Know what your engineering org actually ships.
Weave looks at every PR and answers one question: how long would this take an expert engineer to complete?
Output trend across the last 13 weeks, benchmarked against peer engineering teams.
What output measures
One standardized unit of work, applied to every engineer, every repo, every language.
One standardized unit of work
Consistent and comparable across individuals, teams, languages, and organizations.
Meaning over volume
Unlike lines of code or PR count, Weave analyzes the impact of every change, not just how much was written.
Apples to apples, every time
The same model runs across every engineer, every repo, every language. No exceptions.
Compare teams
Visualize output across squads. Identify high-performing teams and surface where technical debt or process friction is slowing delivery.
Output per engineer · 30 days
Individual drill-down
View output per engineer within a team context. Spot coaching opportunities and recognize your most impactful contributors.
Platform · by engineer
| Engineer | Output | Quality | Reverts |
|---|---|---|---|
| M. Chen | 142 | 94 | 1 |
| R. Mehta | 131 | 92 | 0 |
| J. Park | 118 | 89 | 2 |
| S. Tombs | 96 | 91 | 1 |
What quality tracks
Three signals that connect production incidents back to the code that caused them.
Trace bugs to the PR that introduced them
Every incident links back to the commits that shipped before it. Stop reconstructing the timeline by hand.
Incident → cause
- Incidentsev-2 · 482
- Deploy2.14.0
- Pull request#1204
Track revert rates
See which engineers and repos have the highest revert rates. Catch quality issues before they compound.
Revert rate by repo · 90 days
Identify volatile codebases early
Spot areas of your codebase with high churn and rising bug rates before they become costly to maintain.
Churn by week · payments
What reviews measure
Metrics that show you whether your review process is a quality gate or a bottleneck.
Impact Analysis
Score code review quality on depth, thoroughness, and practicality. Keep your review bar high over time instead of letting it silently degrade.
Average review cycles before merge
Track how many rounds of review each PR goes through. Identify bottlenecks and reduce the cycles that slow delivery.
Review volume by team
Break down review volume by team member and week. Balance workload and keep reviews from piling up on your most senior engineers.
Rounds to merge · all repos
Reviews per reviewer · weekly
What benchmarks cover
Benchmarks calculated from real engineering data across thousands of organizations — not a published report.
Deploy MTTR · benchmark tiers
- EliteUnder 1 hour
- HighUnder 1 day
- MediumUnder 1 week
- LowOver 1 week
CI/CD metrics
Core deployment health
Deploy MTTR
Mean time to recovery from a failed production deployment. Benchmarked from elite (under 1 hour) to low (over 1 week).
Deploy frequency
How often your team deploys to production.
Deploy success rate
Percentage of production deployments that succeed.
PR deploy lead time
Time from code merge to production deployment.
Industry benchmarks
Segmented by org size: 1–5 up to 201+
Code output per engineer
Compare normalized output against real data from thousands of engineering orgs.
PRs per engineer
Fair comparisons regardless of your team’s scale.
Code review quality & turnaround
Industry-standard review depth and cycle time benchmarks.
Code LOC turnover
How much of your codebase is being rewritten relative to industry peers.
What AI impact tracks
Two signals that go beyond prompt counts, connecting AI activity to what actually ships.
Know how much of your code is written by AI
Track AI-generated code as a percentage of total output. Benchmark against industry peers so you know where your organization stands.
Authorship of merged code · 30 days
See the impact of AI on your output
Compare AI-assisted code against actual engineering output across bugs, features, and maintenance. Volume alone doesn’t tell the story. Weave connects AI usage to what actually ships.
AI-assisted share by work type
What the score tracks
Three signals behind the score.
Weave syncs usage metadata from connected IDEs and CLI tools. It attributes code output to either AI or human authorship using advanced heuristics, then correlates AI usage with engineering productivity and cost data to surface the full picture.
Foreground vs. Background AI
Categorize your tools into foreground AI (interactive tools like Cursor and Claude Code) and background AI (automated agents). See how each category impacts your codebase differently.
Impact tracking
Visualize how specific tools influence delivery speed. Correlate adoption of Cursor, Windsurf, Claude Code, and others with actual productivity changes across your team.
Financial health
Monitor subscription spend per user alongside output data. Make sure your investment in AI tooling aligns with the productivity gains you’re actually seeing.
The engineering intelligence platform for the AI era.
Output, quality, review health, and delivery benchmarks in one place — measured consistently across every team you run.