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AWS Solutions Library

Guidance for Assisted Diagnosis and Troubleshooting on AWS

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Overview

This Guidance demonstrates how to harness generative AI on AWS to
transform unstructured industrial data into actionable insights. It
shows operators and managers how to implement natural language
interactions for real-time equipment diagnostics, maintenance planning,
and documentation access. Through the integration of AWS services like
Amazon Bedrock and Amazon Q Business, the Guidance helps streamline
troubleshooting workflows using advanced techniques such as
retrieval-augmented generation (RAG) and entity extraction. With this
Guidance, operators and managers can reduce downtime, improve
operational efficiency, and make data-driven decisions through
AI-powered recommendations and intelligent data processing.

Benefits

Enable maintenance teams to quickly diagnose issues through natural language conversations with AI that intelligently analyzes equipment telemetry and maintenance documentation. Reduce mean time to repair (MTTR) while maintaining security controls.

Automate work order creation and access to operational data through secure integration with enterprise systems. Empower maintenance staff with self-service troubleshooting while ensuring consistent procedural compliance.

Deploy centralized maintenance intelligence that learns from your documentation and historical data. Provide AI-assisted diagnostics while maintaining role-based access control and audit capabilities.

How it works

Amazon Q Business deployment

This architecture diagram illustrates how to build an AI-powered diagnostic system using Amazon Q Business. This accelerates equipment troubleshooting through natural language conversations with maintenance documentation and real-time telemetry data.

Download the architecture diagram
Guidance for Assisted Diagnosis and Troubleshooting on AWS

Amazon Bedrock RAG deployment

This architecture diagram illustrates how to build an AI-powered maintenance diagnostic system using Amazon Bedrock Agent. This accelerates troubleshooting through conversational interfaces with maintenance documentation and real-time IoT equipment data.

Download the architecture diagram
Guidance for Assisted Diagnosis and Troubleshooting on AWS

Deploy with confidence

Ready to deploy? Review the sample code on GitHub for detailed deployment instructions to deploy as-is or customize to fit your needs.

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Disclaimer

The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.

References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.