Lucidworks, the leading enterprise search and product discovery solutions provider, today announced the general availability of its Neural Hybrid Search™ product solution. For companies struggling to offer an improved digital experience, better search relevance is critical.
The time commitment and cost to improve search relevance can be staggering to even the most committed companies. Neural Hybrid Search accelerates digital product discovery relevance by speeding up implementation and removing relevance management complexity.
During the past year, Neural Hybrid Search clients achieved a 91% reduction in zero-results queries, a 10%-15% Mean Reciprocal Rank improvement, and 20%-30% less configuration time due to the reduction of manually curated rules for complex queries, resulting in more than 1,000 curation hours saved per year.
This new product is critical for large enterprises because it accurately answers complex searches across extensive applications, intranets, and websites. By automating relevancy rules and understanding user intent, it delivers precise results even for the most intricate queries. This solves problems related to catalog discrepancies, knowledge gaps, and language nuances, ensuring that large enterprises avoid irrelevant or empty search results that can hinder productivity and customer satisfaction.
“B2B commerce organizations struggle with catalog discrepancies, language nuances, and stock discrepancies—all of which negatively impact the buying experience,” said Eric Immermann, Practice Director, Search and Content, Perficient. “Neural Hybrid Search is so promising because it automates solutions for these big issues, helping teams provide relevance and improve outcomes with less work.”
Neural Hybrid Search is unique and transformative because of the following:
- It is ready to use out of the box. Typically, implementing vector search is a lengthy and costly process that prohibits most companies from using it. Neural Hybrid Search democratizes vector and lexical hybrid approaches.
- Users can adjust the weightings of lexical and semantic search components and select from various pre-trained embedding models to power the semantic component. This massively speeds up the implementation phase and time-to-value.
- It’s simple to train AI embedding models that continuously learn from company data and user search behavior. This automates ever-improving relevance.
“Neural Hybrid Search is a first-of-its-kind in the industry and is a transformative technology,” said Mike Sinoway, CEO, Lucidworks. “The speed-to-relevance and automation that is offered is unmatched. Our clients have experienced improved relevance with less curation and a faster time to value.”
Visit Lucidworks’ Neural Hybrid Search solution page to learn more.
