Ring Optimizes Global Customer Support with Amazon Bedrock

Ring, Amazon's home security subsidiary, faced challenges in scaling global customer support. This article outlines how Ring built a Retrieval-Augmented Generation (RAG)-based support chatbot using Amazon Bedrock Knowledge Bases. This solution reduced scaling costs by 21% by eliminating the need for region-specific infrastructure deployments, while maintaining a consistent customer experience across 10 international regions.

In the implementation process, Ring adopted metadata-driven filtering for region-specific content and divided content management into ingestion, evaluation, and promotion stages. This approach resulted in significant cost savings while scaling up. The architecture described utilizes Amazon Bedrock Knowledge Bases, AWS Lambda, AWS Step Functions, and Amazon S3.

Previously, Ring's customer support relied on a rule-based chatbot built with Amazon Lex. While functional, this system had limitations due to predefined conversation patterns that could not handle the diverse range of customer inquiries. During peak periods, 16% of interactions required escalation to human agents, and support engineers spent 10% of their time maintaining the rule-based system. As Ring expanded into international markets, this approach became unsustainable.

The Ring team identified four key requirements for creating a RAG-based support system. First, global content localization requires more than just translation; each region needs specific product information, including voltage specifications and regulatory compliance details. Second, a serverless architecture would allow engineers to focus on improving customer experience rather than managing infrastructure. Third, scalable knowledge management must support automated content update processes. Fourth, performance and cost optimization are essential.

To meet these requirements, Ring implemented metadata-driven filtering using content locale tags, allowing region-specific content to be served from a single centralized system. For their serverless needs, Ring chose Amazon Bedrock Knowledge Bases and Lambda, which removed the need for infrastructure management while providing automatic scaling.

The RAG-based chatbot architecture was designed to separate content management into two core processes: ingestion and evaluation, along with promotion. This two-phase approach enables Ring to maintain continuous content improvement while keeping production systems stable. The content upload process involves the Ring content team uploading documentation and troubleshooting guides to Amazon S3, where they structure objects with metadata.