Entity relationships and resolution signals, live
JetGraph can hold a working knowledge graph of entities and shared attributes for real-time queries. It is not a complete master-data or ontology platform.
Entity relationships
People, organizations, accounts, devices, identifiers, and documents as nodes; typed edges for ownership, employment, co-occurrence, shared identifier, and so on. Cypher MATCH is the exploratory interface; feature APIs remain for O(1) counts and flags.
Entity resolution
Resolution is usually a pipeline (blocking, similarity, clustering). JetGraph’s role is to store resulting same-as / shared-attribute links and to serve them at decision time: “how many identifiers already cluster with this account?” HyperLogLog and neighbor lists help describe cluster fan-out. The matching algorithm itself still lives in your ER service.
Graph enrichment
As streams arrive, upsert nodes and edges so the next decision sees enriched context. Snapshots persist the working graph. Heavy historical enrichment jobs can write into JetGraph rather than making the engine crawl object storage.
Shared attributes and connected clusters
Shared email, phone, device, or registration IP edges are the same pattern as fraud rings. Cardinality and short traversals find connected clusters without claiming JetGraph runs global connected-components at warehouse scale.
Real-time graph signals
Once the knowledge graph is in memory, fraud and identity flows can reuse it: novelty of a new identifier link, velocity of attachments to a cluster, neighbor risk if any member is flagged. That is the product’s actual center of gravity — real-time graph intelligence — with a knowledge-graph-shaped schema.