AI-Powered Maintenance Chatbot – Tyre Manufacturing Industry
Developed an AI-based Maintenance Chatbot for tyre manufacturing plants designed specifically for maintenance engineers. Provides instant troubleshooting guidance and contextual insights using RAG (Retrieval-Augmented Generation) architecture.
Client: Leading Tyre Manufacturer
Reduced
MTTR
Faster repair times
Improved
Efficiency
Response time
Higher
Availability
Plant uptime
Instant
Support
Real-time guidance
The Challenge
What problems did they face?
Tyre manufacturing plants with highly automated production lines (Mixing, Calendering, Extrusion, Curing) faced high downtime due to prolonged troubleshooting. Machine knowledge was scattered across manuals, SOPs, error code lists, and past maintenance logs. There was a heavy dependency on experienced personnel and limited real-time support on the shop floor.
Our Solution
How we addressed it
We developed an AI-based Maintenance Chatbot specifically for maintenance engineers. It uses RAG architecture to integrate multiple data sources including machine manuals, error code databases, SOPs, and historical logs. The system is connected to shop-floor machines via industrial data pipelines to provide context-aware responses and step-by-step troubleshooting guidance.
Technical Implementation
How We Built It
Key Technologies Used
Large Language Models (LLMs) – For natural language interaction
RAG (Retrieval-Augmented Generation) – Retrieves relevant technical knowledge
Vector databases – Semantic search across manuals and logs
Industrial IoT integration – Real-time machine and alarm data ingestion
System Architecture
Secure, enterprise-grade application
Accessible via Web, Tablets, and Shop-floor terminals
Role-based access for maintenance teams
Controlled access to sensitive machine data
On-premise / private deployment options
Functional Capabilities
Natural language query handling for maintenance engineers
Understanding and interpretation of machine error codes
Step-by-step troubleshooting guidance
Context-aware responses based on machine state
Root cause hints and recommended corrective actions
Key Differentiators
Deep understanding of tyre manufacturing equipment
RAG-based approach ensures factual, plant-specific responses
Combines machine data with unstructured knowledge
Designed for real-world maintenance use cases
The Results
Measurable Impact
Reduced Mean Time to Repair (MTTR)
Improved maintenance efficiency and response time
Reduced dependency on senior experts
Higher equipment availability and plant uptime
Improved maintenance standardization
Faster identification of probable root causes
Knowledge retention and reuse across shifts
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