End-to-end regression testing, written from plain English

Every release, verified end to end.

Describe your critical user journeys in plain English. Qavtrix writes the tests, runs them in isolated cloud browsers on every change or on a schedule, and explains every failure with evidence your team can act on.

100 free credits every month. No credit card required.

How it works

  1. 1

    Create a project

    Point Qavtrix at your staging or production URL.

  2. 2

    Describe a workflow

    Write what a user does, in plain English.

  3. 3

    AI writes the test

    An LLM generates a reviewable Playwright script.

  4. 4

    Runs in the cloud

    Each run gets a fresh, isolated browser container.

  5. 5

    Evidence + analysis

    Screenshots, trace, and an explanation of any failure.

Everything your release process needs

Plain-English test authoring

Describe a user journey; Qavtrix writes a reviewable Playwright test. Browser flows and HTTP APIs alike.

Isolated cloud execution

Every run gets a fresh browser in a sandboxed runner, with strict time limits. Long journeys run in parts and still finish.

Evidence on every run

Step timeline, screenshots, logs and a Playwright trace you can replay, kept with a permanent run ID.

Failure analysis and UI change detection

Expected vs. actual, a likely cause and the next action. When a screen changes, it names the element that replaced the old one and updates the test with one click.

Automation and CI

Schedule journeys every 15 minutes or daily in your time zone, or run them from GitHub Actions on every push, labelled with branch and commit.

Run history and trends

Pass rate and duration per project over time, so unstable areas surface before your customers find them.

A failure report you can act on

A red build tells you something broke. Qavtrix tells you which step, what it expected, what it saw, whether it looks like an application bug, a stale selector, a timeout, test data or the environment, and what to do next. The script, screenshots and trace are one click away.

  • Expected vs. actual, side by side
  • Evidence quoted from logs, network and the DOM
  • Confidence score and model name on every analysis
FailedCheckout: guest order with promo code
1m 04s · run #214 · main @ 3f9c2e1
AI failure analysisApplication bugconfidence 0.92

The order total did not update after applying promo code SAVE10. The discount request returned 500, so the UI kept the pre-discount total and the assertion on step 5 failed.

Expected
Order total: $44.10
Actual
Order total: $49.00
Evidence
  • POST /api/cart/discount → 500 Internal Server Error (312 ms)
  • console.error: TypeError: Cannot read properties of undefined (reading 'rate')
  • Step 5 expect(locator('[data-test=total]')).toHaveText('$44.10') timed out
Suggested action: check the discount service's handling of codes without a configured rate; this is a backend regression, not a flaky test.

Security and governance, built in

Test code is treated as untrusted. Runners are isolated from your account data, and every action leaves a record.

Credential-free runners

Test scripts run in a separate runner that holds no secrets. Results return through short-lived, presigned upload links.

Signed execution

Every request to a runner is signed with an Ed25519 key and verified before anything executes.

Script screening

Scripts are checked before they run: no access to the runner's file system, processes or environment.

Scoped API keys

One key per project for CI, stored hashed, shown once and rotatable at any time.

Administration

Admin roles, account suspension and a cross-account view of failing runs with suggested fixes.

Usage you can audit

Credits are charged per test step, with every charge, grant and refund kept in a ledger.

Put your critical journeys under test this week.

Create a project, describe one workflow, and see it run with full evidence in minutes.

Start free