Project products into the canonical model
See how content projects into one canonical model as typed, content-addressed nodes rather than separate silos - Cogitave's Core is the worked example - and how the fact registry's cite-not-restate rule and fact-drift scanner keep one owner per fact across your estate.
Units5
Duration25 min
Levelintermediate
By the end of this module, you'll be able to:
- Explain why an AI-native org treats docs, standards, and learn content as projections of one graph rather than separate stores - with Cogitave's estate as the worked example.
- Describe how a source document becomes a queryable node: the ACQUIRE-to-PUBLISH pipeline, its UID and content-addressed contentHash, and the closed edge set that links it in.
- State the fact registry's cite-not-restate rule - one owner per fact, everything else points - and what the fact-drift scanner checks for.
- Read the Day-0 status of Cogitave's Core accurately: the registry and scanner run on the mirror as plain files today, and full graph projection lands when Core runs.
Prerequisites
- Completion of the first three modules in this path (the canonical node/edge model, the query interface, and the native MCP interface) is recommended but not required.
- No prior graph-database experience needed.
Units
- 01Introduction4 min
- 02How content projects into the canonical model7 min
- 03The fact registry7 min
- 04Knowledge check4 min
- 05Summary3 min
Related
- The one-model architecture Explain why an AI-native organization runs on one canonical property graph that humans and agents both query, what its node/edge model and identity scheme look like, and why a labeled property graph is the right substrate. Cogitave's Core is the concrete example.
- A native MCP interface to the canonical model Read a canonical model's native MCP interface as the source of truth for how humans and agents query one org's estate - why the surface is native rather than adapted, the protocol contract it commits to, and the tools and resources you call. Cogitave's Core is the worked example.
- Query your canonical model Learn how a query layer resolves a request against one canonical graph - lexical BM25 and dense HNSW vectors fused by Reciprocal Rank Fusion, a graph-aware rerank and ranking signals, and a bounded, read-only profile agents call directly. Cogitave Query is the reference implementation you'll trace.