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Applied Industrial AI · Product Discovery · 2025

Talking Systems

When AI Starts Understanding Industrial Machines

Mälardalen University · Mälardalen Industrial Technology Center (MITC)

What if industrial systems could explain themselves in natural language instead of cryptic error codes?

  • Industrial AI
  • Generative AI
  • Product Discovery
  • RAG
  • LangChain
  • OPC UA
  • Siemens S7 PLC
  • IT/OT Integration
  • Edge
  • Vector Search
  • User Validation

Opportunity

Industrial operators often depend on specialists to interpret alarms, error codes and technical documentation, resulting in delayed troubleshooting and unnecessary dependency on specialist support.

Discovery

  • Interviewed system owners and testbed operators
  • Studied incident and troubleshooting workflows
  • Identified operational constraints and pain points
  • Identified specialist dependency
  • Identified that significant machine data existed but lacked contextual explanation
  • Defined the AI-assistant concept around explainability and self-service
The project started with the operational problem, not with the AI technology.

Solution

Designed and developed a proof-of-concept AI assistant combining industrial machine data with domain knowledge and technical documentation to provide natural-language guidance.

Edge deployment kept industrial data on-site.

  • Retrieval-Augmented Generation
  • LangChain
  • OPC UA
  • Siemens S7 PLC
  • Vector search over technical documentation
  • Live machine data
  • Edge deployment

Capabilities

  • Root-cause explanations
  • Step-by-step resolution
  • Interactive maintenance guidance
  • Natural-language alarm explanation
  • Contextual technical information

Validation

Evaluated in the FESTO CP-Factory smart-factory testbed together with MITC partners.

User testing demonstrated that non-specialist users could successfully work through incidents using the AI assistant without waiting for specialist support.

Potential business value

The concept demonstrated potential to:

  • Reduce troubleshooting time
  • Reduce downtime exposure
  • Improve first-line support
  • Make specialist knowledge accessible at the point of operation
  • Improve support scalability

Product perspective

The architecture was not limited to one troubleshooting scenario. The same combination of operational data, domain knowledge and AI assistance could support multiple reusable product capabilities.

  • AI-powered technical support
  • Maintenance decision support
  • Field service
  • Operational knowledge assistance
  • Product selection
  • Sales enablement

From problem to product opportunity

  1. Operational Problem
  2. Discovery
  3. AI Capability
  4. Validation
  5. Reusable Product Opportunities

Images

Large screen showing the Talking Systems assistant answering a question about the production line
The assistant running on the testbed display at MITC
Assistant interface explaining an emergency-stop alarm in plain language
Assistant interface — alarm guide answer
Presentation slide framing the industrial troubleshooting scenario
Concept framing — the scenario behind the assistant
Two people standing in front of the testbed screen during the demonstration
Demonstration at the smart-factory testbed