We conduct productivity benchmarks to diagnose issues, spotlight high-performing teams, and promote best practices. By leveraging data-driven insights, our model provides objective, scalable assessments of software engineering output, fostering transparent, constructive discussions to address challenges and optimize performance.

As engineering teams grow, relying on intuition and experience to assess productivity becomes unsustainable. Traditional metrics like story points or commit counts are flawed and easily manipulated. Our research at Stanford suggests a more effective approach: analyzing code downstream to uncover team dynamics and bottlenecks. While manual code reviews can yield accurate insights, they’re slow and costly. That’s why we developed a groundbreaking tool that evaluates code with precision, speed, and cost-efficiency—providing data-driven support to enhance developer productivity at scale.

This case study compares five software teams from three agencies working on similar projects, uncovering significant performance gaps. While Net Promoter Score (NPS) highlights client expectations, it fails to measure the true value teams deliver for their cost. By analyzing over 2 billion lines of code, we aim to provide transparent, data-driven insights to optimize team performance and outsourcing decisions.