{
  "id": 965520,
  "title": "Building Vyapar AI: My 10-Day Journey Building an Indian Voice Agent with Murf Falcon",
  "url": "https://urgent.news/2026/08/15/building-vyapar-ai-my-10-day-journey-building-an-indian-voice-agent",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-15T05:08:12.000Z",
  "source": {
    "name": "Dev.to",
    "slug": "dev-to",
    "url": "https://dev.to/sudipto_bhadra/building-vyapar-ai-my-10-day-journey-building-an-indian-voice-agent-with-murf-falcon-1fef"
  },
  "original_language": "en",
  "account": "Building Vyapar AI: A 10-Day Journey Creating an Indian Voice Agent with Murf Falcon\n\nOver the course of 10 days, I embarked on an ambitious journey to build Vyapar AI, an AI-powered voice assistant tailored for Indian businesses. The vision was to create a system that could understand and assist users through natural voice conversations in Hindi and other Indian languages. Throughout this project, I encountered numerous challenges, from API rate limits to networking issues and voice output problems, but each obstacle became a valuable learning experience.\n\nVyapar AI is an Indian-focused voice assistant designed to make business assistance as easy as speaking to another person. Users can simply speak their queries, and the AI will respond with the relevant information. For instance, a user can ask, \"आज गेहूं के आटे का भाव क्या है?\" (Today's wheat flour price?), and Vyapar AI will reply with the current price, \"आज के अपडेट के अनुसार गेहूं का आटा 45 रुपये प्रति किलो है।\" This approach is particularly useful for users who prefer speaking over typing, especially in Indian-language and code-mixed conversations.\n\nThe primary motivation behind building Vyapar AI was to provide a more natural and user-friendly experience for Indian businesses. Traditional applications often require users to navigate complex menus, search for information, and take actions, which can be time-consuming and frustrating. With a voice interface, these steps are significantly reduced, allowing users to interact with the system more intuitively. Additionally, voice has the advantage of accommodating various Indian languages, making the assistant feel more relevant and practical for Indian users.\n\nDuring the 10-day development process, I followed several key steps to create Vyapar AI. On the first two days, I focused on establishing the basic architecture and defining essential components such as personality, objectives, conversation behavior, safety guardrails, response style, and Indian voice requirements using Murf Falcon, a fast TTS API. These initial steps were crucial in setting a predictable and helpful tone for the voice agent.\n\nOne of the most significant aspects of the project was integrating Murf Falcon to provide Vyapar AI with a natural Indian voice. The voice is not merely an output layer; it plays a crucial role in shaping the user experience. A robotic or unnatural voice can detract from the otherwise powerful capabilities of an AI agent. Murf Falcon helped me achieve a more natural conversational experience for Indian users, enhancing overall usability.\n\nThe core voice pipeline of Vyapar AI consists of several key components. When a user speaks, their voice input is captured through a microphone and fed into a Speech-to-Text system, such as Deepgram. The resulting text is then processed by an LLM (Reasoning Layer) to understand the user's intent and generate an appropriate response. This response is subsequently converted back into speech using Murf Falcon's Text-to-Speech capabilities. In the background, a real-time communication platform like LiveKit handles the seamless exchange of audio data between the user and the AI agent.\n\nTo give Vyapar AI a distinct personality, I defined clear roles and objectives for the agent. Vyapar AI was designed to communicate naturally in Hindi when appropriate and to adhere to specific safety boundaries, tool usage rules, and escalation procedures. By establishing a clear role and behavior for the AI agent, the user experience became more engaging and reliable.\n\nMemory is a crucial component of a useful AI assistant, as it allows the agent to retain context from previous conversations. For Vyapar AI, I implemented a memory system that enables the assistant to retain information about returning users. This memory storage system can access important details from past conversations, allowing for more personalized and context-aware responses. By integrating memory into Vyapar AI, the system moved closer to the functionality of a true assistant rather than a simple chatbot.\n\nTo enhance the agent's capabilities, I connected Vyapar AI to various tools that could retrieve and provide relevant information. For example, when a user asked for business information, such as the price of wheat flour, the AI could understand the intent and call a specific tool, like lookup_product_inventory(). The tool would then retrieve the necessary data and return the result to the AI, which would subsequently communicate the information to the user in a natural, conversational manner. This integration of LLMs with tools is essential for creating a more robust and versatile AI agent.\n\nAnother important feature I explored was the ability for Vyapar AI to initiate outbound calls. This capability allows the assistant to proactively reach out to users, rather than only responding to inbound calls. However, this functionality introduced new challenges related to telephony configuration, such as setting up SIP connections, configuring phone numbers, trunks, and establishing proper connections. Overcoming these technical hurdles was a significant part of the project, and it highlighted the complexity involved in building a fully-featured AI voice assistant.\n\nRecognizing when to escalate a conversation to a human is critical for maintaining the reliability of an AI system. Vyapar AI includes a human-escalation mechanism that allows the assistant to determine whether a user's query can be handled effectively by AI or if human intervention is necessary. For instance, if the AI encounters a query it cannot safely address, it will respond with, \"Let me connect you with a human.\" This escalation process ensures that users receive the most accurate and helpful responses, even when dealing with complex or sensitive matters.\n\nIn conclusion, building Vyapar AI was an enlightening and challenging 10-day journey. Through the integration of various technologies, including Murf Falcon for natural voice generation, Deepgram for speech-to-text conversion, LLMs for understanding user intent, and tools for retrieving relevant information, I created an AI-powered voice assistant tailored to the needs of Indian businesses. The project not only pushed me to overcome numerous technical challenges but also reinforced the importance of user experience, language relevance, and contextual awareness in building effective AI systems. Vyapar AI serves as a testament to the potential of AI in simplifying and enhancing business interactions, making it easier for users to access the information and support they need through natural voice conversations.",
  "summary": "* 10 Days of Voice Agents — VoiceForBharat Edition * What if a small business owner could simply speak to an AI assistant in Hindi or a mix of Indian languages and get useful business information instantly? That question led me to build Vyapar AI, an AI-powered voice assistant designed around the needs of Indian businesses. Over the last 10 days, I went from building a basic conversational voice…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 2,
    "also_reported_by": [
      {
        "outlet": "Dev.to",
        "title": "Building Bharat Buddy: My 10-Day Voice AI Journey with Murf Falcon",
        "url": "https://urgent.news/2026/08/15/building-bharat-buddy-my-10-day-voice-ai-journey-with-murf-falcon",
        "published": "2026-08-15T05:53:01.000Z"
      }
    ]
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}