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Global HR policy automation is a key part of Human Capital Management transformation, yet employees often struggle to get clear and timely answers to policy questions. This beginner-friendly session demonstrates how to build a Global Policy Assistant using GraphRAG and Neo4j. You will learn how to model HR policies as connected data, combine graph-based retrieval with vector search and large language models, and deliver accurate, context-aware responses. The session focuses on practical implementation steps and real-world HR data challenges.
What You Will Learn
How to structure HR policies as connected data using a graph model
How to combine graph-powered retrieval, vector search, and LLMs for accurate responses
A practical approach to building an AI-powered assistant for global HR policy use cases
Who Should Attend
Developers and engineers new to GraphRAG
Data engineers and platform engineers
HR technology professionals
Architects exploring AI-driven knowledge assistants