Engineering Intelligence, DORA metrics, AI Impact — our analyses and practical guides.
Fixed staleness thresholds produce noise. Here is how to build a P50/P75/P90 pickup-time baseline from your own merged pull requests, and use it to flag the open PRs that are about to become blockers.
How DevPrism MCP integration eliminates context-switching by bringing platform insights where developers already work — their IDE. Less tab-juggling, faster incident response, better flow.
An honest comparison of Engineering Intelligence platforms: positioning, pricing models, AI capabilities and where each one genuinely fits.
Close to 3 hours per Engineering Manager per week go into a status report that is stale before it is read. How an AI-generated digest replaces it, personalized per audience.
Why the next generation of engineering intelligence platforms uses AI agents that investigate root causes — not just display metrics. The shift from passive dashboards to agentic analysis.
A framework for graduated AI autonomy in engineering teams — from passive dashboards to closed-loop optimization. Where does your organization stand?
A 5-dimension maturity model that replaces subjective reviews with objective metrics. Build a 30-60-90 day improvement plan grounded in data.
A practical framework for measuring the true return on investment of AI coding assistants. License costs, time saved, productivity gains, and the metrics that actually matter.
Acceptance rate tells you adoption, not impact. Learn how to cross-correlate AI assistant usage with velocity, quality, and technical debt to measure what actually matters.
GitHub says +55%. METR says −19%. DORA says "it depends on your organization." We read the serious studies on AI-assisted development — and explain why the only number that matters is your own.
How to implement the SPACE framework with a blend of objective metrics and targeted surveys. Move beyond gut feelings to data-driven developer experience measurement.
Beyond the marketing: how to objectively measure Copilot's impact on your team. Acceptance rate, throughput, quality — the metrics that matter.
Deployment Frequency, Lead Time, Change Failure Rate, MTTR and the new Rework Rate: implement the DORA metrics with real data and avoid the common pitfalls.
Why we built DevPrism — a platform that doesn't just display metrics, but understands the Why and acts automatically.