From risk management to recommendations, context is key. Seven solutions, one engine. Click any of them for the full picture: the diagram, the use cases, and how it works on a graph.
Unified knowledge layers across documents, databases, APIs, and entities: one queryable model of what your organization knows.
Community detection, centrality, pathfinding, dependency mapping, and anomaly detection. Structure as signal.
Graph + vector + BM25 retrieval with LLM reasoning on top. Answers carry citations and the path that produced them.
Hidden relationships, circular transactions, and identity networks, surfaced as explainable paths, not black-box scores.
Co-purchase and behaviour graphs drive relevance. Every suggestion comes with an explainable “because” path.
Users, devices, permissions, and attack paths in one graph. Find the route an attacker would walk before they walk it.
Accurate, contextual, explainable search over everything your organization holds, permission-aware by construction.
AML failures carry criminal liability and nine-figure fines. Under BSA/AML and SR 11-7, examiners are asking for AI decision documentation now. ATHERGraph turns every flagged transaction into an artifact you can hand across the table.
Walk a flagged transaction back through CAUSED_BY edges to source data: an examination-ready audit trail for 100% of flagged transactions.
K-hop suspicious-path detection: account → device → merchant → beneficiary. Explainable fraud paths, not black-box scores.
Augment NICE Actimize / Oracle FCCM / FICO alerts with structural explanations their post-hoc tooling cannot produce.
Time-travel queries reconstruct exactly what was known when the decision was made.
Entry point: augment your existing AML alert layer in a 90-day deployment. Read the fraud & risk page →
Decision provenance for clinical AI: every model output traceable to the data and version that produced it.
Permission-aware, access-logged answers: retrieval that respects the asking clinician's rights and leaves a record.
Lineage across every deployed model and agent: a single causal record ahead of EU AI Act obligations (Aug 2026).
Reports drawn from the same graph the auditors examine, so there is no reconciliation between what the board sees and what the examiner finds.
Transform your data into knowledge. We'll walk your use case end to end on real data. Your data never leaves your laptop during the demo.