Whitepapers on data
architecture for AI

SurrealDB gives us a foundation where we can unify semantic search, knowledge graphs, and AI-driven decision making without stitching together multiple systems. Collapsing responsibility into SurrealDB has become our default engineering posture.

As a startup, we didn't want to fragment our stack every time we add a capability… instead we got graph, vector, and full-text from one engine. What surprised us was how quickly we stood up a multi-layered, graph-based memory model for our AI agent.

An assertion edge carries the voice that made it, the document and page it came from, the figure it states and the dates it holds for. SurrealDB stores that as one record and queries it in one statement. We did not write middleware to hold the ontology together, because there is nothing to hold together.