Smart Assistant for Maintenance – Conversational Search in Production Line Technical Documentation
October 7, 2025 ┃ 6 min read
Challenge
In modern manufacturing plants, maintenance is becoming increasingly complex—machines are equipped with dozens of sensors, components, and control systems. Maintenance technicians need instant access to accurate information to quickly diagnose malfunctions and minimize downtime. However, the traditional approach, based on manually searching through documentation, often results in time loss and a higher risk of incorrect actions.
At the same time, companies are facing a generational shift in their teams. Experienced workers are gradually leaving, and the new generation lacks the same level of knowledge and hands-on experience with specific equipment. Knowledge sharing, therefore, must be systematic and instantly accessible.
Complex and fragmented documentation: Maintenance technicians often worked with dozens of manuals in various formats, without the ability to quickly find a specific procedure or safety instruction.
Dependence on senior specialists: Less experienced technicians often had to contact experts, which slowed down problem resolution and extended downtime.
Time-consuming process and risk of errors: Finding the correct procedure often took several minutes or even tens of minutes, with a risk of misinterpreting the steps.
Insufficient knowledge digitalization: Key know-how was stored in PDFs, paper manuals, and internal files without interconnection.
Solution
The AI assistant for maintenance enables technicians to ask questions in natural language and instantly receive answers, procedures, or recommendations from internal documentation. The solution is built on a Retrieval-Augmented Generation (RAG) architecture and uses the AWS Bedrock platform for processing and generating responses.
Main solution components:
Knowledge base of documents: Manuals, drawings, and procedures are processed, indexed, and stored in a secure cloud environment.
Semantic search: The AI model understands the context of the question and retrieves relevant chapters, tables, and instructions.
Answer generation with citations: The output includes precise references to source documents and may also display previews of drawings.
Secure cloud environment: The solution runs within AWS infrastructure with separated environments (PoC / test / production) and continuous monitoring of response quality.
Scalability: The system can be extended with OCR for paper documents and integration with internal maintenance tools.
Implementation:
The initial phase of the solution had a research and development (PoC) character. It focused on verifying the accuracy of responses to typical maintenance questions, including safety procedures and the correct sequence of steps. The system was tested directly on real production line manuals and validated in cooperation with the maintenance team. The PoC results serve as the foundation for expanding the solution to include additional datasets and document types.
Results and benefits
The introduction of the conversational assistant brought immediate improvements in work efficiency and decision-making quality. Maintenance teams gained unified access to knowledge and reduced their dependency on individual expert memory. As a result, response times during malfunctions shortened, and downtime was minimized.
In addition to measurable time and cost savings, the project also became the foundation for broader knowledge digitalization in manufacturing. The assistant has become a tool that unites data, documentation, and human expertise into a single accessible platform.
The solution delivered:
Reduction of malfunction diagnostic time by 40–60%.
Reduction of the need for consultations with senior experts by 50%.
Faster onboarding of new technicians thanks to interactive responses.
Higher procedure accuracy – AI responses always include citations to source documents.
Possibility to extend the solution with multimodal elements (drawings, diagrams, images).
“The AI assistant has brought speed and confidence to the daily work of maintenance. Technicians no longer have to search through hundreds of pages of manuals—the answer comes in seconds.”
– Head of Maintenance
Conclusion
The project confirmed that even in highly technical environments, measurable value from AI solutions can be achieved quickly. The maintenance assistant demonstrated that the combination of high-quality data preparation, semantic search, and a secure cloud environment can fundamentally transform how technical teams work. Digitalized know-how becomes an instantly accessible source for decision-making and efficient maintenance.
Collaboration with AWS and Aspecta experts delivered a proven model that can be adapted to other manufacturing segments. The solution is ready for scaling, integration, and further development toward fully intelligent maintenance.
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