Case Study: System1 boosts MapQuest search performance and AI capabilities with MongoDB Atlas

System1 speeds MapQuest queries by 30% with MongoDB

System1, a customer acquisition company, faced challenges after acquiring MapQuest. Their goal to enhance the mapping service with modern features like a recommendation engine was hindered by a costly, fragmented data architecture using in-memory storage for over 100 million points of interest and a separate PostgreSQL database. This setup was complex to maintain and slowed development. To consolidate data and improve performance, they turned to MongoDB, specifically the MongoDB Atlas platform.

By migrating to MongoDB Atlas, System1 consolidated its data, including points of interest and API logs, into a unified document model. This solution delivered a 30% faster query performance for users and saved several hundred thousand dollars in annual database costs. The implementation also enabled new capabilities using MongoDB Atlas Vector Search for semantic search and supported a new CI/CD pipeline, allowing the team to focus on building features rather than maintaining infrastructure.

System1