Beetle AI

Beetle AI is an AI-powered code reviewer that analyzes entire repositories, detects bugs, and auto-suggests fixes — helping teams ship cleaner, faster, and safer code.

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Inspiration

While working at a fast-paced startup, I noticed a major problem — developers were pushing code faster than it could be reviewed. Even with tools like CodeRabbit, bugs and security issues often slipped into production. Manual reviews just couldn’t keep up with the speed of AI-assisted development tools like Cursor or Trae. That’s when I got the idea for Beetle AI — an AI-powered teammate that could deeply analyze the entire codebase, detect issues, and even suggest or apply fixes automatically.

What It Does

Beetle AI is an AI code reviewer that goes beyond pull requests. It performs full repository analysis, finding bugs, vulnerabilities, and quality issues across the entire project. It then creates GitHub issues or pull requests with suggested fixes, making code review faster and more reliable. It also reviews pull requests with full repository context, ensuring precise and meaningful recommendations.

How We Built It

Beetle AI is built on a Turborepo architecture using Next.js and Express, with MongoDB as the main database. The deep code analysis is handled in a Python environment running inside secure E2B sandboxes, ensuring isolation and safety while scanning repositories. The backend runs on AWS EC2 via Docker, and the frontend is deployed on Vercel. We’re also planning to add vector embeddings and semantic search to store analysis results for faster, context-aware insights.

Challenges We Ran Into

  • Handling large repositories efficiently without overloading compute resources.
  • Creating a robust agentic workflow that maintains context across multiple files.
  • Ensuring AI-generated fixes match the existing code style and logic.
  • Managing security while executing and analyzing dynamic code in a sandboxed setup.

What We Learned

We learned how to design AI agents that can understand, review, and fix code intelligently. The project deepened our understanding of secure sandboxing, LLM orchestration, and real-world AI application in developer workflows. Most importantly, we learned that speed and reliability are the two things developers value most — and AI can empower both.

What’s Next for Beetle AI

Next, we plan to:

  • Launch the first public beta within the next 2 months.
  • Add IDE integration, allowing developers to review and fix issues directly from their editor.
  • Implement vector-based memory and semantic search for deeper analysis and faster recall.
  • Onboard the first 100 paying teams and reach $50K–$100K ARR within 6–10 months of launch.

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