End-to-end testing for iOS, Android and web. You can afford to test every release. Write the test yourself, or ask your AI tool to. You choose the AI model for each compile, run and discovery, from seventeen Claude and OpenAI models. You see the estimated cost in credits before you run or schedule a test.
The loop
Describe a test as ordered natural-language steps. Compile it into a deterministic plan: with a map from Discovery, the compile takes your app's real selectors from it; without one, it works from the steps alone. Runs replay the plan, so a green run is reproducible, not a lucky improvisation. Run it on a real device from the farm. Read the evidence: a screenshot per step, the duration, the model used, the redacted logs, the network capture when the run recorded one, and an honest pass / fail / error verdict — an error is an infrastructure answer, never a fake test verdict.
Around that loop: Discovery crawls an app screen by screen and proposes scenarios (quoted before it starts), schedules run scenarios unattended on a cron cadence, and everything the app can do is also reachable by your agent and your CI — the same workspace API key drives the CLI, the MCP server and the REST surface.
Scenarios, runs, discovery, schedules and devices are also documented in the app — each screen explains its own contracts. What is on these pages is the part you need before you are signed in, plus the machine surfaces (nth, MCP, REST) an app screen cannot show you.