Articles By: Elastic
Case study: Cisco chooses Elastic to power its enterprise search platform
Cisco’s team of over 11,000 customer support engineers required a more efficient process to handle nearly two million service requests annually and ensure real-time support for customer issues. A modern, search AI experience was the answer. With Elastic’s integrated, advanced search capabilities, Cisco.com users now receive detailed, easy-to-consume results with direct links to relevant content.
Case study: Labelbox reduced search response times from 20 seconds to 1 second
Labelbox built a customizable AI platform on Elastic designed to enhance training data quality and model performance for their Catalog solution, helping AI teams accelerate machine learning development. After the deployment of Elasticsearch, search times improved drastically to about one second, enabling complex searches and sophisticated analytics for better data curation decisions.
Introduction to Search: Laying the groundwork for generative AI
Overview Search has been a driver of improved customer and employee experience for years — yet recent breakthroughs in generative AI present new opportunities for enterprise investment. This short introduction highlights the fundamentals of implementing search, how AI can level-up search relevance, and the critical role search plays in generative AI.
IDC infobrief: How advances in AI changed the game for search and knowledge discovery
Generative AI (GenAI) is changing how forward-looking organizations approach search, knowledge management, and other forms of knowledge discovery. Search is foundational for helping organizations discover, analyze, and utilize key data. However, with ever-increasing amounts of data, legacy search systems can struggle to help users quickly find what they need and avoid wasting time.
Building search in the age of generative AI: A blueprint for success
There’s never been a better time to create exceptional search experiences. By leveraging the capabilities of LLMs and generative AI, we can predict user intent, improve relevance, surface timely content, and even provide human-like responses. But one size doesn’t fit all for search. You can utilize out-of-the-box technology, build your own with feature-rich, custom design and functionality, or anything in-between.
Semantic search excellence: Getting started with AI
Overview This is part one of a two-part series on the journey to semantic search and generative AI excellence. Join us as we guide you through the spectrum of search methodologies, the steps to building search that understands the meaning of a query, and the choices and options along this path.
Beyond RAG basics: Strategies and best practices for implementing RAG
Overview Join us to explore advanced techniques in retrieval augmented generation (RAG). This talk is for developers, data scientists, and AI enthusiasts, and provides essential insights to elevate your RAG systems.
AI deep dive: A technical guide to the foundations of search and generative AI
This technical whitepaper takes you deep into the inner workings of generative AI and vector databases. It provides a detailed look at the technical foundations of vector embeddings, generative AI architecture, and how retrieval-augmented generation (RAG) works to provide LLMs with the critical domain-specific knowledge they need.
