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TestRelic AI

Ask your Playwright tests why they failed

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Artificial Intelligence
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Every QA debug loop looks the same — CI fails, you open logs, Slack, Jira, Grafana. 45 minutes later, still no root cause. Ask AI fixes that. Type a question, get a rendered artifact — dashboards, sprint reports, test plans, stakeholder slides — built from your live Playwright data. No queries. No config. Just answers. Free 14-day trial. No credit card. Built by the founding team behind LambdaTest Test Insights.

Top comment

Hey Product Hunt 👋 I'm Srivishnu — founder of TestRelic AI and previously on the founding team at LambdaTest where I built Test Insights from zero to scale. The problem I kept seeing: QA engineers spend more time finding failures than fixing them. 6–9 tools, no single answer, no connection to real user impact. Ask AI is my first swing at fixing that. Type a question in plain English, get a rendered artifact back — dashboard, report, slides, test plan — built from your live Playwright data. Nothing to configure. It's early. I'd genuinely love to hear from anyone who's lived this debug loop — what's missing, what doesn't make sense, what you'd want it to do that it doesn't yet. Try it free at testrelic.ai — no credit card, installs in under 3 minutes. Happy to answer anything below. 🙏

Comment highlights

This resonates a lot! the QA debug loop you describe is painfully real. Jumping between CI logs, monitoring, and tickets just to reconstruct context is where most of the time gets lost.

The “ask → get structured artifact from live data” approach is especially interesting. Turning raw Playwright signals into something like dashboards or test plans without manual querying feels like a big step toward making QA workflows actually usable at scale.

We actually launched on Product Hunt yesterday as well — building Ogoron, an AI system that automatically generates and maintains test coverage as products evolve. Slightly different angle, but very aligned in spirit: reducing the manual overhead around QA and making the system itself do the heavy lifting.

Curious how you handle ambiguous signals or partial failures in CI — where the root cause isn’t clearly attributable to a single source?