AI Productivity Metrics, Backed

by Stanford Research & P10Y

Validated with Real Code, Not Theory
70 real Java commits were analyzed by 10 experts to create a solid evaluation benchmark.
Grounded in Stanfor Research
The system was trained on reviews by senior software professionals using structured evaluation criteria.
Expert-Level Code Review, Powered by AI
AI models can now mirror expert code assessments — in seconds, not hours.
Beyond LOC & Story Points
Traditional metrics fall short — AI estimation correlates better with real development effort.
99% Faster Than Manual Review
AI gives expert-level insights in under 1 second per commit.
Applied by P10Y, Informed by Stanford
This approach is actively used by P10Y and grounded in Stanford's peer-reviewed research.

Our universal, tech-agnostic output metric lets you benchmark your team's performance against other organizations.

Our model delivers scientifically-validated software productivity insights.

Analyze your current performance and the impact of past decisions.

Choose between cloud or on-prem deployment (for heightened security needs).

Developer Contribution Assessment
Analysis of added, deleted, refactored, and reworked code for each participant.
Performance Comparison
Displaying developers' results relative to the global median.
Issue Detection
Identifying potential delays, team overload, or resource shortages.
Decision-Making
Optimizing workflows and task distribution based on data.
Overall Output Volume
Assessing the team's total productivity over a selected period.