MongoDB GeoOps+: Backend Fulfillment Intelligence

This 3-hour backend workshop shows how to build a **real-time, explainable inventory and fulfillment engine with MongoDB Atlas.** The system combines geospatial proximity, semantic precedent via Vector Search, and reactive automation to drive reorder and substitution decisions with full traceability. Key technical components: **Schema design**: for warehouses, inventory_levels, shipments, past_incidents, substitute_products, reorder_requests, and audit_logs. **Geospatial fulfillment**: using 2dsphere indexes and proximity queries to find nearest warehouses with available stock. **Operational KPIs via Aggregation Framework pipelines:** (stock by region, aging inventory, shipment throughput, hotspot detection). **Reactive detection of inventory risk using Change Streams:** to flag low-stock and initiate downstream logic. **Automated decisioning service:** that generates reorder or substitution recommendations based on threshold rules enriched with historical context. **Semantic layer powered by Atlas Vector Search:** matching current demand patterns or stockout situations to similar past incidents and viable substitute products to inform fallback strategies. **Hybrid ranking:** that fuses geospatial score (proximity + availability) with vector similarity to rank fulfillment candidates. **Consistency & explainability** through multi-document transactions, versioned audit logs capturing provenance and vector similarity scores, and “why” payloads attached to every recommendation.