Greptile AI Achieves 82% Bug Detection Rate in "Golden Set" Evaluations, CEO Highlights Broader Value

San Francisco, CA – Daksh Gupta, CEO of Greptile, recently emphasized the significance of objective evaluation functions for AI tools, particularly in the context of their "golden set evals" for bug detection. Gupta stated that while these evaluations show strong performance, they only represent a portion of Greptile's overall capabilities. The company maintains a proprietary dataset of real-world bugs to measure its AI's effectiveness in identifying issues.

Greptile, an AI-powered platform, positions itself as an "AI expert on any codebase," designed to assist developers and teams in understanding and reviewing large, complex codebases. Its primary function involves automating pull request analysis, enforcing coding standards, and facilitating cleaner code delivery by providing comprehensive context. The company, founded by Soohoon Choi, Vaishant Kameswaran, and Daksh Gupta, aims to streamline software development workflows.

The "golden set evals" mentioned by Gupta refer to Greptile's internal benchmarks, which rigorously test the AI's bug-catching prowess. According to their 2025 AI Code Review Benchmarks, Greptile achieved an 82% catch rate across 50 real-world bugs sourced from major open-source projects. This performance significantly outpaced competitors, with Greptile detecting 41% more bugs than the second-place tool in their evaluation.

Gupta clarified that "catching bugs is a subset of greptile's value prop, by extension this eval is incomplete by design." Beyond bug detection, Greptile offers a suite of features including complete codebase context understanding, conversational AI capabilities for fix suggestions, and reinforcement learning from user feedback. These functionalities allow the AI to provide deeper insights, generate PR summaries, and adapt to specific team coding standards.

Greptile's comprehensive approach aims to enhance overall code quality and accelerate development cycles by addressing various aspects of the code review process. The company's focus extends beyond mere error identification to providing a holistic understanding of code, contributing to faster releases and improved developer productivity in a competitive AI code review market.