AI coding tools have reduced the cost to ship. The cost to keep still compounds. Canopy connects your existing data and shows you where to invest and where to prune.
Example feature scorecard
Real data from cal.com, an open-source scheduling product: 44 features, 6 data sources.
Your git history knows which features churn. Your issue tracker knows which ones generate bugs. Your analytics know which ones users actually use.
The missing piece is a Canopy feature map: a manifest that connects every code path, every ticket, every event back to the features in your product. Once it exists, the signals flow automatically.
The feature map and verdict rules are shaped by your codebase, your data sources, and your business model — not by a generic template.
A complete snapshot of your portfolio: every feature scored on cost, benefit, and confidence, with verdicts backed by the underlying evidence.
Snapshots stack into history. Watch cost and benefit shift per feature, and see which decisions actually delivered the impact you hoped for.
Canopy has been validated on production systems. Two early case studies — one open-source, one private; one TypeScript monorepo, one multi-repo mobile app — surfaced the same shape of feature economics despite very different codebases.
The same pattern shows up in both:
A 30-minute call to walk through what a Canopy for your business would look like — no pitch deck, just questions about your product and technology.