Impact of AI on Engineering Productivity

Understand how AI coding tools affect developer productivity and code quality

Research Problem

Organizations need data-driven insights on how AI tools like Cursor or GitHub Copilot impact their engineering teams' productivity and code quality.

Our Solution

We measure the real-world impact of AI tools on engineering productivity via a ML model that replicates a panel of experts evaluating every code commit written by your engineers.

Since 2022, we've worked with

600+

Organizations

120K+

Engineers

Our research has been featured in

Criteria for Participation

We work with companies and organizations (not individuals)

Any Geography & Industry

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Minimum Company Size: 50+ Software Engineers

Git Only: GitHub, GitLab, Bitbucket, or Azure DevOps

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AI Tool Usage: Using Copilot, Cursor, etc. with API access to usage data

Receive Insights in 4 Steps

Integrate Repository

⏱ ~5 min

Connect your Git repository

Provide AI Tool Usage

⏱ ~5 min

Connect via API to your AI coding tools

Provide Metadata

⏱ ~15-90 min

Share non-confidential organizational data

Receive Results

Get comprehensive productivity insights

Deployment Options

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Cloud

Code processed in our secure cloud environment

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On-Prem (Private Cloud)

Code never leaves your environment

Other Ongoing Research

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Contact Us

Want to get in touch with the research team?