Chapter 5
Additional use case examples
The same pattern, across industries. Three short examples show how a contextual data layer supports production AI where relationships, time, governance, and multimodel data all matter at once.
01
Financial Services
Real-time fraud detection
Time
Relationships
An AI fraud-detection agent monitors transactions and surfaces high-risk patterns to analysts through real-time scoring and traversal.
GraphRAG connects accounts, transactions, and merchants across siloed systems; temporal modeling flags synthetic identities through 6-degree relationship patterns before settlement.
Outcome
Fraud rings flagged before settlement — full relationship trace on every alert.
02
Networking • HPE Aruba
Global network operations on one platform
Time
Multimodel
HPE Aruba delivers secure networking for millions of devices worldwide. Managing that scale used to mean six siloed databases. They consolidated onto the Arango Contextual Data Platform — unifying graph, document, and key-value — and retired six systems.
Outcome
6 → 1 database systems. One platform, one governance model.
03
Regulated enterprise
Audit-ready compliance reporting
Provenance
AI-ready
An AI compliance assistant lets auditors and risk teams query policies, trace decisions, and generate audit-ready documentation. Arango RBAC and provenance ensure every recommendation cites source documents and policies; AutoRAG delivers auditable responses.
Outcome
Regulatory audits passed with zero findings.
