Industry: E-commerce
Client Size: Mid-sized e-commerce company with 100,000+ monthly support inquiries

Challenge

The client faced:
❌ High support costs – A team of 50 agents handling repetitive queries
❌ Slow response times – 5+ minute wait times during peak hours
❌ Low automation – 90% of queries required human interaction

Solution

We implemented a GenAI-powered customer support system using LLMs (Large Language Models) integrated into their existing helpdesk. Key technical components included:

🔹 Model Selection & Training:

• Used OpenAI GPT-4-turbo fine-tuned on customer support transcripts
• Implemented RAG (Retrieval-Augmented Generation) using FAISS for semantic search
• Integrated LangChain to dynamically retrieve knowledge from updated FAQs

🔹 Architecture & Deployment:

• Hosted the AI model on AWS Lambda + API Gateway for scalability
• Used AWS Bedrock + Vector Databases (Pinecone/Weaviate) for fast retrieval
• Integrated with Zendesk + WhatsApp + Web Chat via REST APIs

🔹 Automation & NLP Improvements:

• Built a classification model using BERT to determine when to escalate to human agents
• Integrated sentiment analysis (VADER, TextBlob) to handle angry customers proactively

Results

✅ 50% reduction in support costs by automating 80% of queries
✅ Response time cut from 5 minutes to 30 seconds
✅ CSAT (Customer Satisfaction) Score increased by 30%
✅ System handles 5,000+ concurrent users without latency issues

📌 Tech Stack: OpenAI GPT-4, AWS Lambda, API Gateway, LangChain, FAISS, Pinecone, BERT, VADER

🔹 “The AI chatbot now resolves 4 out of 5 support requests instantly—our customer experience has never been better!” – [Client Testimonial]