QA.tech: Dynamic Testing on Every PR

About QA.tech

QA.tech operates as an AI-driven QA testing platform that automates exploratory, regression, and PR tests on web and native mobile apps. It targets engineering teams who want faster feedback on changes and reliable verification without writing traditional test scripts. The tool emphasizes automated, goal-based testing rather than script-based steps, relying on AI agents to validate user journeys and changes across environments.

QA.tech routes testing tasks to specialized AI agents based on the context of the app and current changes. It describes flows in plain English and lets agents learn from the app to execute tests that adapt as the product evolves. The system supports tests from PR previews through production, providing failure details like screenshots and explanations to aid debugging.

QA.tech presents various plans and security considerations, including enterprise-grade options with SSO and SOC 2 compliance.

Use cases

End-to-end testing of long, multi-step journeys across apps and environments
PR-level testing and CI/CD gatekeeping

Key features

End-to-end, goal-based testing without scripts
PR and CI/CD testing integration
Contextual, knowledge-graph driven test creation
Rich failure insights with visuals
Cloud-native execution with no code access

Pros and cons

Pros

Runs tests across PRs, previews, and production environments

No code required for test creation

Cons

Starter plan excludes mobile testing

Some capabilities require higher-tier plans

Reviews

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Frequently asked questions

Can QA.tech create tests without us writing code?

Yes. QA.tech is an AI agent that performs manual testing workflows at the pace of automation. You describe the goal in natural language – what to verify and what should happen – and the agent runs the workflow end-to-end. No scripts, no pre-defined paths.

Can QA.tech integrate with our CI/CD pipeline and run tests on pull requests?

Yes. Tag the QA.tech bot in a PR comment and it opens a conversation using the PR context (notes, comments, Jira or Linear acceptance criteria). It runs existing tests for the affected area or auto-generates new ones, then posts a report inside GitHub – typically in a couple of minutes instead of a day or two.

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