A product engineering team was struggling with backlog hygiene — tickets were poorly scoped, missing acceptance criteria, and grooming sessions consumed hours of senior engineer time every sprint.
The Problem
Backlog grooming was eating 4–6 hours per sprint across the senior team. Tickets arrived from multiple sources (support, product, engineering) with inconsistent quality. Sprint planning was slow because tickets needed significant rework before they could be estimated.
The Approach
We built an AI grooming agent that:
- Analyzes incoming tickets against the team’s project templates and historical patterns
- Enriches descriptions with missing context and technical details
- Suggests acceptance criteria based on similar past tickets
- Identifies dependencies and potential blockers
- Flags tickets that need human review before they hit the sprint backlog
The Outcome
- Backlog grooming sessions cut from 4–6 hours to 1–2 hours per sprint
- Ticket quality improved measurably — fewer “needs clarification” rounds
- Sprint planning became more predictable
- The team reported higher confidence in sprint commitments