Knowledge Graphs for Enterprise AI: From Data Retrieval to Reasoning

Knowledge graphs represent enterprise entities such as customers, suppliers, accounts, devices, policies, and events alongside their explicit relationships, enabling AI systems to answer questions that depend on ownership, dependencies, permissions, and other connected context. Unlike vector databases, which retrieve semantically similar text, or relational databases, which can require complex joins for multi-step connections, graphs support traceable relationship-path queries and can incorporate current operational data. The material identifies fraud detection, customer intelligence, cybersecurity, supply-chain operations, and enterprise knowledge agents as prominent applications, where connected data can improve investigations, recommendations, incident response, disruption planning, and policy guidance. It describes GraphRAG as a hybrid retrieval approach in which vector search locates relevant content while graph queries supply entity relationships, constraints, and evidence to ground large language model responses. It also argues that organizations should begin with a focused, high-value use case and expand graph infrastructure as benefits are demonstrated, while presenting TigerGraph and its Savanna cloud platform as tools for deploying graph-and-vector AI workloads.

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