graphs

One graph. Every relation.

Building, pipeline queued

Semurg Graphs is the graph and knowledge-graph surface over the universal substrate: entities and relations as the same uniform containers, traversed as graph nodes on disk. No separate graph store: the graph is a subgraph of the one data model. A local model reads your documents and proposes entities and relationships into a governed graph, with provenance on every fact, contradictions kept side by side rather than silently resolved, and a review view over the low-confidence cases. Because it lives on the out-of-core substrate, it scales past memory.

What you will build here

Knowledge graph from your documentsEntities, relations, and provenance proposed by a local model into a governed graph, on-premise, with contradictions kept side by side for review.

No per-item human curationThe defensible claim, with a footnote for per-domain tuning, a seeded vocabulary, and expert sign-off on the schema in regulated deployments. Not fully automated, and not undisputed.

Out-of-core at knowledge scaleThe graph rides the same substrate that deep-traverses a graph bigger than memory, so a large corpus is not bounded by RAM.

Honest status

✓Done, universal graph engine: pages are graph nodes, k-hop traversal out-of-core (see the board).
●In progress, ingest and schema surface for named ontologies.
○Queued, the knowledge-graph build and query pipeline and its public surface.

We will publish extraction quality and recall on a standard reference set before any accuracy figure ships. A comparison to a closed proprietary knowledge platform is a capability and positioning comparison, not a reproducible benchmark. Best fit: government and sovereign intelligence, legal and case-heavy work, and enterprise knowledge management, on-premise.

Until the pipeline is live and measured, this page says so plainly. The graph primitives it will ride on are already real and reproducible today: run the k-hop board yourself.