Intelligent Customer Support Workflow Automation (LLM + RAG)
Automated customer support system that uses an AI workflow to understand queries, retrieve answers from knowledge bases, and generate accurate replies.
🎯 Project Overview
Built an end-to-end AI support workflow using LLMs + RAG (Retrieval-Augmented Generation) that automates customer support operations. The system intelligently understands customer queries, retrieves relevant information from knowledge bases, and generates accurate, contextual replies with human-level accuracy.
💼 Business Impact
- 70% reduction in support workload
- Average response time cut from 2 minutes → 4 seconds
- 24/7 availability without human intervention
- Consistent quality across all customer interactions
- Scalable solution handles thousands of queries simultaneously
🛠️ Technical Architecture
Workflow Components
1. Input Processing
User query captured via React-based chat UI with real-time typing indicators and message history.
2. Intent Detection
Classifier identifies query category (billing, account, technical, general) using fine-tuned LLM for accurate routing.
3. RAG Workflow
Vector database (Pinecone/FAISS) retrieves relevant documents from knowledge base using semantic search. Chunks are ranked by relevance and passed as context to LLM.
4. LLM Response Generation
GPT-4.1/5 produces contextual, accurate replies using retrieved context. Responses are validated for accuracy and tone before delivery.
5. Action Layer
Automatically creates support tickets, updates CRM records, sends follow-up emails, and triggers workflows based on query type and resolution status.
6. Continuous Learning
User feedback loop (thumbs up/down) improves answers over time. Failed queries are flagged for human review and added to knowledge base.
Core Technologies
- Python - Backend logic and workflow orchestration
- FastAPI - High-performance REST API for chat interface
- LangChain - Workflow orchestration and prompt management
- Pinecone/FAISS - Vector database for semantic search
- GPT-4.1/5 - Large language models for response generation
- React - Modern chat UI with real-time updates
🔧 Technical Challenges Solved
Challenge 1: Context Window Limitations
Problem: Knowledge base contains thousands of documents, but LLMs have limited context windows.
Solution: Implemented intelligent chunking with overlap, semantic search to retrieve only relevant chunks, and context compression techniques to maximize information density.
Challenge 2: Response Accuracy
Problem: LLMs can hallucinate or provide incorrect information.
Solution: Multi-stage validation pipeline: fact-checking against source documents, confidence scoring, and human-in-the-loop for low-confidence responses.
Challenge 3: Real-Time Performance
Problem: RAG + LLM calls can be slow, affecting user experience.
Solution: Implemented caching layer for common queries, parallel processing for retrieval and generation, and streaming responses for perceived speed.
📊 Performance Metrics
💡 Key Innovations
- Multi-stage RAG: Hierarchical retrieval (coarse → fine-grained) for better context
- Intent-aware routing: Different RAG strategies based on query type
- Feedback integration: Continuous improvement from user interactions
- CRM automation: Seamless integration with Salesforce, HubSpot, Zendesk
- Multi-language support: Handles queries in multiple languages
🚀 Results
- ✅ Deployed for enterprise clients handling 10,000+ queries/day
- ✅ 70% reduction in support ticket volume
- ✅ Customer satisfaction scores improved by 40%
- ✅ Cost savings of $50K+ per month in support operations
- ✅ Scalable architecture supports multiple clients with isolated knowledge bases