The challenge
Maintenance in modern manufacturing plants is becoming more complex as machines incorporate dozens of sensors, components, and control systems. Technicians need immediate access to accurate information so they can diagnose faults quickly and minimize downtime. Traditional manual searches through technical documentation take time and can increase the risk of following the wrong procedure.
Companies are also facing generational change within maintenance teams. Experienced employees are gradually leaving, while newer technicians do not yet have the same level of knowledge or experience with specific equipment. Know-how therefore needs to be shared systematically and made immediately accessible.
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Complex and fragmented documentation: Maintenance teams worked with dozens of manuals in different formats without a fast way to find a specific procedure or safety instruction.
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Dependence on senior specialists: Less experienced technicians frequently had to contact experts, slowing troubleshooting and extending downtime.
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Time requirements and risk of errors: Finding the correct procedure could take several minutes or tens of minutes, with a risk of misinterpreting individual steps.
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Limited digitization of knowledge: Critical know-how was stored across PDFs, paper manuals, and disconnected internal files.
The solution
The maintenance AI assistant allows technicians to ask questions in natural language and immediately retrieve answers, procedures, or recommendations from internal documentation. The solution uses Retrieval-Augmented Generation (RAG) and Amazon Bedrock to process questions and generate answers.
Main components of the solution:
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Document knowledge base: Manuals, drawings, and procedures are processed, indexed, and stored in a secure cloud environment.
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Semantic search: The AI model interprets the intent of a question and retrieves relevant chapters, tables, and instructions.
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Answer generation with citations: Responses include references to source documents and can also provide previews of drawings.
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Secure cloud environment: The solution runs on AWS with separate PoC, test, and production environments and monitoring of answer quality.
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Extensibility: OCR can be added for paper documents, together with integrations into internal maintenance tools.
Implementation:
The initial phase was a research and development PoC. It tested answer accuracy for typical maintenance questions, including safety procedures and the correct sequence of steps. The system was tested using actual production-line manuals and validated together with the maintenance team. The PoC results provide a basis for adding further datasets and document types.
Results and benefits
The conversational assistant improved work efficiency and supported faster decision-making. Maintenance teams gained a consistent way to access knowledge and became less dependent on individual experts’ memory. This shortened response times during faults and helped reduce downtime.
In addition to measurable time and cost savings, the project created a foundation for broader digitization of manufacturing knowledge. The assistant connects data, documentation, and employee experience in one accessible environment.
What the solution delivered:
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40–60% shorter fault diagnosis time.
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50% fewer consultations with senior specialists.
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Faster onboarding of new technicians through interactive answers.
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More consistent procedures because AI responses include citations to source documents.
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The option to extend the solution with multimodal content such as drawings, diagrams, and photographs.
“The AI assistant has brought speed and confidence to everyday maintenance work. Technicians no longer have to search through hundreds of manual pages; they can get the answer in seconds.” Head of maintenance
Conclusion
The project confirmed that AI solutions can deliver measurable value even in technically complex environments. The maintenance assistant showed how data preparation, semantic search, and a secure cloud environment can change the way technical teams access and use knowledge. Digitized know-how becomes immediately available for maintenance decisions and troubleshooting.
Working with AWS and Aspecta experts produced a validated model that can also be adapted to other manufacturing environments. The solution is prepared for scaling, integration, and further development toward more intelligent maintenance.
