Building Graph-Grounded Copilots with Neo4j in Fabric: Real-Time Recommendations & Substitutions
Learn how integrating Neo4j Graph Intelligence into Microsoft Fabric transforms standard AI outputs into context-aware, highly accurate product recommendations and intelligent item substitutions.
In this session, we explore how traditional relational data structures limit AI agents, leading to generic or irrelevant recommendations. By grounding Microsoft Copilot with Neo4j's native Fabric integration, you can tap into graph algorithms to feed real-time graph context, market basket dynamics, and deep entity relationships directly into your Copilot workflows.
What You'll Learn:
- Graph-Grounded AI Architecture: Why traditional SQL queries fall short for recommendation engines and how Neo4j grounds Microsoft Fabric Copilots.
- Native Microsoft Fabric Integration: How to leverage Neo4j directly within Microsoft Fabric and OneLake to query complex relational data as a graph.
- Real-Time Recommendations & Substitutions: Using Neo4j graph algorithms (such as Node Similarity) to replace outdated co-occurrence logic.
- Hands-On Demo: Step-by-step walkthrough of connecting Neo4j graph datasets to Fabric Data Agents for context-rich Copilot responses.
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