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Teams using AI coding tools ship 98% more pull requests. Review time grew 91%. The code volume scaled — QA didn’t. QLane is the QA half. Point QLane at your app, open a pull request, and an AI agent navigates it in a real browser, finds the bugs, and posts a structured review with screenshots and repro steps. Silent on pass. Evidence on fail.

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What you get on every PR

Bug reports

Reproducible, not opinionated. Every bug ships with a screenshot, the exact click path, and a severity grounded in user impact.

Test cases

Generated as you ship. QLane reads each PR diff and drafts the cases that should exist — they become active when you merge.

Coverage map

What’s tested, what’s not. Coverage areas mapped to test cases with pass/fail history.

GitHub reviews

Silent on pass. Bugs come back as structured per-comment reviews with severity tags.

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Pick your starting point

Quickstart

Five minutes to your first PR test.

How QLane fits your app

Test a URL, a single repo, a Compose stack, or your existing previews.

Connect a Compose stack

Run your whole multi-service app on every PR.

Bug reports

What a finding looks like, and how to triage one.

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Who it’s for

  • Engineers — bugs caught before review, with reproducible evidence. /qlane:fix loads the bug straight into your editor.
  • QA teams — AI as a force multiplier. Review proposals, run sessions on demand, keep the smoke suite sharp.
  • Product managers — release with evidence, not vibes. Coverage gaps surface the moment a feature ships without tests.