Multimodal Vectors for Instant, Personalized Product Discovery

Learn to build an autonomous product discovery system using multimodal embeddings and vector databases in this conference presentation from Qdrant's Vector Space Day 2025. Discover how to create a practical product vectorization and search pipeline that transforms raw product metadata including price, category, and imagery into structured assets for semantic search. Explore the implementation of an orchestrator that generates Vision Transformer (ViT) and sentence embeddings, with Qdrant serving as the vector database for instant retrieval. Understand how combining user queries with personal context enhances retrieval relevance and accelerates product discovery journeys. Watch a live demonstration of hybrid retrieval functionality featuring "screenshot in, product out" capabilities. Access reproducible workflows through shared n8n configurations and GitHub starter code provided as a community contribution. Gain practical patterns for building measurable, flexible personalization systems that deliver real impact beyond the typical LLM hype, with insights from two iCompetence speakers on creating autonomous product discovery solutions.