Modernizing Legacy Search with Semantic Retrieval in the AI Era | Qdrant vs Elastic Demo
Many search systems excel at keyword retrieval but struggle to capture meaning. Relevance improvements often require major rewrites…or so it seems.
Are you searching for a practical bridge to modern, semantic retrieval without tearing down what’s already been built?
As AI-driven applications move beyond keyword search into semantic and multimodal search, many teams are hitting structural limits in Java-based search engines. This webinar walks through those limits, explains how Rust-based systems address them, introduces semantic and vector-native search as the new retrieval paradigm, and shows how to build this in practice with Qdrant.
In this session, we’ll show you the in's and out's of semantic with Qdrant.
We will cover:
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Java-based search vs Rust-based search; semantic and vector-native search as the new paradigm; building semantic retrieval with Qdrant; a live demo; and migration and coexistence tips.
You’ll also learn about:
- hybrid architectures, reranking, real-world latency considerations, and more.
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Who should attend:
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Teams hitting the limits of keyword-only search and looking to modernize with dense retrieval and hybrid methods.
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Engineers & architects working with Elastic or OpenSearch
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ML engineers building RAG, semantic search, or hybrid systems
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Teams exploring dense retrieval but worried about performance or integration
Why This Matters
Dense vector search is the new standard, but most search stacks aren’t built for it. Qdrant lets you build a semantic layer that plugs into your existing architecture, giving you modern retrieval without the risk.