Inside the Enterprise Knowledge Graph: how SNAP turns scattered files into reasoning
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An enterprise knowledge graph connects every note, PDF, and deck into nodes your AI can reason across. Here's how SNAP builds and queries it.
Most "AI search" tools bolt a chatbot onto a folder. An enterprise knowledge graph does something different: it turns every artifact your teams produce into connected nodes an AI can reason across. Here's how SNAP builds that layer.
From files to nodes
SNAP ingests the material your teams already generate — pages, PDFs, decks, screenshots, and voice notes — and extracts entities, claims, and relationships. Each becomes a node in your graph, with edges that capture who wrote it, when, and how it relates to other work. Nothing is flattened into a single embedding blob; the structure is preserved.
For a closer look at how source material becomes grounded context, see Inside SNAP's Source Layer.
Reasoning across the graph, not just retrieving from it
A vector store can find passages that look similar to your query. A knowledge graph lets the model traverse relationships — following a customer complaint to the product decision it influenced, then to the roadmap doc that shipped six months later. That traversal is what surfaces answers a task-level assistant never would.
Role-scoped, cited, and yours
Every answer is scoped by role, cited back to source nodes, and traceable. The graph itself stays inside your walls — it's your asset, not the model vendor's. That's the mechanic behind the shared memory layer executive advisors run on.