How does Olakai calculate pull request velocity and ROI for engineering teams?
Olakai Agentic reads pull request data directly from your GitHub org and plots cycle time for AI-assisted versus non-AI PRs side by side, across every repo, team, and provider. AI-assisted PRs are typically 25 to 40 percent faster, and Olakai Agentic tells you whether yours are. Nothing gets installed on a developer's machine.
What PR Analysis measures
Olakai Agentic breaks coding time, review time, and total cycle time out for AI-assisted and non-AI PRs side by side. It reports PR volume and the AI code ratio, meaning the percentage of merged lines that came from AI-assisted PRs. It also gives a provider breakdown showing which tool produces faster PRs on your actual repos.
Source: For VPs of Engineering
How the data gets there
Coding IQ connects directly to your GitHub organization and to your AI coding tool admin APIs, covering Anthropic, GitHub Copilot, Cursor, Windsurf, and OpenAI, and pulls the data together automatically. Engineering teams do not have to build custom data pipelines or work from fragmented vendor consoles. The result is a unified view: cycle time comparison between AI-assisted and non-AI PRs, provider cost breakdown, developer adoption cohorts of Power, Casual, New, and Idle, and team-level benchmarks.
Source: Is Your $500K AI Coding Tool Investment Paying Off?
Baselines and cohorts behind the number
The Impact Dashboard's Developers tab breaks adoption into cohorts. Its Productivity tab uses a before and after methodology that compares each developer against their own historical baseline rather than against peers. None of this requires installing anything new on a developer's machine. It runs on pull request data Olakai already has access to.
Source: Inside the AI Impact Dashboard
Also asked as
- How does Olakai measure PR speedup and output from AI coding assistants?
- Can Olakai track code delivery acceleration across engineering cohorts?