CodeCortex
Persistent codebase knowledge layer for AI agents. Pre-builds architecture, dependencies, coupling, and risk knowledge so agents skip the cold start and go straight to the right files.
The Problem
Every AI coding session starts with exploration — grepping, reading wrong files, re-discovering architecture. On a 6,000-file codebase, an agent makes 37 tool calls and burns 79K tokens just to understand what's where. And it still can't tell you which files are dangerous to edit or which files secretly depend on each other.
The data backs this up:
- AI agents increase defect risk by 30% on unfamiliar code (CodeScene + Lund University, 2025)
- Code churn grew 2.5x in the AI era (GitClear, 211M lines analyzed)
The Solution
CodeCortex eliminates the cold start. It pre-builds codebase knowledge — architecture, dependencies, risk areas, hidden coupling — and injects it directly into your agent's context (CLAUDE.md, .cursorrules, etc.) so agents have project knowledge from the first prompt.
