Case Study: Kovai improves enterprise knowledge base search with MongoDB Vector Search

Kovai delivers <4ms vector search with MongoDB

Kovai, an enterprise software company, noticed its customers shifting from using keyword search to asking natural language questions of their knowledge base platform, Document360. To meet this demand for fast, contextual answers powered by AI, Kovai needed a vector search solution. The challenge was to integrate this without creating data sync issues or a complex architecture.

Kovai implemented MongoDB Vector Search on Atlas as their solution. This allowed them to store both their knowledge base content and its embeddings together in MongoDB, simplifying their architecture and eliminating data synchronization problems. The results were exceptional, with vector search returning results in less than 4 milliseconds. This provided users with a seamless hybrid search experience and boosted trust by delivering accurate, contextual answers.

Kovai