Case Study: Relevance AI achieves 3,000% ROI for clients with MongoDB Atlas Vector Search

Relevance AI delivers 3,000% ROI with MongoDB

Relevance AI, a provider of a no-code platform for building customized AI workforces, faced a challenge scaling its operations due to its initial database's lack of vector search capabilities. The company needed a more comprehensive solution from vendor MongoDB to handle large volumes of unstructured data and vectors quickly and reliably to support its AI-driven growth for clients.

By switching to MongoDB Atlas and its Atlas Vector Search product, Relevance AI gained a flexible and manageable solution that combined operational and vector databases in a single platform. This allowed them to manage extraordinary growth, scaling from processing 200,000 tokens a day to nearly a billion. The solution enabled them to deliver AI agents that provide clients with an impressive 3,000% return on investment.

Relevance AI