Asset-Support Chatbot for Industrial Equipment Maintenance
Developed an advanced AI-powered chatbot system designed to provide intelligent support for industrial equipment maintenance.
The system integrates multiple AI capabilities including document retrieval (RAG), real-time sensor data analysis, web search,
and natural conversation - all orchestrated through an intelligent routing system that automatically determines the best
tool for each user query.
🎯 Key Achievement
Reduced support ticket volume by 40% and equipment downtime predictions by 60%
through automated, context-aware maintenance insights. Successfully onboarded 50+ enterprise users
during the beta phase with 99.9% system uptime.
🤖 Intelligent Query Routing System
Implemented an LLM-based intelligent classifier that analyzes user queries and automatically routes them to the
appropriate processing tool:
- Document Retrieval: For technical manual queries, troubleshooting guides, and maintenance procedures
- Web Search: For latest industry updates, vendor information, and external resources
- Signal Processing Analysis: For real-time sensor data interpretation and anomaly detection
- General Chat: For conversational queries and general assistance
The routing system achieves 98% accuracy in tool selection.
📚 RAG-Based Document Intelligence
Built a sophisticated Retrieval-Augmented Generation pipeline for technical documentation:
- Automated document chunking and preprocessing of technical manuals
- Embedding generation using all-mpnet-base-v2 model
- Vector storage and indexing in Pinecone for sub-second retrieval
- Semantic search across thousands of document segments
- Achieved 85% faster information retrieval
- Django REST API backend for document upload and processing
📊 Real-Time Signal Processing & Analytics
Integrated live sensor data analysis capabilities for predictive maintenance:
- Real-time analysis of velocity, acceleration, spectrum, and harmonics
- Pattern recognition for anomaly detection in sensor readings
- Predictive alerts for potential equipment failures
- Historical trend analysis and baseline comparisons
- Integration with company's asset monitoring infrastructure
- Contextual insights combining sensor data with maintenance history
🔍 Web Search Integration
Enhanced chatbot with external knowledge access:
- Real-time web search for latest equipment updates
- Vendor website crawling for parts availability
- Integration of external knowledge with internal documentation
- Citation and source tracking for all web-sourced information
💬 Natural Conversational Interface
- Context-aware conversations with memory of previous interactions
- Multi-turn dialogues for complex troubleshooting scenarios
- Natural language understanding for technical and non-technical queries
- Proactive suggestions based on equipment status
Backend Architecture
- Django REST API: Scalable backend with comprehensive endpoints
- Database Optimization: Advanced PostgreSQL indexing (50% faster responses)
- Asynchronous Processing: Celery + Redis for background tasks
- Query Classification: LLM-based routing with 98% accuracy
- Vector Database: Pinecone for semantic search
AI/ML Pipeline
- Embedding Model: all-mpnet-base-v2 for vectorization
- LLM Integration: Advanced language models for responses
- RAG Framework: Custom retrieval and generation pipeline
- Signal Processing: Real-time analysis algorithms
- Classification Model: Intent recognition for routing
Infrastructure & Deployment
- Cloud Platform: AWS EC2 for scalable compute
- Containerization: Docker for consistent deployment
- Web Server: Nginx for load balancing
- Database: PostgreSQL with advanced indexing
- Cache Layer: Redis for improved performance
Python
Django
REST API
LangChain
LLMs
RAG
Pinecone
PostgreSQL
Redis
Celery
Docker
Nginx
AWS EC2
all-mpnet-base-v2
NLP
Transformers
Signal Processing
- Intelligent Routing: Designing accurate query classification across different domains
- Performance Optimization: Database indexing and caching for real-time queries
- RAG Pipeline: Balancing chunk size, semantic accuracy, and retrieval speed
- Multi-Modal Integration: Combining structured sensor data with unstructured documents
- Production Deployment: Architecting robust, scalable infrastructure
- Multi-language support for international documentation
- Visual AI integration for image-based troubleshooting
- Advanced predictive analytics using historical data
- Integration with IoT platforms for expanded coverage
- Voice interface for hands-free operation