AI systems that have to answer to a regulator need more than accuracy: they need a defensible audit trail. ATHERGraph delivers it.
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.
Decision provenance for clinical AI under FDA AI/ML SaMD guidance, and HIPAA-auditable retrieval: permission-aware, access-logged answers that respect the asking clinician's rights and leave a record.
AI decision lineage across every deployed model and agent: a single causal record ahead of EU AI Act obligations (Aug 2026), with board-level risk reporting drawn from the same graph the auditors examine.
Every solution above runs on one of these. Each opens a full catalog, with a dedicated page, diagram, and use cases for every one.
The graph database. Solutions for everything connected data unlocks:
The agentic AI framework. Solutions for putting agents to work:
Co-purchase and behaviour graphs drive relevance. Every suggestion comes with an explainable “because” path.
Topology-aware root cause: what depends on the failing thing. Blast radius before you reboot, not after.
Supplier → part → product dependencies as a live graph. Single-source risk and ripple analysis become one query.