- The new tool, dubbed ESRE, is propelled by built-in vector search and transformer models that combine the most recent advances in generative AI with enterprise-specific data.
- According to the company, ESRE will enable businesses to develop highly specialized models trained on their own structured and unstructured proprietary data.
Elastic N.V., a provider of enterprise search technology, is launching its new Elasticsearch Relevance Engine, intending to assist businesses in developing and integrating generative artificial intelligence models fueled by their corporate data.
The new tool, dubbed ESRE, is propelled by built-in vector search and transformer models that combine the most recent advances in generative AI with enterprise-specific data.
Elastic is the developer of the ubiquitous open-source Elasticsearch platform, which businesses use to store, search, and analyze massive amounts of structured and unstructured data in near real time. Elasticsearch is the underlying engine for millions of applications with complex requirements and features. Elasticsearch will gradually power a new generative AI applications version built on enterprise data with the introduction of ESRE.
According to the company, ESRE will enable businesses to develop highly specialized models trained on their own structured and unstructured proprietary data.
Ash Kulkarni, Elastic’s Chief Executive, said, “Generative AI is a revolutionary moment in technology, and the companies that get it right, fast, are tomorrow’s leaders. The Elasticsearch Relevance Engine is available today, and we’ve already done the hard work of making it easier for companies to do generative AI right.”
According to Elastic, ESRE effectively democratizes AI and machine learning access. It combines hybrid search capabilities and unified APIs for vector search (BM25f search) with a new transformer model compact enough to fit inside a business laptop.
Elastic stated that users would be able to construct custom generative AI applications that utilize their unique, proprietary datasets without worrying about the cost or size of training and operating large language models. In addition, businesses can introduce their transformer models and integrate them with third-party models, such as OpenAI LP’s ChatGPT, to construct secure chat models that utilize their own highly specific data.
According to Elastic, some of Elastic’s most prominent consumers are already utilizing ESRE to augment their internally developed generative AI applications. Relativity ODA LLC, a legal technology provider, uses ESRE and Microsoft Corporation’s Azure OpenAI Service to enhance the relevance of results within its eDiscovery product RelativityOne.
Chief Product Officer of Relativity, Chris Brown, stated that ESRE enabled the company to develop “industry-leading search capabilities” that help customers and partners better organize and act upon their data. He also said, “We’re experimenting with ESRE right now and are excited about its potential to deliver powerful, AI-augmented search results to our customers.”
James Governor, the Co-founder of the developer-focused analyst firm RedMonk, stated that while businesses are enthusiastic about the potential of generative AI, they are overwhelmed by the rate of technological advancement. He said, “ESRE is designed to ease adoption of transformers, homemade and third-party LLM models, building on the original core strengths of Elastic in search.”