YC Root Access

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

BFBrad FlorahostDGDaksh Guptaguest

Sep 23, 2025·6m·Watch on YouTube ↗

📖 CHAPTERS

1. 1

   Greptile’s core product: AI that understands large codebases

   Daksh Gupta explains that Greptile builds AI aimed at understanding entire, large codebases—not just generating snippets. Teams use it primarily for pull request review and for answering questions about how their code works.

2. 2

   Why code review is expensive—and where Greptile creates value

   Greptile targets a major time sink for senior engineers: reviewing pull requests and scanning for issues. The product aims to shorten review cycles while catching bugs that humans commonly miss.

3. 3

   Humans + AI in the loop: code review as mentorship and foresight

   Customers generally don’t want fully autonomous code review; they want an AI assistant that handles the tedious parts while humans handle judgment. Daksh emphasizes that reviews are also about mentorship and aligning code with the future direction of the codebase.

4. 4

   Scale and results: millions of lines reviewed and 20,000 bugs per week

   Daksh shares usage and impact metrics, highlighting the tool’s scale and the volume of bugs it surfaces. The value is framed as catching issues that would otherwise slip into production and cause downstream problems.

5. 5

   Why now: LLMs are currently better at reading code than writing it

   Greptile’s timing is tied to the maturity of language models, which the team observed early (since around the DaVinci-2 era). Daksh argues LLMs’ strongest near-term leverage is interpreting and analyzing code, making review and bug detection a natural fit.

6. 6

   From codebase Q&A to bug detection as the “most useful” outcome

   The team initially built codebase question-answering by loading a repository and allowing queries like “How does XYZ work?”. They realized that once a model can understand a codebase, the highest-value application is systematically finding bugs before code ships.

7. 7

   Origin story: struggling with a large front-end codebase and onboarding pain

   Greptile’s idea emerged from a practical problem in college projects: a teammate owned the entire front end and then left, leaving the rest unable to work effectively. That experience mirrored industry realities where engineering difficulty often stems from navigating complex, sprawling codebases.

8. 8

   Bugs are the persistent problem—and software resilience matters more every year

   Daksh explains a key product thesis: codebase unfamiliarity is temporary, but bug-finding remains constant. He connects this to rising societal dependence on software (grids, supply chains), raising the stakes for resilience and correctness.

9. 9

   What developers think about AI tools: adoption varies wildly

   Daksh notes he was surprised by the spread in developer attitudes toward AI. Some highly capable engineers reject AI tools outright, while others enthusiastically adopt them; Greptile evaluates customer fit partly based on conviction that AI can improve the business.

10. 10

    Not “just another AI coding tool”: distinguishing review/analysis from codegen

    Early on, Greptile was often lumped into a generic “AI coding” bucket. Over time, as organizations gained experience with different AI tools, they became better at distinguishing between code generation and augmenting specific workflows like review and analysis.

11. 11

    Founder advice: ignore distractions and build what customers love

    Daksh’s main advice is to stay focused because distractions are constant and seductive. He recommends prioritizing building a product customers truly need and love over the many secondary activities that feel important but matter less.

12. 12

    How to try Greptile: quick signup and GitHub connection

    The conversation closes with a simple call to action: Greptile is free to try, with a fast onboarding flow. Users can sign up on the website and connect GitHub to start using it quickly.