Beyond the Banana: Driving real-time recommendations with graph-grounded Copilots in Microsoft Fabric
Blog post from Neo4j
Microsoft Fabric and Neo4j Graph Intelligence are presented as a combined approach for improving retail AI recommendations by unifying siloed sales, customer, inventory, and promotional data in OneLake Delta tables and modeling their relationships as graphs. The approach addresses limitations of relational queries and simple co-purchase analysis, which can overemphasize universally popular products such as bananas, miss subtle purchasing affinities, and struggle with novel cart combinations or complex multi-hop queries. Neo4j’s Graph Data Science capabilities, including Node Similarity algorithms, analyze product neighborhoods and overlapping relationships to identify more meaningful complements, substitutes, and cross-sell opportunities. Fabric Data Agents can translate natural-language business questions into Neo4j Cypher queries, providing Copilots with deterministic, relationship-rich context while retaining Fabric’s governance, single sign-on, and tenant security controls. Organizations can deploy the integration through Fabric’s Workload Hub or Azure Marketplace to build graph-grounded recommendation systems without separate infrastructure.
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