Test automation with AI: code review, tests and documentation at your team's pace
The development team ships under pressure and tests are left for later. Code review depends on a senior developer who is always overloaded. Documentation of internal systems is out of date, and every new integration needs a meeting to understand how things work.
Software development with AI, step by step
Review of every pull request
The agent reads the changed code and points out likely bugs, security risks and departures from the team's standards, for the human reviewer to decide.
Tests written alongside
The agent suggests and writes automated tests for what changed, prioritizing the critical parts of the system.
Documentation kept current
With each change, the agent updates the API and module documentation and asks the author of the change to review it.
Tasks organized
The agent turns bugs and requests into well-written issues in Jira or GitHub and follows their progress with the team.
Less repetitive work. More control.
- More tests covering the critical parts
- Faster, more consistent code review
- Documentation that keeps pace with the code
- Senior developers with time for architecture
Ready-made AI teams for this work
Questions about software development with AI
Does the agent merge on its own?
No. It reviews and suggests. Approval and merging stay with the developers, according to the repository rules.
Is the company's code kept safe?
Yes. Repository access is by permission, actions go into the audit trail and the code is not used to train third-party models.
Does it integrate with GitHub and GitLab?
Yes, as well as Jira, Confluence and Slack.
Does it work with legacy systems?
Yes. The agent is especially useful for documenting and testing old code, which is usually the code with the least documentation.
