Release confidence score
A 0-100 score that combines test results, coverage, change risk, defect severity, flaky tests and AI-code exposure.
Qualyn turns pull requests, test results, coverage, security findings, AI-code risk, manual risks, and operational readiness into one clear call: safe to ship, why, and what would change the answer.
Safe to deploy with one review item: AI-generated checkout changes need targeted regression coverage before merge.
Ship after targeted regression on checkout promotions and mobile payment edge cases.
Qualyn helps teams convert noisy engineering activity into a simple, explainable view of release confidence.
A 0-100 score that combines test results, coverage, change risk, defect severity, flaky tests and AI-code exposure.
Every recommendation explains why the score changed and what action will reduce risk before production.
Built-in release gates flag failed tests, missing evidence, critical findings, and blocking release risks.
Identify when AI-generated code increases release uncertainty and apply stricter checks where they matter.
See whether your product quality is improving sprint by sprint, or whether velocity is hiding risk.
Plain-English release summaries for CTOs, product leaders and founders who need decisions, not jargon.
The beta combines automated GitHub signals with human-owned release context, so the score can reflect both code evidence and operational readiness.
Track human-owned release blockers, mitigations, acceptances, owners and expiry dates, then include them in the release score.
Capture deployment, rollback, monitoring, support and incident-response checks so readiness becomes part of the release signal.
Review score contributions, policy versions, evidence-set fingerprints and missing inputs behind every release score.
Record production outcomes, incidents, rollbacks and escaped defects to spot false greens and improve scoring over time.
Use GitHub code scanning and dependency review evidence alongside tests, coverage and change data when those signals are available.
Qualyn answers the questions engineering leaders ask before every release using measurable signals and a score the team can trust.
Qualyn maps code, tests and issue signals into a release-level risk profile.
Risk heatmaps highlight fragile flows, untested areas and high-change modules.
Each score is backed by the signals that increased or reduced confidence.
Actionable recommendations help teams reduce risk before production.
Connect GitHub and your existing test, coverage and issue signals.
Qualyn evaluates change risk, test evidence and AI-generated code exposure.
Every release receives a transparent confidence score with supporting reasons.
Your team gets a clear recommendation: ship, review, or hold.
Practical guides on CI, release readiness, and the checklist teams need before a ship/no-ship decision.
A green pipeline confirms configured checks passed. It doesn't confirm that the release has enough evidence to be safe for customers.
Release readiness is the state of evidence behind a release decision, and no single checklist item can hold it.
A useful checklist does not replace judgment. It makes the release evidence visible enough for judgment to improve.
For trying Qualyn on one repository.
For teams that need release confidence and actionable insight.
For teams with multiple repos and governance requirements.
Qualyn scores the release evidence your team already produces. If there is no CI pipeline, no integration tests, and no release process, there is nothing for a score to stand on. Qualyn won't build that foundation for you.
Testology places a QA engineer with your team to build it: CI/CD pipelines where none exist, the first integration and end-to-end tests, and some order in whatever unit tests you already have. Expect 3 to 6 months from zero to a release process Qualyn can score. After that, keep the engagement running while you adopt Qualyn as the decision layer, or take it from there yourselves.
Testology is run by the team behind Qualyn: they set up the gates, Qualyn keeps them honest.
Move from scattered QA evidence to a clear decision layer for engineering leaders, product teams and founders.