told us the proof was rigged — so we fixed the product, not the test. The honest 5 → 1 → 5.
Our coral sat 9° from our error red. We fixed color by measuring, not feeling.
A user told us twice the colors were too similar. The first fix over-corrected — every category chip went gray, and uniform is not the same as clear. The second fix put a number on it: our brand coral lives at hue 9°, our error red at 0°. Nine degrees apart, same lightness. You don't argue with 9°. You move.
The governance flywheel: when recurring review findings write their own methodology rule
Every AI review finding already lands in a per-case worklist. But the patterns across cases were going unread. The flywheel reads them — and when a weakness has truly recurred, it proposes one grounded SKILL.md rule into the queue you already approve from. The SQL decides. The model only phrases. A human still signs.
Drop a case onto a plan — the target appears only while you drag
Grab a test case from the Library tree, drag it down, and a drop strip materializes at the bottom of the screen — listing only the plans that can actually accept the case. Zero idle pixels, no closed ledgers reopened by accident.
The AI rewrites your Gherkin — one click brings the original back
Every content edit to a test case now snapshots a version, with a server-computed diff. So when the AI rewrites a scenario to fix a review finding, the byte-exact original is one click away. AI you can audit and undo.
AI review findings that stop vanishing
One click runs an AI review on a pending test case. The findings don't evaporate when you look away — they persist as a worklist with a real lifecycle, each one actionable, and they quietly feed everything downstream.
The And-step you write becomes a runnable automation step
A single-line Given/When/Then can't carry a real multi-step scenario. We added a verbatim Gherkin field and made every write path — import, chat, manual, CSV round-trip — preserve your newlines byte-for-byte, so the steps you author become the steps your code runs.
Reviewing dozens of pending cases without losing your place
Approving AI-generated test cases used to mean a modal that opened, then closed, then dropped you back at the top of the list. We rebuilt review as a persistent three-zone workspace where context never resets.
Coverage you can act on: the gap is the button
OpenTestX rebuilt its Coverage tab around an authoritative requirement catalog. A requirement with zero tests now shows as a gap card you generate tests from in one click — the gap is the button.