How To Modernize Legacy Search with a Semantic Layer
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?
In this session, we’ll show how you how to layer semantic search with Qdrant on top of existing systems, such as Elastic/OpenSearch. You’ll learn:
- hybrid architectures,
- indexing patterns,
- reranking,
- and real-world latency considerations.
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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.
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