Dev Task Automation with Jira, GitLab, and AI
An engineering team was spending 40% of their sprint on routine tickets. We built a pipeline that reads Jira tickets, generates code, creates merge requests, and moves tickets to review — so engineers can focus on the hard stuff.
This SaaS company's engineers spent nearly half their time on routine work: config changes, copy updates, simple CRUD operations. Senior engineers were constantly context-switching between strategic work and boilerplate. The backlog grew faster than the team could clear it.
We connected Jira, GitLab, and Claude. When tickets are tagged for automation, the system extracts requirements and codebase context, generates implementation code following the team's patterns, creates a merge request, runs tests, and moves the ticket to review. Engineers review and approve rather than write from scratch.
Routine tickets that took an average of 4 hours now take under 45 minutes. Engineers got back 15+ hours a week for complex feature work. The backlog cleared 3x faster. Junior developers learned the codebase faster by reviewing well-structured generated code.
What happened
- ·Routine tickets: 4 hours → under 45 minutes
- ·Engineers reclaimed 15+ hours per week
- ·Backlog cleared 3x faster
- ·Consistent code patterns across implementations
Working on something similar?
We'd be happy to talk through your situation and see if we can help.
Get in touch