Beyond LLMs: Building Smarter Search with Superlinked & Qdrant

​Designing Smarter Search: Real-Time, Multi-Attribute Retrieval with Superlinked and Qdrant

Modern users expect more from search. Whether booking a hotel or browsing a product catalog, they want results that reflect complex, nuanced preferences like: “affordable luxury hotels near the Eiffel Tower with great reviews and free parking.” Meeting that expectation requires more than just semantic similarity.

In this session, you’ll learn how to build real-time, multi-attribute search applications using Superlinked and Qdrant. We’ll explore a next generation of retrieval systems that combine semantic understanding with structured reasoning across different data types.

​Using the open-source hotel search demo as our guide, we’ll show:

1. How to work with multi-attribute data and the advantages this gives you
2. How to run semantic + structured queries over dozens of hotel filters
3. How to adapt scoring based on what your users care about (lower prices, higher ratings, amenities)

This session is ideal for teams building recommendation systems, AI-native search, or RAG applications. Whether you’re in e-commerce, travel, or SaaS, if your users expect intelligent, context-aware results, this is for you.

​By submitting, you agree to Qdrant's Privacy Policy and allow Qdrant to store and process the information submitted above to provide you with the content requested.