Real data from real engineering teams
Case studies, productivity data, and engineering insights — all from measuring our own team with the tools we build.
Claude Code Usage Analytics: What Engineering Teams Actually Need to Track
Most engineering teams know how many Claude Code seats they pay for — but not how many developers actively use them. Here's what usage analytics for Claude Code captures, the four metrics that predict long-term adoption, and what dedicated tooling shows that Claude Code's own interface never will.
How to Roll Out Claude Code to Your Engineering Team: A 30-Day Playbook
Most engineering teams provision Claude Code seats and never track adoption. Here's a week-by-week rollout playbook — the three setup steps before day one, the metrics that predict long-term adoption, and how to spot ghost seats before your first renewal.
How BYOK Works with Claude Code — and Why Engineering Teams Use It
BYOK (bring your own key) lets engineering teams route Claude Code sessions through their own Anthropic API keys — enabling per-developer cost attribution, spending caps, and data control that Anthropic's default team plan doesn't provide.
How to Track GitHub Copilot Usage Across Your Engineering Team
GitHub Copilot's native analytics show active users and acceptance rates — but not which seats went dark after week one, per-developer session depth, or cross-tool comparisons. Here's how to get real adoption visibility.
How AI Coding Tools Change Your DORA Metrics (And What They Can't Tell You)
Teams using AI coding tools see 18–35% improvements in deploy frequency within 90 days — but DORA metrics alone can't tell you if AI caused it. Here's what changes, what stays hidden, and how to track both.
AI Management Myths vs Reality
Six AI management myths engineering leaders still believe — and what session-level data shows instead. Most 'AI strategies' are subscriptions, not strategies.