Case Study: Condé Nast achieves 35% higher click-through rates with MongoDB Atlas Vector Search and Voyage AI

Condé Nast boosts content recommendations 35% with MongoDB

Condé Nast, a global media company, sought to optimize its content recommendation engine for millions of readers across its many brands. Their challenge was managing the complexity of a vast multimedia library and scaling their existing pipelines, which had become slow and costly after introducing a new embedding model.

MongoDB implemented a solution using MongoDB Atlas and MongoDB Atlas Vector Search, integrated with Voyage AI embedding models. This reduced recommendation latency by 90%, cut operational costs by 65%, and increased click-through rates by 35%, delivering more relevant content faster.

Condé Nast