How I Automated 70% of Customer Support with an AI Chatbot (Django + OpenAI)

I'm Kirill Strelnikov, a freelance Python/Django developer based in Barcelona, Spain. I build AI-powered products for businesses across Europe. This is a real case study from a client project. The Problem An e-commerce clothing store was drowning in repetitive support tickets: "Where is my order?", "What's your return policy?", "Do you have size X in stock?" — the same 15-20 questions making up 80% of all inquiries. Two full-time support agents were spending most of their day copy-pasting answers. The client asked me to build an AI chatbot that could handle these repetitive queries automatically, while escalating complex issues to human agents. The Solution: RAG Chatbot with Django + OpenAI I built a Retrieval-Augmented Generation (RAG) chatbot — not a simple FAQ bot, but one that understands context and generates natural answers from the store's actual data. Architecture User message → Django backend (receives via REST API) → Vector search (pgvector) finds relevant docs → OpenAI GPT-4