Enterprise Knowledge Search
Rippling needed a resilient, scalable data foundation to support a rapidly expanding workforce platform and new AI features that answer questions across live operational data.
Reported impact
Rippling used its MongoDB foundation to expand to nearly 70 products, speed customer-specific solution building, and prepare a platform-wide AI agent that can work across live workforce data.
Rippling has used MongoDB Atlas since the company was founded, building its workforce platform around a common operational data store the team calls the employee graph. That graph brings together pay, benefits, IT, and expense information in a single view so each new product can reuse the same core data model instead of creating isolated stores. As Rippling accelerated its AI work in 2025, the company extended that foundation with Rippling AI and Data Connectors, a framework that ingests external customer data into MongoDB-backed models that its Solutions team can tailor for each deployment. MongoDB Atlas therefore acts as both the shared system of record for nearly 70 products and the live, permission-aware backbone for future AI agents that need to answer questions accurately across the company's full product portfolio.
Pay, benefits, IT, expense, and external customer data is ingested into MongoDB-backed models and the employee graph on MongoDB Atlas, then Rippling products and AI features query that live, permission-aware data to answer workforce questions and configure services.
"A common data store on [MongoDB] is the core of a key Rippling feature: the employee graph, which captures pay, benefits, IT, and expense information in a single view."
"Data Connectors [is] a framework that ingests external customer data into MongoDB-backed models."
"Powered by MongoDB, Rippling has expanded its platform to dozens of offerings."
"The company recently launched Rippling AI, a tool that uses live data and permissions to help customers accurately answer questions across their entire workforce."
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