Ring, the home security subsidiary of Amazon, has successfully overhauled its global customer service operations by moving away from rigid, rule-based chatbots. The previous system, built on Amazon Lex, struggled with the nuances of diverse customer inquiries and required significant engineering overhead to maintain across multiple international markets.

To solve this, Ring developed a multi-locale support system using Amazon Bedrock Knowledge Bases. This architecture utilizes Retrieval-Augmented Generation (RAG), a technique that allows the AI to pull precise information from a vast library of product manuals and troubleshooting guides before generating a response. By centralizing this knowledge and using metadata tags to filter for specific regions (like "en-GB" for the UK), Ring eliminated the need to build separate technical setups for every country they serve.

The results are significant: the cost of adding a new locale has dropped by 21%, and the system maintains consistent performance even during peak traffic. Ring also introduced an automated quality control layer where a powerful language model acts as a "judge" to evaluate the accuracy of the bot’s answers against a set of correct examples. This ensures that customers receive reliable, voltage-accurate technical advice regardless of their location.

Ring, the home security subsidiary of Amazon, has successfully overhauled its global customer service operations by moving away from rigid, rule-based chatbots. The previous system, built on Amazon Lex, struggled with the nuances of diverse customer inquiries and required significant engineering overhead to maintain across multiple international markets.

To solve this, Ring developed a multi-locale support system using Amazon Bedrock Knowledge Bases. This architecture utilizes Retrieval-Augmented Generation (RAG), a technique that allows the AI to pull precise information from a vast library of product manuals and troubleshooting guides before generating a response. By centralizing this knowledge and using metadata tags to filter for specific regions (like "en-GB" for the UK), Ring eliminated the need to build separate technical setups for every country they serve.

The results are significant: the cost of adding a new locale has dropped by 21%, and the system maintains consistent performance even during peak traffic. Ring also introduced an automated quality control layer where a powerful language model acts as a "judge" to evaluate the accuracy of the bot’s answers against a set of correct examples. This ensures that customers receive reliable, voltage-accurate technical advice regardless of their location.