zao whatsapp chatbot

About Zao

Inspiration

Growing up in remote Kenya, we witnessed firsthand how limited access to agricultural expertise impacts farming communities. Despite technological advancements in agriculture, most solutions require expensive devices or complex applications. Zao was born from our vision of democratizing agricultural knowledge through WhatsApp—a platform already familiar to millions of African farmers.

What it does

Zao transforms WhatsApp into a sophisticated farming companion by offering:

AI-driven agricultural advisory services
Contextual farming recommendations
Farm planning and management guidance
Real-time crop advisory support

How we built it

We leveraged a modern technology stack:

OpenAI for intelligent agricultural recommendations
MongoDB for:
- User data and chat history
- Vector embeddings of Kenyan farming practices (RAG)
- Conversation state management
WhatsApp Business API for communication
Python backend with FastAPI
LangGraph for conversation workflows

Challenges we ran into

Key technical and operational challenges included:

Constraining AI responses to strictly agricultural topics while maintaining meaningful interactions
Ensuring recommendations align with local farming practices in Kenya
Engineering effective conversation flows within WhatsApp's constraints
Balancing automated AI responses with accurate farming context

Accomplishments that we're proud of

We've successfully:

Retrieval Augmented Generation (RAG) to enhance relevance and accuracy of recommendations
Implemented persistent conversation memory enabling natural, contextual interactions
Created a seamless WhatsApp-based farming advisory system
Developed an accessible platform that works with technology farmers already have
Built a system that delivers expert agricultural advice in an approachable format

What we learned

The development journey taught us:

The role of Retrieval Augmented Generation (RAG) in delivering region-specific agricultural insights
Methods for maintaining conversation context in chat-based applications
The importance of understanding local farming contexts
How to effectively combine AI capabilities with practical agricultural knowledge
Techniques for optimizing AI models for resource-constrained environments
The value of user-centered design in agricultural technology