Smoke on a new feature
A baseline set of checks appears in minutes, then you add the edge cases.
AI test case generation
You supply the description, the system returns structured cases with preconditions, steps and expected results. Edits and the final call stay with you.
What goes in
A single text field. You paste what is already written: nothing has to be prepared specially.
- A ticket, a user story or a fragment of a spec
- Up to 10,000 characters at a time
- Cases are created straight into the project suite you picked
What comes out
A set of cases in the same format as hand-written ones: edit them, extend them, put them into a plan right away.
- Preconditions, steps and an expected result on every case
- Wording is fixed before saving, anything extra is deleted
- Saved cases are indistinguishable from ones created by hand
Where it saves time
The release is in two days
The draft covers the routine part and your time goes to negative scenarios.
One tester on the team
You do not have to hold the whole scope in your head: the system proposes a structure and you check it.
Your data stays yours
Your descriptions are not used to train public models. In an on-premise deployment the AI runs inside your own infrastructure and never reaches out.
Try it on a real task
Take a ticket that is in progress right now and see what comes back. The call on what to keep always stays with a person.