This session explores how Azure Cosmos DB Vector Search enables high-performance multimodal Retrieval-Augmented Generation (RAG) for real-world predictive maintenance scenarios based on drone imagery. Using images of steel structures captured by drones, combined with historical inspection data and engineering documentation, the session demonstrates how multimodal embeddings can be used to detect structural anomalies and predict maintenance needs.

Multiple vector search and RAG system configurations are evaluated and compared, focusing on retrieval accuracy, latency, scalability, and multimodal relevance. Azure Cosmos DB consistently demonstrates best-in-class results for image-centric RAG workflows, particularly when working with large-scale multimodal datasets and similarity-driven reasoning.

Through practical architecture patterns and live demonstrations, attendees will see how Cosmos DB enables reliable, explainable, and production-ready multimodal AI systems-unlocking predictive insights for infrastructure maintenance and other image-intensive AI workloads.