Stop settling for static search. Learn how to build a dynamic product recommender using Red Hat OpenShift AI that combines hybrid semantic search, real-time LLM summaries, and personalized onboarding.
Join Peter Samouelian, Principal Software Engineer at Red Hat, as he demonstrates the Product Recommender AI quickstart. This video covers how to integrate embedding models like OpenAI CLIP for image-based search and how to use Red Hat AI Inference Server to generate real-time product pros and cons from user reviews. You will see how the system captures user intent during a multi-step interview process to create a truly customized landing page. Peter also explains how continuous user interaction data is used to fine-tune the recommender model, ensuring your enterprise AI evolves with your customers.
Red Hat AI quickstarts are a catalog of ready-to-run, industry-specific use cases for your Red Hat AI environment. Each AI quickstart is simple to deploy, explore and extend. They give teams a fast, hands-on way to see how AI can run solutions on enterprise-ready, open source infrastructure.
🚀 Explore the product recommender quickstart → https://docs.redhat.com/en/learn/ai-quickstarts/rh-product-recommender-system
📖 Read the technical deep dive → https://developers.redhat.com/articles/2026/01/20/ai-quickstart-product-recommender-openshift-ai
✨ Discover Red Hat OpenShift AI → https://www.redhat.com/en/technologies/cloud-computing/openshift/openshift-ai
#RedHatAI #AgenticAI #OpenShift
