What is a
knowledge graph?
A knowledge graph stores information as entities (people, projects, concepts) and the relationships between them, dots and lines, so software can follow those connections to answer questions, instead of just matching keywords.
The insight is in
the connections.
A pile of documents can tell you facts. A knowledge graph tells you how those facts relate, which client said what, which decision led to which outcome, how a position connects to everything around it. That's what lets AI reason over your business instead of guessing.
It's the backbone of a digital brain. We built one for a political party from fifteen categories of data (see the build), and another from 30+ hours of interviews (the advisor brain). You can build your own with Brain Graph.
An LLM, but it knows
how things relate.
One way people describe a knowledge graph after they see their own: it is like your own LLM, but smarter, because it knows how things relate to each other. It is a specific kind of database, distinct from a spreadsheet, a SQL database or a vector store. Where those hold rows or loose chunks, a graph holds entities and the edges between them.
The most interesting version is your internal knowledge: the relationships inside your own business that no general model has ever seen.
Common questions.
How is it different from a normal database?
A database stores rows in tables; a knowledge graph stores entities and the explicit relationships between them, so you follow connections instead of joining tables.
Is a knowledge graph the same as RAG?
No, and they're complementary. See knowledge graph vs RAG.
See one in action.
Build a knowledge graph from your own content, free, or come build one with us, live.