AI test assistant inside your project

Open a suite, and the assistant suggests test cases that match its content or the linked requirement. You edit titles and ask for more options with Follow up, then add the relevant cases. The suite gets ready test cases, and the team saves design time.

The assistant reads a requirement and finds edge cases with possible logic resolutions. You discuss unclear points with the team before development starts. Risky scenarios reach the test suite instead of production.

Upload a screenshot, and the assistant writes a test description with user actions, expected results, and the test data it finds on the screen. You edit the draft and save it when it matches the scenario. Tests get documented faster, and details stay in place.

The assistant analyzes the code of an automated test and writes a readable description. Manual testers and managers understand automated tests without reading the code. The whole team works with the same clear test base.

AI reviews a test description or test code and suggests improvements. You apply the suggestions that match your standards. Clear tests reduce confusion during execution and onboarding.

Describe the runs or tests you need in plain language, and AI writes the TQL query. The assistant uses a separate schema for Runs and for Tests, so the query matches the fields of each area. You build advanced filters without the exact TQL syntax.

You ask questions about project documentation in plain language. The AI assistant searches the requirements and answers with the context of your project. Decisions take less time, and knowledge stays available to the whole team.

The assistant compares a requirement with its linked suites. You add missing tests and remove redundant ones directly in the AI window. Coverage grows where it matters, and the suite stays clean.