AI Engineering Productivity
Measure delivery speed with real engineering data
Connect your repository to quantify how GenAI changes your delivery speed. Move from tracking seat counts to proving ROI with high-fidelity data from the codebase. Instead of relying on tool dashboards or license utilization, Milestone measures what matters: how work moves through your engineering pipeline.
Measure Delivery Speed with Real Data
Milestone reconstructs your teams’ entire development history from repository activity and completed work to show how AI-assisted development is actually affecting delivery timelines. Milestone replaces surface-level tracking with artifact-based performance metrics, measuring what matters: how work moves through your engineering pipeline.
Benchmark AI tool impact on cycle time
See whether AI actually helps your teams ship faster. Milestone compares GenAI-assisted work with traditional coding across every phase of the development cycle. From first code change to final merge, revealing where AI accelerates delivery, where it improves throughput, and where it creates hidden review or coordination friction.
Compare coding and review throughput
Evaluate specific tool performance by separating the coding phase from the review phase. Identify whether specific tools help developers author code faster or if they generate changes that add complexity, slow down peer review, and create hidden delivery friction.
Analyze tool efficiency by PR size
Milestone groups pull requests by size so you can compare AI impact across different levels of complexity. Group pull requests into Small, Medium, and Large categories to reveal exactly where your chosen tools improve efficiency and where human coordination remains a priority.
Engineering intelligence from your actual codebase
Milestone analyzes the code your team produces to give engineering leaders a clear, reliable view of productivity, delivery flow, and AI impact.
Codebase as the source 
of truth
Completed work reconstruction