Case Study: Regal achieves safer, more mergeable AI-generated code with CodeRabbit

Regal raises comment acceptance to 47.4% with CodeRabbit

Regal, an enterprise voice AI and customer communications company, faced a challenge when AI coding agents increased the volume of pull requests from non-technical contributors faster than engineering review capacity could handle. This led to inconsistent code quality and placed a significant communication and cleanup burden on engineers. To address this, Regal implemented CodeRabbit as an AI-first pull request reviewer.

CodeRabbit became the first reviewer on every pull request, providing consistent feedback and allowing non-technical authors to iterate before human review. The solution included features like Change Stack for understanding complex changes and custom rules to enforce 12 database-migration risk patterns. As a result, overall comment acceptance rose from 37% to 47.4%, with critical findings accepted at a rate of 50.3%. CodeRabbit enabled engineers to focus on higher-value architectural decisions while ensuring more contributors could ship code confidently.

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