How BlackBat AI Works

That's all it needs. Point it at staging, production or a preview deployment — no scripts, no selectors, no configuration. If the interesting parts of your app sit behind a login, add a dedicated test account's credentials once and save them to an environment so later runs reuse them. Apps that only run inside a private network can be tested through the BlackBat AI Runner.

It navigates your UI the way a person would: loading pages, deciding what is clickable, filling in forms and following links from one screen to the next. Every page and flow it reaches is charted on a navigation map, so you can see what was covered and, just as usefully, what wasn't. A full run crawls everything; a diff run re-crawls only the pages that changed since your last run, for a fraction of the time and credits.

Broken interactions, dead clicks, failed form submits and error states are flagged as they happen. Each finding comes with a screenshot of the moment it broke and the sequence of steps that led there, so you can retrace it in a minute instead of guessing at a vague report.

Beyond automatic discovery, describe specific flows to test in plain English — "Check that users can sign up, verify their email, and complete onboarding." BlackBat AI generates a structured, runnable test suite from it, which you can read and tighten before running anything. No selectors, no scripts. Use exploration for breadth and prompted suites for the journeys the business depends on.

Start runs on demand, on a recurring schedule, or straight from your CI/CD pipeline. Each run gives you pass/fail results per test, with screenshots and context for every failure, so a failure points at a specific step rather than a red flow. Trigger runs from a CI/CD pipeline with an API key (paid plans) to gate a deploy on the result.