Span
Span offers a platform that aggregates AI‑generated code, pull‑request metrics, and time‑tracking data to give engineering teams a unified view of development activity. It provides real‑time dashboards and effectiveness scorecards that quantify AI impact, highlight bottlenecks, and surface high‑impact work, integrating with tools like GitHub, Linear, and Jira.
- Artificial Intelligence
- Data & Analytics
- Developer Tools
Funding
Funding not disclosed
Founders
Product
Problem
Engineering teams scaling with AI generate more output but lose visibility into how work progresses, where time is spent, and which efforts drive results. Coordination overhead, fragmented reporting, and hidden context create inefficiencies and hinder continuous improvement.
Solution
Span provides a platform that aggregates and visualizes engineering activity, linking AI‑generated code, pull‑request metrics, and time data into a unified view. By instrumenting development workflows, Span captures agent traces and contextual signals, then surfaces actionable dashboards that show effort, impact, and quality at the team level. The system quantifies AI effectiveness with metrics such as estimated bug density and effectiveness scorecards, helping teams identify bottlenecks and optimize AI adoption. Integrated with existing tools, Span enables leaders to align planning, reduce reporting friction, and focus on building rather than tracking.
Target Audience
Span is aimed at engineering leaders, DevOps managers, and AI‑focused development teams in fast‑growing startups and large enterprises seeking data‑driven insight into their software delivery processes.
Features
- Automatic collection of AI agent traces, code review data, and time logs across repositories and CI pipelines
- Real‑time effectiveness scorecards that evaluate AI‑generated code quality, bug density, and PR size impact
- Interactive dashboards that map effort to outcomes, highlighting high‑impact work and coordination hotspots
- Integration connectors for popular version‑control and project‑management platforms (e.g., GitHub, Linear, Jira)
- Environment readiness insights that surface configuration issues affecting AI agent performance
- Spotlights feature that surfaces isolated wins and propagates best practices across the organization