Measure the real impact of AI on your engineering.
Your team adopted Copilot, Cursor, or another AI coding assistant. But did it actually improve delivery performance? Or just change the shape of the work?
“AI made us faster.”
Prove it.
Most teams adopt AI tools based on developer sentiment. But feeling faster and being faster are different things. CodeSpectra gives you the data to know which one it is.
Did lead time decrease?
Compare average lead time before and after AI tool adoption. Across teams, repos, and products.
Did quality hold?
Check if SonarCloud metrics (coverage, bugs, code smells) stayed stable or improved alongside speed gains.
Did failure rate change?
Faster code generation can mean more bugs. Track whether Change Failure Rate moved with AI adoption.
Before/after analysis, automated.
Mark the adoption date
Set the date your team started using an AI coding tool. Per-team or per-org granularity.
Automatic comparison
CodeSpectra compares DORA metrics, code quality, and practice traits from before and after the adoption date.
Quantified impact
See exactly how lead time, deployment frequency, failure rate, coverage, and code complexity changed.
Be the first to measure AI impact.
Join the waitlist and get early access when AI Adoption Tracking launches.
Book a Demo