Communities, influence, paths, and anomalies: insights that live in the shape of your data, not in any single row. AtherGraph runs the classic graph algorithms directly on live data.
From raw connections to decisions, with every step inspectable.
The most valuable patterns in enterprise data are relationships, not rows: the supplier everything quietly depends on, the accounts that behave like a ring, the team that actually ships together. Graph algorithms read that structure directly (no feature engineering, no flattening), and AtherGraph runs them on live data, so the answer reflects now, not last quarter's export.
Find the clusters that matter: customer segments that actually behave alike, fraud rings that share infrastructure, teams that really work together.
Which supplier, server, or account is quietly critical? Centrality algorithms surface the nodes whose failure or fraud hurts most.
Shortest paths, all paths, dependency chains: answered natively, without recursive JOIN gymnastics.
Structure that does not fit (a new edge pattern, an impossible triangle, a sudden hub) is flagged as it forms, with the evidence attached.
How it's built, and the reproducible numbers behind it.
SDKs, query examples, and local setup: what's live and what's coming.
Five phases, honestly labelled: shipped, live, in development.
8 questions, free gap analysis against EU AI Act and SR 11-7.
Book a session and we’ll walk this use case end to end on real data: architecture, queries, and what a 90-day path looks like for your team.