Case Study: Rangespan achieves massive multi-supplier catalogue scale and accelerated time-to-market with MongoDB

Rangespan - Customer Case Study

Rangespan, founded in 2011 by former Amazon engineers, provides an automated supply-chain service that lets traditional retailers massively expand product selection without the cost and complexity of holding inventory. The company faced the challenge of consolidating highly variable supplier data (hundreds of differing attributes, partial overlaps and sparse tables) while delivering fast, scalable performance for millions of SKUs — a problem ill-suited to relational databases and the heavy normalization they require.

Rangespan built its catalogue on MongoDB’s dynamic schema and horizontal scalability, using Python-based NLP to cleanse and enrich documents, MapReduce for analytics, and ElasticSearch for deep search, with replica sets for high availability. The result: first customer deployed in six months, the ability to onboard suppliers in days rather than months, 400M product records with predictable performance, and substantial savings — about 12 FTE months and over $400K annually — plus reduced complexity and faster time-to-market.

Rangespan

James Summerfield

Technical Director