Description
With 80% of enterprise data being unstructured, traditional keyword-based search is no longer sufficient for modern information retrieval. This talk explores the paradigm shift brought by vector search technology, which has consistently delivered a 20-30% boost in discovery accuracy across diverse applications. By harnessing dual encoder neural networks and open-source models like BERT, both documents and user queries are mapped into high-dimensional embeddings, enabling semantic search that understands meaning beyond mere keywords.
Real-world implementations have already demonstrated remarkable impact: digital marketplaces have improved product discovery by 35%, academic platforms have enhanced research paper matching by 40%, and healthcare systems have reduced clinical information retrieval time by 60%. Enterprise knowledge bases have also reported a 45% increase in employee information access efficiency.
This session will provide a technical deep dive into approximate nearest neighbor (ANN) algorithms such as HNSW and IVFPQ, which power fast and scalable vector search across billions of embeddings. Attendees will gain insights into choosing the right embedding models, optimizing index structures, and deploying vector search solutions across industries such as legal research, e-commerce, media, and healthcare—where implementations have driven up to 50% improvements in discovery accuracy and 25% enhancements in content recommendations.
Join us to explore how vector search is reshaping search experiences and unlocking the next frontier of intelligent information retrieval.