Building AI Chatbots That Actually Serve Real Customers

1 min readBy Alpit Mali
  • AI Chatbots
  • LangChain
  • RAG
  • WhatsApp Business API
  • OpenAI
  • Anthropic Claude
  • Qdrant
  • LLM

Most AI chatbot demos look impressive. Few survive contact with real customers placing real orders in languages the developer doesn't speak.

Over the past year, I built and shipped a production AI chatbot for JIJO Ventures — a regional FMCG brand — that supports 5 languages (Marathi, English, Gujarati, Hindi, Spanish) and runs entirely on WhatsApp. Customers browse products, place orders, and track deliveries without ever leaving the chat.

The Stack

  • LangChain for orchestration
  • OpenAI GPT-4o-mini and Anthropic Claude as the LLM backbone
  • Qdrant vector database for RAG
  • WhatsApp Meta Business API as the interface

What I Learned

1. RAG needs domain-specific tuning. Generic embeddings fail when your product catalog includes regional FMCG items. Building a training dashboard where admins seed domain knowledge made the difference between a toy and a tool.

2. Multilingual is harder than it looks. Language detection, context switching mid-conversation, and handling code-mixed inputs (Hinglish, Marathi-English) required careful prompt engineering and fallback chains.

3. WhatsApp is a different UX paradigm. No buttons, no carousels (at first). Every interaction is text-first. Designing conversational flows that feel natural while being deterministic enough to process orders is a design challenge as much as an engineering one.

The chatbot now handles real transactions daily. Not a demo — a business tool that replaced manual order-taking for an entire sales channel.

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