RAG finds chunks. TrailGraph finds answers. Here's the difference.

Imagine asking your AI assistant: "How does lead qualification work?" RAG searches the vector store, pulls the top 5 chunks by cosine similarity, and hands them to the model. You might get three paragraphs about leads, one about opportunity stages, and one about case escalation — because they all share similar keywords. The model does its best to stitch them together. Sometimes it works. Sometimes you get a confidently wrong answer built from pieces that were never meant to be combined. To be clear — RAG is a proven, powerful pattern. For unstructured documents, broad search, and fast retrieval, it's hard to beat. But when knowledge has clear hierarchy and multi-level relationships, retrieval by similarity alone can lose the structure that makes the answer meaningful. So I built TrailGraph — an AI agent that navigates a knowledge graph step by step. It explores nodes, scores them for relevance, follows the most promising path, and only reads the full content when it's confident it foun