Elastic, the company behind Elasticsearch®, announced the launch of the Elasticsearch Relevance Engine™ (ESRE™), powered by built-in vector search and transformer models, which is designed to bring the power of AI innovation to proprietary enterprise data.
ESRE enables companies to achieve breakthrough results by helping organizations create secure deployments to take advantage of all their proprietary structured and unstructured data while optimizing infrastructure and using talent resources more efficiently.
“Generative AI is a revolutionary moment in technology and the companies that get it right, fast, are tomorrow’s leaders,” said Ash Kulkarni, CEO of Elastic. “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.”
“The expertise of Elastic in enterprise search is evident in the way the company thinks about how to incorporate generative AI into it,” said Julia Liuson, President of Microsoft’s Developer Division. “We’re working closely together to help companies leverage the power of Azure OpenAI and ChatGPT over all of an organizations' proprietary data in Elasticsearch to help our joint customers deliver a richer and more seamless experience.”
Elastic has made substantial investments in foundational AI capabilities to democratize AI and machine learning for developers. Unified APIs for vector search, BM25f search and hybrid search, plus a new transformer model small enough to fit on a laptop’s memory mean businesses and teams are now able to optimize infrastructure and talent resources more efficiently. Using a relevance engine, like ESRE, allows companies to take advantage of all of their structured and unstructured data to build custom generative AI (GAI) apps, without having to worry about the size and cost of running large language models. The ability to “bring your own” transformer model and integrate with third-party transformer models allows organizations to create secure deployments that leverage all the cutting-edge innovations of GAI on their specific business data. With ESRE, the thousands of companies and vast community of users that have invested in Elastic solutions can advance AI initiatives right now without a lot of additional resources.
“Enterprises are excited about the potential for generative AI in their applications and workflows, but are all also swamped by the pace of innovation in the field,” said James Governor, co-founder of RedMonk. “ESRE is designed to ease adoption of transformers, homemade and third-party LLM models, building on the original core strengths of Elastic in search.”
The product of more than two years of research and development, ESRE is already being utilized by several prominent Elastic customers to augment their own AI-powered applications. In addition, Relativity, a global legal technology company and an Elastic OEM partner, is experimenting with ESRE in conjunction with the Azure OpenAI Service to understand and demonstrate how it might improve the relevance of results within their eDiscovery product, RelativityOne.
"Ensuring our customers and partners have industry-leading search capabilities is vital to our mission to help them organize data, discover the truth and act on it. To that end, I’m thrilled about the benefits we can bring to customers through our investments to harness Elasticsearch within RelativityOne,” said Chris Brown, Chief Product Officer at Relativity. "We're experimenting with ESRE right now and are excited about its potential to deliver powerful, AI-augmented search results to our customers."
Elasticsearch Relevance Engine (ESRE) includes:
- Industry leading advanced relevance ranking features, including BM25f, a critical component of hybrid search
- A vector database for storing and querying embeddings in high-dimensions
- A new proprietary transformer model that delivers out-of-the-box semantic search
- Bring your own transformer models
- Integration with third-party transformer models, such as OpenAI GPT 3/4 via APIs
The Latest
Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...
For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...
Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...
Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...
For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...
New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...
Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...
In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ...
In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...
When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...