Case Study: How Rippling supports many user types with Decagon
Key results
The challenge
Rippling's prior decision-tree support tool required heavy manual oversight and could not deliver dynamic, accurate responses across its HR, IT, and Finance products. Customers self-served only about 38% of the time in chat and had no AI agent in email, and misrouted tickets caused delays.
The solution
Rippling adopted Decagon's AI agents, connecting to internal APIs for context-aware responses and building a tagging and routing system with 75+ tags across 12+ core products, backed by forward-deployed engineering support.
“We brought this problem statement to Decagon and they delivered. We are able to tailor the experience and responses to customers to not only deliver strong deflection results, but also enhance the customer experience along the way.”
HNHusam NajibVP, Customer Support, Rippling
The results, in context
The case study reports a 32% increase in deflection, with chat deflection rising from 38% to over 50%, and an immediate 7% improvement in customer conversation routing. Decagon supports over 400,000 Rippling users across HR, IT, and Finance products.