PR testing

Every PR tested.
To ship with confidence.

Get dynamic testing on every PR. QA.tech validates your changes with regression and exploratory testing before anyone reviews – or merges.

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Trusted by high-performing engineering teams at:

Lovable
Nordea
Pricer
AstraZeneca
Upsales
Sambla Group
AdminPulse
MeetingPackage

What PR testing does

When a pull request opens, QA.tech classifies the change, runs regression and exploratory tests against your preview environment, and posts a verdict back to GitHub or GitLab before a human opens the review. Verdicts are anchored to the diff, so pre-existing failures are reported separately instead of blocking your PR, and if the tests ran without actually exercising your change you get an “unverified” verdict rather than a false green. You can also mention @qa.tech in a PR comment to trigger a deeper review with your own instructions.

Validate every PR with AI
that runs the tests for you.

Stop merging code that breaks staging. Agents test every PR in its preview environment and tell you – and your reviewers – exactly what changed before review even starts.

Illustration of a smooth GitHub setup flow

Smooth setup

Connect the GitHub App or GitLab and QA.tech automatically picks up every PR and its preview deployment – tests run before you merge, zero config required.

Illustration of a fast PR feedback loop

Fast feedback loop

QA.tech runs regression and exploratory tests on every PR, posts pass/fail results to GitHub and flags what broke – before anyone reviews.

Network log and trace surfacing a payment validation failure

Detailed debugging insights

Get screenshots, logs and network activity for every step of the test run to debug faster and know exactly what caused the failure.

320h/mo
QA time saved

"We have replaced over 320h of manual testing every month with QA.tech."

Fredrik Seidl
Fredrik Seidl – CTO at Upsales
Upsales is a SaaS platform for CRM, marketing automation & Sales in B2B. ~150 employees.
Read their story

Complete your agentic workflow with AI testing

Every PR check runs on our AI QA testing platform, the same agents that power scheduled suites.

Remove the maintenance tax

AI agents adapt to UI changes automatically – so your tests keep running without your team spending hours fixing brittle selectors.

  • No selectors to update on every refactor
  • Tests follow intent, not the DOM
  • Suite stays green through redesigns
AI agent typing into a sign-up form without brittle selectors

Accelerated release velocity

Agents run continuously, cutting QA time and freeing senior engineers from script maintenance to focus on strategy and architecture.

  • Validate every PR in parallel
  • No queueing behind a manual QA cycle
  • Confidence to merge without waiting on staging
Dashboard with QA tech agents scheduled across builds and times

Vision-based UI testing

QA.tech sees your UI visually, not through code. It finds elements the way a user would – so design or flow changes never break your test suite.

  • Catches regressions selectors would miss
  • Robust against component library upgrades
  • Works on any framework, no SDK required
Vision-based test comparing four balance card variants

Instant tests across mobile, web and API flows

Create tests with natural language prompts and catch cross-platform failures early. AI mimics real user journeys that jump between mobile apps, web and API calls – without your team diving into device-specific selectors. Backend-only contracts run through native API testing, with no browser involved at all.

  • One scenario across every surface
  • Covers async flows and webhook callbacks
  • Reuse the same tests across environments
Diagram of tests flowing across mobile, web and API surfaces

Your AI testing blueprint for
fast, reliable releases.

No infra setup needed

AI agents run in the cloud. Point them at your environment and start testing.

Write tests in natural language

Work with AI in plain English and get new test case ideas.

SOC 2 Type 2 compliant

Safe and enterprise-ready. Your data is never used to train models.

No access to code needed

QA.tech tests your product without reading your code – no lengthy approval to try it.

Accessibility testing

Run accessibility checks on every change. Catch regressions before they become debt.

Dynamic test data generation

Generate compliant, realistic datasets covering standard paths, edge cases and error inputs.

Use cases teams start with.

Start with the checks you want blocking a merge, then let dynamic tests cover whatever the diff touched:

PR & CI/CD gatesSmoke testingAutomated regression testingShift-left testingTesting AI-generated codeAPI regression testingFeature flags & A/B variantsStaging vs productionTests that survive UI changesExploratory testing

Your team moves fast. Can your testing keep up?

QA.tech agents test your product autonomously, so moving fast never means shipping broken. See how it works in a 30-minute demo.

Get a demo

Frequently asked questions

Can't find what you're looking for? Reach out.

What is dynamic PR testing?
Dynamic PR testing is how QA.tech tests a pull request: instead of replaying a fixed regression suite, the agent reads the change, decides which flows the diff can affect, and runs regression and exploratory tests against those flows in the preview environment. The verdict is anchored to the diff, so a pre-existing failure is reported separately and a run that never exercised your change comes back “unverified” rather than green.
How is this different from running our regression suite in CI?
QA.tech keeps the two apart on purpose. A small critical-path suite (sign-up, payment, whatever must never break) runs on every PR as the blocking gate. Dynamic testing scoped to the diff then covers what the change actually touched. A large suite run on every PR does neither job well: it is slow, and at roughly 1% flake per test a 100-test suite fails on its own almost every run.
Does it check the PR against the ticket?
Yes, when the branch links to a Linear or Jira issue, or carries the issue key in its name. The review agent pulls that issue and checks the change against its acceptance criteria.
What if the tests pass but didn't touch my change?
You get an “unverified” verdict. Green means the change was exercised and behaved; unverified means it wasn't, which is a different thing and worth knowing.