AI Codebase Intelligence

Problem

Engineering teams struggled to understand large, legacy codebases. New developers took weeks to become productive, and architectural decisions were made without full context of existing patterns and dependencies.

Context

A growing SaaS company with a monorepo containing multiple services, shared libraries, and years of accumulated technical decisions. Documentation existed but was incomplete and often outdated.

Challenges

• Codebase too large for any single engineer to hold in memory
• Documentation drifted from actual implementation
• Cross-team dependencies were difficult to trace
• Onboarding new engineers was slow and expensive

Approach

Built an AI-powered codebase assistant that indexes the repository, understands architectural patterns, and answers natural language questions about implementation details, dependencies, and design decisions.

Architecture

Vector embeddings of code chunks stored in pgvector, semantic search for relevant context retrieval, LLM synthesis with source citations, and a conversational interface integrated into the developer workflow.

Implementation

Implemented incremental indexing on git push, AST-aware chunking for better context boundaries, RAG pipeline with reranking, and guardrails to prevent hallucinated code references.

Outcome

Engineering teams reported significantly faster onboarding and more confident architectural decisions. Developers could ask questions about unfamiliar parts of the codebase and receive accurate, cited answers within seconds.

Technologies