New MongoDB Atlas Vector Search integration with Amazon Bedrock to help accelerate development of highly engaging applications powered by generative AI
MongoDB, Inc. today at AWS re:Invent 2023 announced plans to integrate MongoDB Atlas Vector Search with Amazon Bedrock to enable organizations to build next-generation applications on Amazon Web Services (AWS) and their industry-leading cloud infrastructure. MongoDB Atlas Vector Search uses an organization’s operational data to simplify bringing generative AI and semantic search capabilities into applications for highly engaging and customized end-user experiences. This integration will make it easier for developers to create applications on AWS that use generative AI to complete complex tasks for a wide range of use cases and deliver up-to-date responses based on proprietary data processed by MongoDB Atlas Vector Search.
“Customers of all sizes from startups to enterprises tell us they want to take advantage of generative AI to build next-generation applications and future proof their businesses. However, many customers are concerned about ensuring the accuracy of the outputs from AI-powered systems while protecting their proprietary data,” said Sahir Azam, Chief Product Officer at MongoDB. “With the integration of MongoDB Atlas Vector Search with Amazon Bedrock, we’re making it easier for our joint-AWS customers to use a variety of foundation models hosted in their AWS environments to build generative AI applications that can securely use their proprietary data to improve accuracy and provide enhanced end-user experiences.”
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Amazon Bedrock is a fully managed service from AWS that offers a choice of high-performing foundation models (FMs) via a single API, along with a broad set of capabilities to build generative AI applications with security and privacy. This new integration with Amazon Bedrock allows organizations to quickly and easily deploy generative AI applications on AWS that can act on data processed by MongoDB Atlas Vector Search and deliver more accurate and relevant responses. Unlike add-on solutions that only store vector data, MongoDB Atlas Vector Search powers generative AI applications by functioning as a highly performant and scalable vector database with the added benefits of being integrated with a globally distributed operational database that can store and process all of an organization’s data.
Using the integration with Amazon Bedrock, customers can privately customize FMs—from AI21 Labs, Amazon, Anthropic, Cohere, Meta, and Stability AI—with their proprietary data, convert data into vector embeddings, and process these embeddings using MongoDB Atlas Vector Search. Leveraging Agents for Amazon Bedrock for retrieval augmented generation (RAG), customers can then build applications that respond to user queries with relevant, contextualized responses—without needing to manually code. For example, a retail apparel organization can more easily develop a generative AI application to help employees automate tasks like processing inventory requests in real time or to help personalize customer returns and exchanges by suggesting similar styles of in-stock merchandise. With fully managed capabilities, this new integration will enable joint AWS and MongoDB customers to securely use generative AI with their proprietary data to its full extent throughout an organization and realize business value more quickly—with less operational overhead.
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“In this next wave of widespread AI adoption, organizations want to strengthen their data strategies to develop differentiating and competitive generative AI solutions,” said Vasi Philomin, Vice President of Generative AI at AWS. “The MongoDB Atlas Vector Search integration with Amazon Bedrock will help customers tightly align their data strategies to build and scale generative AI innovations. With a relationship spanning more than a decade, we look forward to continuing our momentum with MongoDB and enabling our joint customers to make the most of generative AI.”
Scalestack AI is an all-in-one data enrichment, prioritization, and activation platform that allows go-to-market teams to easily map existing data to their ideal customer profile and power their sales and revenue engine. “Scalestack’s mission is to help organizations unlock sales productivity, and our relationship with MongoDB has been integral to that,” said Elio Narciso Co-founder and CEO at Scalestack. “We use MongoDB Atlas Vector Search to store the data we use in our RAG chatbot, and it provides long-term memory to the large language models we use. We’re really excited about the integration between MongoDB Atlas Vector Search and Amazon Bedrock—this fully managed system will let our developers focus on innovating on behalf of customers. We look forward to working with both MongoDB and AWS to further the development of Scalestack’s AI-powered RevOps platform.”
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