What challenges did Ring face before AI implementation?

Scale self-service customer support globally across 10 international regions with region-specific content beyond translation while keeping latency and costs under control. The previous rule-based chatbot had limitations, contributed to 16% escalations to human agents, and required ongoing engineer maintenance.

What was the impact of the AI solution?

Reduced the cost of scaling to each additional locale by 21%. Maintained consistent customer experiences across 10 international regions. Achieved end-to-end latency of about 7–8 seconds.

Rank#381of 3,107

Source

Ring scales global customer support with Amazon Bedrock Knowledge Bases (multi-locale RAG chatbot)

Use case typeRAG infrastructureUpdated Jun 13, 2026

Ring, Amazon’s home security subsidiary, built a production-ready multi-locale RAG support chatbot using Amazon Bedrock Knowledge Bases. The solution addresses the need for region-specific support content across 10 international regions, beyond simple translation, while keeping latency and infrastructure costs under control.

Organization: Ring
Industry: Tech & Comms
Location: United States
Published: March 2026

Reported outcomes

−21%

costCost savings

8 secondsquantified impact

Strategic outcomes

Customer experience & trustMaintained consistent support across regionsNew product / capabilityBuilt a multi-locale RAG support chatbotSpeed & agilityEnabled region-specific content routing

Catalog median for cost savings deployments: −45% across 345 reported metrics. Compare benchmarks →

Primary read

Use case focus

Showing 2 of 2

1Customer Support Automation
2Retrieval-Augmented Generation (RAG)

Scale self-service customer support globally across 10 international regions with region-specific content beyond translation while keeping latency and costs under control.
The previous rule-based chatbot had limitations, contributed to 16% escalations to human agents, and required ongoing engineer maintenance.

Built a centralized, production-ready RAG chatbot with Amazon Bedrock Knowledge Bases.
Used metadata-driven filtering to route country or region-specific content from a shared knowledge base.
Orchestrated ingestion, evaluation, and promotion workflows with AWS Step Functions, AWS Lambda, and Amazon S3.
Used Amazon OpenSearch Serverless as the vector store option and Amazon Bedrock foundation models, including Titan embeddings and Claude Sonnet 4 for LLM-as-judge evaluation.

Technologies

Amazon Bedrock Knowledge Bases Amazon Bedrock Titan embeddings AWS Step Functions AWS Lambda Amazon S3 Amazon OpenSearch Serverless Claude Sonnet 4

Reduced the cost of scaling to each additional locale by 21%.
Maintained consistent customer experiences across 10 international regions.
Achieved end-to-end latency of about 7–8 seconds.

Architecture

Ring uses Amazon S3 to store localized support documents, AWS Lambda to process uploaded content, AWS Step Functions to orchestrate daily knowledge base creation and evaluation, and Amazon Bedrock Knowledge Bases to retrieve region-filtered context at runtime. The architecture supports versioned knowledge bases, LLM-as-judge quality validation with Claude Sonnet 4, rollback to prior versions, and a centralized RAG chatbot served through Lambda and Amazon Bedrock.

Sources & evidence1

Groundedness: 5/5Type: Blog PostPublished: Mar 30, 2026Publisher: AWSEvidence: VendorConfidence: Medium

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