Unlocking the Potential of Elasticsearch Relevance with Generative AI


Elastic is taking its enterprise search technology to the next level with the launch of the Elasticsearch Relevance Engine (ESRE). This AI-powered vector search feature will help ensure even more relevant results for users’ queries. By combining open-source Apache Lucene data indexing with vector support, AI capabilities, and its own transformer neural network model, Elastic is able to offer more powerful and accurate search results within its enterprise platform.

With ESRE’s vector search feature, organizations are able to assign content a numerical representation which contributes to a more relevant search outcome. Organizations can also take advantage of tooling such as OpenAI’s GPT-4 to leverage generative AI insights on their data with their own transformer models.

Matt Riley, general manager of Enterprise Search at Elastic, believes ESRE will make an impact on the way organizations search for relevant content. He is excited to see the possibilities generated by the combination of search relevance technologies.

Elastic is an open source search and analytics platform that provides an exhaustive range of technology solutions for big data. Their products are used to power advanced search and analytics applications for the web, mobile, and enterprise. They also offer consulting services to businesses looking to improve their use of AI or develop customer-facing applications.

Matt Riley is the General Manager of Enterprise Search at Elastic. He is leading the company’s initiatives around improving search relevance with ESRE and its related technologies and features. Previously, he was the Head of Search at IBM Watson and Google. He brings a wealth of experience to the table, having lead search and AI initiatives for both companies.

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