AutoQAin
From application knowledge to resilient release confidence.
AutoQAin keeps manual intent, client approval, automation, execution, AI repair, and release evidence connected in one explainable workflow.
01020304
Flow healthIntent preservedAll evidence attached
step.06Locator drift detectedRemapped inside Flow DNARELEASE SIGNALSafe to ship
Evidence complete · 08:42Platform modules
A connected path, not another tool pile.
Projects & Knowledge
Structure environments, product context, acceptance criteria, tickets, documents, recordings, and navigation knowledge.
AI Test Designer
Generate manual cases from requirements and application knowledge, then route them through client review.
Test Optimizer
Import client cases, remove duplication, correct gaps, enrich steps, and preserve source lineage.
Test Repository
Filter approved coverage by project, functionality, environment, creator, source, and review state.
Automation Studio
Generate structured Playwright specs and reusable helpers with exact manual-step mapping.
API Testing
Turn API definitions into compiler-safe Playwright tests with assertions, responses, and debug context.
Execution Center
Run tests, follow step states, inspect evidence, and distinguish script drift from product defects.
Release Guard
Protect critical flow sets and summarize whether a build is safe, risky, or blocked.
Why it is different
AI can act. Your team can still see why.
Agents work from saved product knowledge and explicit workflow boundaries.
Client approval remains a meaningful quality gate instead of a hidden model decision.
Every status should trace back to steps, logs, screenshots, responses, or audit history.
Safe automation drift is repaired; persistent product behavior is classified for human action.
Early-access feedback
We are earning the proof before publishing the praise.
Our pilot program collects consent-based feedback from real testers. Public customer quotes will appear here only after verification and approval.
Does knowledge improve coverage?
Pilot teams compare AI-designed cases with the requirements and application context they supplied.
Can users trust the repair?
Testers review exactly where automation failed, what changed, and whether intent stayed intact.
Is the evidence actionable?
Quality leads evaluate whether reports support debugging, defects, and release conversations.
Evidence policyNo fabricated testimonials. No invented usage numbers. No certification claims before verification.Join the early-access program
A better quality conversation starts here
Request a guided pilot↗