Semantic Search for Legacy Code

Reduce onboarding time by 40%. AI-powered search that understands your legacy codebase.

Powered By

MongoDB Vector Search
MongoDB
Vector Search
MongoDB Vector Search
Tavily
AI Search
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Semantic Understanding

Ask questions in natural language. Our AI understands intent, not just keywords.

⚡

Instant Answers

Get synthesized answers from your codebase and external documentation in seconds.

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Legacy Code Friendly

Perfect for undocumented code, weird decisions, and obscure syntax from older languages.

The Problem

  • •40% of dev time is spent working with legacy code
  • •Onboarding takes weeks or months for complex codebases
  • •Lack of documentation makes understanding code fuzzy and time-consuming
  • •Older syntax and weird architectural decisions are hard to parse

How It Works

1

Ingest Code

Paste a GitHub file URL. We summarize and embed it into our vector database.

2

Ask Questions

Ask about syntax, architecture, or get an overview. Our AI searches semantically.

3

Get Answers

Receive synthesized answers combining your code and external documentation.

Start Searching →

No signup required. Just paste a GitHub URL and start asking questions.

Example Usage:

User: https://github.com/llvm/llvm-project/blob/main/llvm/include/llvm/ADT/StringRef.cpp
AI: ✓ File ingested and embedded successfully
User: Explain how the find function works in the StringRef.cpp file
AI: The find function uses std::memcmp for efficient memory comparison...