Manufacturing & Industry 4.0 MetaMindsSubmitted July 18, 2026

FactoryLens - An End To End Factory Failure Investigation Platform

An MCP app on the Model Context Protocol built by MetaMinds at the Amrita University Amritapuri campus NitroStack × MCP To The Moon hackathon and deployed on NitroStack.

About this project

FactoryLens – Because Every Second of Downtime Costs More Than Just Time FactoryLens is an AI-powered smart factory maintenance assistant built using the Model Context Protocol (MCP) and the NitroStack SDK. It brings together machine telemetry, maintenance history, equipment manuals, and production schedules into a single intelligent interface, allowing AI assistants to access factory data through structured tools and resources. Instead of manually investigating equipment failures, technicians can simply ask questions like "Why is Machine MCH-004 overheating?" or "What does error E101 mean?" FactoryLens automatically analyzes sensor data, reviews maintenance records, consults equipment manuals, identifies the most likely root cause, estimates business impact, and can even generate a maintenance work order for critical issues. Rather than displaying raw data, FactoryLens connects information from multiple sources to provide clear, evidence-backed insights through interactive dashboards and investigation reports. This helps technicians, engineers, and plant managers diagnose problems faster and make informed maintenance decisions. By reducing troubleshooting time, minimizing equipment downtime, and streamlining maintenance workflows, FactoryLens improves factory productivity and reliability. As it evolves with live IoT integration, predictive maintenance, and cloud connectivity, it has the potential to enable smarter, more proactive industrial operations.

Manufacturing & Industry 4.0 track

Create intelligent systems for smart factories, predictive maintenance, quality control, and supply chain optimization.

Live MCP appSource code

Team MetaMinds

  • Janaki RLead

  • Kothamaddi Venkata Sai Praneeth

  • Mokshitha Yarlagadda

  • Ajalya T M

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Frequently asked questions

What does FactoryLens - An End To End Factory Failure Investigation Platform do?
FactoryLens – Because Every Second of Downtime Costs More Than Just Time FactoryLens is an AI-powered smart factory maintenance assistant built using the Model Context Protocol (MCP) and the NitroStack SDK. It brings together machine telemetry, maintenance history, equipment manuals, and production schedules into a single intelligent interface, allowing AI assistants to access factory data through structured tools and resources. Instead of manually investigating equipment failures, technicians can simply ask questions like "Why is Machine MCH-004 overheating?" or "What does error E101 mean?" FactoryLens automatically analyzes sensor data, reviews maintenance records, consults equipment manuals, identifies the most likely root cause, estimates business impact, and can even generate a maintenance work order for critical issues. Rather than displaying raw data, FactoryLens connects information from multiple sources to provide clear, evidence-backed insights through interactive dashboards and investigation reports. This helps technicians, engineers, and plant managers diagnose problems faster and make informed maintenance decisions. By reducing troubleshooting time, minimizing equipment downtime, and streamlining maintenance workflows, FactoryLens improves factory productivity and reliability. As it evolves with live IoT integration, predictive maintenance, and cloud connectivity, it has the potential to enable smarter, more proactive industrial operations.
Who built FactoryLens - An End To End Factory Failure Investigation Platform?
FactoryLens - An End To End Factory Failure Investigation Platform was built by team MetaMinds at the Amrita University Amritapuri campus NitroStack × MCP To The Moon hackathon, in the Manufacturing & Industry 4.0 track.
What is an MCP app and how is it built?
An MCP app is an application built on the Model Context Protocol — an open standard that lets AI agents connect to tools, data, and APIs. This project exposes MCP tools and resources that agentic AI systems can call. It was built and deployed on NitroStack, the full-stack platform for shipping MCP apps and servers.