In this session, we’ll explore what it really takes to build scalable multimodal and personalized RAG systems for real-world applications. How do you search across images and text simultaneously to surface the most relevant answers in life sciences or financial services? How do leading e-commerce platforms combine semantic understanding, inventory, pricing, user behavior, and personalization in real time?
We’ll break down the core architectural challenges behind modern retrieval systems, including latency, data freshness, hybrid search, ranking orchestration, and combining structured, unstructured, and vector-based data within a single query pipeline.
The session will also demonstrate how Vespa powers large-scale AI applications by unifying retrieval, ranking, vector search, and serving in a single platform — enabling companies such as Spotify, Perplexity, and RavenPack to deliver real-time AI experiences at scale.
Through real-world examples from life sciences, financial services, and e-commerce, you'll learn how organizations are moving beyond basic RAG architectures to build AI systems that are accurate, adaptive, and production-grade.
