MongoDB announced new capabilities that allow companies to better leverage generative artificial intelligence.

MongoDB Atlas Vector Search is now available, allowing customers to embed generative AI into their applications based on their own data. This enables AI to provide accurate and relevant answers for a specific organization or domain.

Customers can embed artificial intelligence features such as semantic search or image matching into their applications. The new tool uses a “flexible and scalable” document-based data model that the company says allows customers to combine queries for vector data, analytic aggregations, text-based search, geospatial data and time series data.

Alongside this innovative tool, the company has introduced MongoDB Atlas Search Nodes. It offers specialized infrastructure for handling generative AI search tasks associated with MongoDB Atlas Vector Search and MongoDB Atlas Search.

This infrastructure operates independently of the operational nodes in the database, enabling workload isolation, cost efficiency, and improved performance, as elucidated by MongoDB.

“With the general availability of MongoDB Atlas Vector Search and MongoDB Atlas Search Nodes, we’re making it even easier for customers to use a unified, fully managed developer data platform to seamlessly build, deploy, and scale modern applications and provide end users with the types of personalized, AI-powered experiences that save them time and keep them engaged,” said Sahir Azam, chief product officer at MongoDB.

Illustratively, a retailer conducting a holiday promotion could leverage this infrastructure to isolate and scale workloads specifically for chatbots in a designated location.

MongoDB asserts that this novel service has the potential to decrease query times by as much as 60%.