Moving GenAI from pilots to production brings two challenges: hallucinated outputs and spiraling retrieval costs. Business value requires factual accuracy, data lineage, and governance across systems. Discover how enterprise knowledge graphs enable trusted, cost-effective GenAI. By pairing LLMs with Neo4j through Graph-RAG, teams convert intent into deterministic graph queries. This grounds responses in validated facts with source provenance while reducing token burn. See how CDAOs establish real-time AI vigilance across data stacks and accelerate time to value without legacy refactoring. Key takeaways: → Unify disparate data with complete lineage: Connect structured and unstructured data into a business-aligned knowledge graph that preserves end-to-end source provenance. → Eliminate hallucinations via graph-RAG: Anchor LLMs to deterministic graph data, transforming models from unpredictable fact generators into auditable reasoning engines. → Operationalize real-time AI vigilance: Leverage graph-based context and memory to monitor accuracy, enforce governance, and improve models across SQL, NoSQL, and web pipelines. → Optimize genAI tokenomics: Slash token burn and latency through precise retrievals and graph query caching. Accelerate speed to value: Integrate graph capabilities into existing applications and pipelines to onboard new data sources without legacy refactoring.