Question: How can I optimize my AI work with faster document search and recommendation systems?

If you want to get more out of your AI work with faster document search and recommendation systems, Vespa is a top contender. It's a unified search engine and vector database that can handle vector search, lexical search and search in structured data. It's good for search, recommendation and personalization with features like fast vector search and filtering with machine-learned models. And it can run large-scale machine-learned model inference and integrates with many machine learning tools, so it's good for building high-performance applications.

Another contender is Algolia, an AI-powered search infrastructure that offers tools for fast, scalable and personalized search. It marries keyword search with vector understanding and can dynamically re-rank results based on user behavior and trends. Algolia can be used for enterprise search, headless commerce, mobile and voice search, and has flexible pricing options, including a Pay-As-You-Go option and no long-term contracts.

If you want a more complete search and recommendation system, GoSearch is an all-in-one enterprise search platform with multimodal AI technology. It integrates with more than 100 data connectors and can provide immediate answers and information discovery across multiple sources. With AI-powered recommendations and deep integrations with tools like Google Docs and Jira, GoSearch is designed for multiple teams and has customizable pricing plans.

Last, Glean is an AI assistant platform that uses Retrieval Augmented Generation (RAG) technology to generate trusted and personalized answers based on enterprise data. It aggregates all enterprise data into a single knowledge graph and offers no-code custom generative AI agents, assistants and chatbots. Glean is geared for teams like engineering, support and sales that need to work more effectively with fast access to the right information.