Greptile: AI That Actually Understands Codebases

Greptile is building AI that understands entire codebases. Each week, Greptile reviews 5–8 million lines of code for customers and surfaces tens of thousands of bugs that would otherwise slip through human review. Fresh off a $25M Series A round, they're on a mission to kill the bug. In an interview with YC Partner Brad Flora, co-founder and CEO Daksh Gupta shares how he and his team discovered that large language models are often better at reading code than writing it, why code review is the most powerful use case for that ability, and how Greptile is helping developers spend less time catching bugs and more time creating software. Learn more about Greptile: https://www.greptile.com Chapters: 00:00 – Intro & What Greptile Does 00:18 – Why Code Review Matters 01:00 – Humans + AI in the Loop 01:40 – 20,000 Bugs Found Every Week 02:10 – Why Now: LLMs and Code 03:20 – From Code Q&A to Bug Detection 04:20 – What Developers Really Think of AI Tools 05:40 – Advice for Founders

Brad FlorahostDaksh Guptaguest

Sep 22, 20256mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

Greptile uses AI to review codebases, catching bugs faster

Greptile automates large portions of pull request review to save senior engineers time and catch more defects than human-only processes.
The product is positioned as “humans + AI,” where AI handles tedious mistake-finding while humans focus on mentorship, architectural direction, and long-term codebase evolution.
Greptile reports reviewing 5–8 million lines of code weekly and surfacing roughly 20,000 bugs per week across customer PRs.
The company’s “why now” hinges on LLMs being especially strong at reading code, and on rising societal dependence on resilient software systems.
The team evolved from codebase Q&A (understanding code) to bug detection (persistent value), after experiencing firsthand how hard large unfamiliar codebases are to navigate.

IDEAS WORTH REMEMBERING

5 ideas

Use AI to compress review time, not to remove reviewers.

Greptile’s value proposition is speeding up and improving code review quality, while keeping humans responsible for judgment calls, mentorship, and design direction.

Code review is more than bug-finding—retain the human “bigger picture.”

Daksh frames reviews as an opportunity for mentorship and forward-looking architecture, areas where today’s AI lacks the “clairvoyance” of senior engineers.

LLMs’ near-term advantage is code reading, not code generation.

Greptile bets on models’ strongest current capability—understanding and analyzing code—rather than relying on them to author large amounts of new code correctly.

If you can teach a model the codebase, bug detection becomes the highest-leverage use.

The team discovered that codebase understanding is a means to an end: preventing defects before changes land, which creates ongoing value beyond onboarding or Q&A.

Large-codebase comprehension solves a pain that teams repeatedly feel.

They started from a real scenario—losing the developer who wrote the frontend—highlighting how knowledge gaps and scale make engineering hard even for capable teams.

WORDS WORTH SAVING

5 quotes

"We're building AI that understands large codebases."

— Daksh Gupta

"Code reviews... serve a purpose greater than just detecting bugs. It's a opportunity for mentorship."

— Daksh Gupta

"This past week, we revealed twenty thousand bugs across the pull requests."

— Daksh Gupta

"They're... better at reading code than they are at writing it."

— Daksh Gupta

"You just really should focus on building products that your customers love."

— Daksh Gupta

AI-assisted pull request reviewHuman-in-the-loop code reviewBug detection at scale (20k/week)LLMs stronger at reading than writing codeCodebase Q&A and context buildingDeveloper adoption skepticism vs enthusiasmFounder focus and avoiding distractions

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