In today's digital era — where data grows exponentially and becomes increasingly complex — the ability to efficiently search and analyze this vast ocean of information has never been more important. But it's also never been more challenging. It's like trying to find a needle in a haystack but with the added challenge of the needle constantly changing its form. This is where vector search emerges as a game-changer, changing how we interact with large data sets.

To meet customer needs for scale, speed, and precision, IBM partners with Elasticsearch to deliver retrieval augmented generation (RAG) capabilities that can be seamlessly integrated into the IBM watsonx Assistant’s new Conversational Search feature. Customers using IBM watsonx Assistant and watsonx Orchestrate can now build conversational AI assistants grounded on their company data with comprehensive search capabilities with RAG.

Elastic has been named a Leader in the 2024 Gartner Magic Quadrant for Observability Platforms. The need for observability platforms continues to evolve as operations teams deal with increased complexity and exponential data growth. Emerging trends like generative AI are driving a paradigm shift in proactive root cause detection and resolution.

Our recent virtual event with Cohere dove deep into the world of retrieval augmented generation (RAG), focusing on the critical considerations for building RAG applications beyond the proof-of-concept stage. Our speakers, Lily Adler, principal solutions architect at Elastic, and Maxime Voisin, senior product manager at Cohere, shared valuable insights on the challenges, solutions, and best practices in this evolving field of natural language processing (NLP).

In 8.15, great search results are even more accessible for our customers. Our latest release brings semantic reranking, additional vector search tools, and more third-party model providers and promotes our native Learning to Rank (LTR) to generally available. And now search is more performant than ever with additional speed and efficiency improvements.

Elastic Observability 8.15 announces several key capabilities: New and enhanced native OpenTelemetry capabilities: Elastic AI Assistant enhancements: Large language model (LLM) observability for Azure OpenAI: Elastic Observability now provides deep visibility on the usage of the Azure OpenAI Service. The integration includes an out-of-the-box dashboard that summarizes the most relevant aspects of the service usage, including request and error rates, token usage, and chat completion latency.

Today, we are pleased to announce the general availability of Elastic 8.15.

Tens of thousands of companies in the world, across almost all industries, from midsize to large enterprises, rely on robust, efficient complex SAP systems to power their core operations. From sales to finance, from warehouse management to production planning and execution, business’s continuity, revenue, and customer success highly depend on processes running on enterprise resource planning (ERP) architectures.

Welcome to the Inside Elastic blog series, where we showcase Elastic's internal operations solving real-world business challenges. This specific series will shed light on our journey to integrate generative AI into our customer success and support operations, providing you with a behind-the-scenes look at our process. We’re blogging about this capability as we’re building it, and we’re excited for you to join the ride!

In today's digital age, data is the lifeblood of modern organizations — and the US government is no exception. As agencies grapple with the ever-increasing volume and complexity of data, it is imperative to adopt a standardized approach to monitoring, analyzing, and understanding the behavior of complex IT systems. This is where OpenTelemetry, an open-source observability framework, comes into play.