Assisted Diagnosis and Troubleshooting

Industrial operations often face extended downtime and reduced productivity due to inefficient troubleshooting and diagnosis.

Our Assisted Diagnosis & Troubleshooting solution leverages Amazon Q, AWS IoT SiteWise, and other AWS Services to help operators and engineers quickly identify root causes, access real-time equipment insights, and receive AI-driven recommendations. By integrating with ERP, MES, and CMMS systems, the solution can provide a unified workflow that accelerates issue resolution, optimizes OEE (Overall Equipment Effectiveness), and reduces operational costs. What makes this solution unique is our pre-built accelerator, a turnkey solution that can be deployed in your AWS environment in under 60 minutes. From there, it can be fine-tuned to align with your business goals, specific data sources, and operational workflows.

Key Features

• AI-powered diagnostic assistant: Real-time troubleshooting guidance using Amazon Q.
• Natural language interface: Query systems conversationally for faster insight retrieval.
• Sensor & OT data integration: AWS IoT SiteWise integration for equipment health monitoring.
• Knowledge retrieval with RAG: Access SOPs, manuals, and maintenance logs for contextual answers.
• Automated work order creation: AI recommendations trigger corrective actions within ERP/CMMS systems.
• Seamless workflow integration: Works across SCADA, MES, ERP, and ticketing platforms.

Benefits

• Reduced downtime: Faster root cause detection minimizes operational disruptions.
• Improved OEE: AI-optimized diagnostics maximize equipment uptime.
• Operational visibility: Real-time health and performance insights across production.
• Data-driven maintenance: Preventive strategies powered by IoT sensor data.
• Optimized workforce efficiency: Empower operators to resolve issues without deep technical expertise.
• Rapid time-to-value: Get started with a fully functional PoC in under 60 minutes.

Use Cases

• Predictive Maintenance: Anticipate and prevent breakdowns with AI and IoT data.
• Production Troubleshooting: Quickly resolve bottlenecks and equipment failures.
• Knowledge Augmentation: Provide technicians with AI-curated guidance and past resolutions.
• Automated Workflows: Streamline ticketing, work orders, and corrective actions.

How It Works

• Operators interact with the AI assistant using natural language.
• The system queries IoT sensor data, maintenance logs, and SOPs using RAG.
• AI delivers real-time recommendations for diagnosis and corrective actions.
• Insights can trigger automated work orders and integrate with ERP/CMMS systems.

Getting Started

• Engage NeenOpal to scope user roles, equipment, and workflows.
• Deploy the pre-built accelerator in less than 60 minutes in your AWS environment.
• Run a Proof of Concept by connecting IoT sensor data, maintenance logs, and SOPs.
• Fine-tune and scale for production-ready troubleshooting and predictive maintenance.