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Microsoft Fabric and Neo4j Graph Intelligence combine to solve the relational database bottleneck that prevents AI agents from accessing rich, multi-hop business context for real-time personalization. By projecting data into connected graph models, this architecture enables Copilots to accurately navigate complex customer-product relationships without relying on isolated transactional rows or failing SQL queries. This dual-engine approach directly addresses critical retail challenges like wasted promo spend and missed cross-sell opportunities by grounding AI recommendations in interconnected intelligence.

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Beyond the Banana: Driving real-time recommendations with graph-grounded Copilots in Microsoft Fabric

Neo4jSeptember 23, 20264 min read

Retailers rarely lack data. Between customer profiles, point-of-sale transactions, inventory levels, and promo schedules, modern enterprises generate terabytes of signals daily. The issue is that legacy relational architectures keep this information locked in siloed systems across POS platforms, inventory systems, and CRM applications. When organizations deploy Copilots or AI agents to handle personalization, they naturally run into an architecture bottleneck: AI agents need rich, interconnected business context, not just isolated transactional rows. Relational tables…

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