Span

Span is a platform built for engineering leaders who want to understand and improve how their teams work, especially as AI tools become more integrated into the development process. It connects data from various sources—like code repositories, project management tools, and AI agent sessions—to provide a unified view of engineering effectiveness. The core idea is to move beyond guesswork and give leaders evidence-based insights into where time is spent, what's working, and where processes can be improved.

The tool works by ingesting what it calls 'agent traces,' which are detailed logs of interactions between developers and AI assistants. Span analyzes these traces to see which AI prompts lead to successful code contributions, tracks sentiment and satisfaction, and links AI-generated work directly to pull requests and outcomes. This allows teams to measure their 'AI effectiveness' over time. Beyond AI, it also calculates standard engineering metrics like DORA, PR cycle times, and allocation of effort, aiming to reduce the manual overhead of gathering these insights.

This platform is most useful for managers of larger engineering organizations who need to prove the value of AI investments, streamline reporting for finance and compliance, and proactively identify areas where developer experience or processes could be better. It helps answer questions like: Is our AI spend driving real results? How much time is lost to maintenance versus new work? And where should we focus our platform improvement efforts to have the biggest impact on the team.