Codebase Intelligence
Deep, queryable understanding of any codebase — grounded answers with citations, living docs, and impact analysis, over API + MCP.
“The brain that actually understands your code — searchable, explainable, and queryable by humans and agents alike.”
Role in the platform
The substrate and technical center of gravity. Master RAG-over-code once, power three products (ContextOS #1 and System Design Assistant #6 reuse it).
Overview
A platform that ingests a codebase and builds rich, retrievable understanding of it: semantic + lexical search, grounded Q&A, automatically maintained documentation and architecture maps, and impact analysis.
It is the RAG-over-code engine — the hardest, most defensible piece of the whole family — exposed as an API and an MCP server so any tool or agent can use it.
The problem
'Talk to your codebase' is validated, hot demand, but most tools are shallow — naive chunking, weak cross-file reasoning, no governance.
Onboarding, code understanding, and impact analysis are universal, expensive pains across large, legacy, and polyrepo codebases.
What it is
An ingest → AST-chunk → embed → hybrid-retrieve → graph-expand → re-rank → cite/answer pipeline backed by pgvector + full-text search and a symbol/dependency graph in Postgres.
Connected via a GitHub App; answers come back grounded with file:line citations, plus auto-generated docs and architecture maps that stay in sync with the code.
Key features
Tech stack
Why it's different
- Over-invests in retrieval quality — evaluated in CI (recall@k, MRR), 'I don't know' over hallucination.
- API/MCP-first and shared/governed, unlike per-user, editor-bound '@codebase' indexes.
- Deeper than editor indexes, lighter to adopt than heavy enterprise code-intelligence suites.