Graphify
An autonomous AI orchestration engine—designed to extract abstract syntax trees (AST) and build topological knowledge graphs of massive codebases.
Problem
Enterprise AI coding assistants struggle to understand massive, undocumented codebases because they lack structural context and rely purely on limited text windows. The challenge: standard embeddings fail to capture the hierarchical dependencies and execution flows required for deep architectural reasoning.
Architecture & Solution
The orchestration engine utilizes deterministic AST extraction. Parallel AI subagents traverse the codebase to map conceptual relationships. Graph mapping models the extracted data into a topological graph, while advanced algorithms execute clustering to group related domains without relying on vector embeddings.
Implementation Decisions
- Deterministic structure extraction — Parsing code via AST provides perfect, hallucination-free structural context before any LLM is invoked.
- Parallel concept mapping — Spawning concurrent subagents to analyze relationships drastically reduces the time required to map massive repositories.
- Topology-based clustering — Leveraging advanced graph algorithms enables the system to autonomously discover logical domains and microservices based purely on dependencies.