Metrics that actually predict engineering health, beyond story points and PR counts. Quantifying technical debt, measuring quality, and making the business case for engineering investment.
A measurement worksheet that pairs delivery throughput with DORA stability signals and AI-contribution context, so you can baseline, compare, and investigate tradeoffs with your own data.
A worksheet for connecting coding-tool subscriptions, model usage, review effort, rework, and build activity to accepted changes, using your own invoices instead of borrowed benchmarks.
How Connectory Genie gives AI agents typed organizational meaning, scoped context, and domain checks before their proposals become software changes.
AI writes code faster than teams preserve why systems work. A typed memory framework covering policies, decisions, owners, and open questions across repos.
A practical Connectory use case for biotech and drug discovery teams that need shared org memory, PR evidence, and agent governance across scientific software.
A practical buyer guide for leaders moving from AI coding tools to governed agentic software delivery, with institutional memory as the control layer.
Concrete frameworks for translating technical debt into financial metrics that make CFOs approve remediation budgets instead of asking 'can it wait another quarter?'
AI-generated code ships fast but can compound technical debt silently. GitClear's churn data shows why, and a few metrics and gates let you catch the debt before it turns into incidents.
Test coverage percentage is a poor predictor of production reliability. Here are the indicators (Change Failure Rate, Review Depth Score, and rework rate) that actually tell you whether your codebase is healthy.
Engineering teams track PRs merged and lines written. Few track whether AI-generated code survives 90 days in production without incident. Here's what to measure instead.
See how Connectory helps teams tackle these challenges.
