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Re: Hey AI, Where's My Purchase Order? — A Procurement Ontology
Thank you so much, bhavanapatil! Really glad the use case resonated - we wanted to show how Ontology can turn raw procurement data into something business users can actually talk to naturally. 🙌 Great suggestion on sample prompts and visuals - we'll definitely look into adding those to make the experience more tangible. Appreciate the feedback! 🚀Re: Hey AI, Where's My Purchase Order? — A Procurement Ontology
Love this direction bhavanapatil ! The multi-agent setup you've outlined is exactly where we see this heading - a PO Tracking Agent, Supplier Risk Agent, and Finance Agent collaborating through the ontology to deliver end-to-end automation. The "Where is my PO?" scenario is a perfect example - instead of just fetching a status, the agents can proactively flag risks and recommend next best actions. Would be exciting to build this out as a next phase. Let's explore this further! 💡Hey AI, Where's My Purchase Order? — A Procurement Ontology
Problem A leading Indian pump manufacturing company with 5,000+ product models and global presence across 120+ countries built a modern analytics foundation using Microsoft Fabric integrated with their ERP system. Dashboards were deployed across Procurement, Inventory, Production, and Finance — yet a fundamental challenge persisted. Procurement and supply chain teams struggled to extract insights without navigating complex dashboards and technical report structures. Users think in terms of suppliers, materials, purchase orders, and invoices — but data was organized around ERP tables and technical field names. Why Fabric IQ – Ontology Fabric IQ Ontology bridges this gap by introducing a business-centric semantic layer that models how the organization actually speaks about its data. Six core business entities — Company, Supplier, Product, Material, PurchaseOrder, PurchaseInvoice — are defined with real-world procurement relationships: Relationship Description Company → manufactures → Product Company produces finished goods Company → buys Material from → Supplier Sourcing of raw materials Supplier → delivers → Material Material delivery by vendor Supplier → fulfills → PurchaseOrder Order fulfillment Material → used to manufacture → Product BOM traceability PurchaseOrder → generates → PurchaseInvoice Invoice generation Company → settles → PurchaseInvoice Payment settlement PurchaseOrder → replenishes → Material Stock replenishment Instead of forcing users to adapt to data models, Fabric IQ Ontology allows data to adapt to the business — enabling natural, conversational interaction with procurement data. Business Outcomes Metric Before After Procurement query resolution 2–3 hours (manual dashboard navigation) Conversational Supplier-material traceability Cross-team coordination required Instant graph traversal Self-service adoption by procurement team Low Self-service ready Key Takeaways Lakehouse unifies procurement data; Ontology unifies procurement meaning Six business entities and eight relationships model the full procure-to-pay lifecycle AI agents can now reason across suppliers, materials, orders, and invoices — not just query tables The ontology transforms analytics from dashboard-driven to conversation-driven Ontology URL: Procurement Ontology
