Release-readiness verdict
One rolled-up call per release, built from real execution results rather than gut feel.
Every release, measured before it ships.
Quality trends, hotspots, flaky rate, cycle time, and coverage roll up into a single release-readiness verdict, so the question 'can we ship?' finally has an answer backed by data.
Every signal streams in.
Sentry, PostHog, Grafana, logs, telemetry, and CI results flow into the same ledger the reviews write to, so quality data and production data live in one place.
Errors get pinned to code.
Every production error is traced to the exact section of code that caused it, with the why, the where, and the owner attached, so triage starts at the answer.
Trends accumulate per repo.
Hotspots, flaky rate, cycle time, coverage, and the share of AI-generated code build up over time, so you can see where risk concentrates before it bites.
The verdict rolls up.
All of it condenses into a single release-readiness verdict per release: ship, or here is exactly what is blocking. The go call becomes a reading, not a guess.
What the ledger tracks.
Numbers that answer questions engineering leaders actually ask, generated from the same reviews that gate your merges.
Quality trends and hotspots
The files and modules where defects cluster, tracked over time so refactoring effort goes where it pays.
Flaky rate and coverage
How trustworthy the suite is and where its gaps are, measured continuously instead of audited yearly.
Cycle time
How long changes take from open to merge, and which stage of review is the bottleneck.
Error tracing
Production errors pinned to the code that caused them, with the owner attached, so triage starts at the answer.
AI authorship share
How much of your codebase is AI-generated and how that code behaves compared to the rest.
Risk is damage multiplied by spread.
Averages hide the module that takes the release down. Autter measures where changes land and what they can reach, so the risk you see is the risk you actually carry.