Building Dukaan Sathi: An AI Voice Assistant for India's Kirana Stores

Sanyam Kansal — Sat, 15 Aug 2026 17:13:55 +0000

Over the past 9 days, I took on the Voice for Bharat Challenge to build an AI Voice Agent tailored for India. The result? Dukaan Sathi, a fully functional voice assistant designed specifically for local Kirana store owners (dukaandars).

Here is the story of how Dukaan Sathi evolved from a simple starter template into a multi-agent powerhouse capable of managing inventory, handling escalations, and speaking in authentic, pure Hindi.

🚀 The Problem & The Solution

India runs on Kirana stores. Dukaandars are incredibly busy people, often juggling inventory, customer queries, and billing all at once. They don’t have the time to sit at a laptop and type data into complicated software.

Why voice? Voice is the most natural, frictionless interface. I wanted a Dukaandar to be able to just speak to their phone: "Mere paas kitna chawal bacha hai?" (How much rice do I have left?) and get an instant, helpful response.

Dukaan Sathi is that solution. It is a helpful, polite, and intelligent voice agent that acts as a digital assistant for the store.

🛠️ Key Features Built Over 9 Days

Dukaan Sathi isn't just a basic chatbot with text-to-speech bolted on. It's a complete, intelligent system. Here are the highlights of what I built:

Authentic Indian Voices: Powered by Murf Falcon, Dukaan Sathi uses extremely natural-sounding Indian voices. The main agent uses a polite female voice (Anisha), ensuring a welcoming tone.
Strict Native Script Guardrails: One of the most important features I enforced was ensuring the LLM always outputs Hindi in the native Devanagari script (नमस्ते) and never in Romanized Hinglish (namaste). This ensures the text-to-speech engine pronounces words perfectly without awkward accents.
Persistent Memory: The agent remembers returning customers! Using SQLite and LiveKit's Job Context, the agent seamlessly pulls up past interactions so it doesn't have to re-ask for your name.
Tool Use & Analytics: The agent can actively fetch stock balances and log call outcomes. I built a real-time Call Analytics Dashboard in Next.js that updates the moment a call ends!
Human Escalation: When a customer gets angry or the agent doesn't know the answer, it triggers a create_escalation tool that saves a transcript and logs a ticket for the human owner to review.
Multi-Agent Handoffs: The crowning achievement! If a user asks for a refund, Dukaan Sathi transfers the call to a dedicated Returns Specialist. The caller actually hears the voice change to a completely new male voice (Samar), and he instantly knows the context of the conversation.

🧗 The Difficult Parts & Lessons Learned

Pretending everything worked perfectly on the first try wouldn't be helpful! Here are some hurdles I faced and how I solved them:

1. The Language Barrier (Romanized vs. Devanagari)

The Problem: LLMs naturally default to writing Hindi in the Latin alphabet (Hinglish) because that's how a lot of their training data looks. However, when you feed Romanized Hindi into an Indian Text-to-Speech engine, the pronunciation often breaks down or sounds unnatural.
The Solution: Heavy prompt engineering. I had to explicitly instruct the agent: "Always write every language in its own native script. Hindi -> Devanagari (नमस्ते), never romanized." This instantly fixed the pronunciation issues.

🛠️ Build Your Own Voice Agent

Want to build your own version of Dukaan Sathi? It's easier than you think. You need four main components:

Speech-to-Text (STT): To transcribe the user's voice.
LLM: The "brain" (e.g., OpenAI or Gemini).
Text-to-Speech (TTS): To generate the agent's voice (Murf Falcon is highly recommended for Indian voices).
Real-time Transport: LiveKit handles the ultra-low latency WebRTC streaming.

Getting Started

Clone the Starter: Start with a LiveKit + Next.js + Python template.
Environment Variables: You'll need API keys for LiveKit, your LLM, and your TTS provider. Never commit these to GitHub! Always store them in a .env.local (frontend) and .env (backend) file.
Run the Backend (Python): Type uv run src/agent.py dev in your terminal.
Run the Frontend (Next.js): Type npm run dev in your terminal.
Test it: Open localhost:3000, click connect, and start talking! You can write a custom SYSTEM_PROMPT in agent.py to give your agent any personality you want.

This 10-day challenge proved that voice AI is no longer a futuristic concept—it is accessible, buildable, and ready to solve real-world problems in India today.

Thanks to LiveKit and Murf AI for providing the incredible tools that made this possible!

Want to see the code behind Dukaan Sathi? Check out the full source code for my 10-day journey on GitHub here: https://github.com/sanyamkansal/AI-Voice-Agent