> ## Content Index
> Fetch the complete content index at: https://blog.8loop.ai/llms.txt
> Use this file to discover other available public pages before exploring further.

# From Hello to Namaste: Building Real-Time, Multilingual Voice Bots with Indian AI (Powered by Sarvam)
- URL: https://blog.8loop.ai/from-hello-to-namaste-building-real-time-multilingual-voice-bots-with-indian-ai-powered-by-sarvam/
- Published: 2025-10-17T12:42:41.000Z
- Updated: 2026-07-09T14:43:38.000Z
- Author: Avaneesh
- Tags: #Import 2026-07-09 16:36

Voice is a commonly used modality for human conversation. The channels businesses choose to support customers depend on a few factors:

- **Phone**: The default when urgency or anxiety is involved, or when back-and-forth coordination is required or when white-glove treatment is expected.
- **Email**: Ideal when you need accountability or must log a request as proof.
- **Chat**: Best for simple questions and quick actions—closing the loop conveniently. OpenAI is emerging as a new channel for straightforward queries and may even become a unified canvas for various interactions.

While talking to potential users, voice automation repeatedly came up as a high-value opportunity—particularly for an Indian customer who cited multilingual support as a deal breaker. With Sarvam gaining traction, we decided to build a small proof of concept to see if a voice bot could deliver real value.

Preview of Tamil voice bot:

Preview of English voice bot:

---

### Building Scalable, Multilingual Voice Bots

Thanks to recent advances, creating a real-time, production-ready voice bot in Indian languages is no longer a distant dream. By combining the right tools, we built a bot that understands and speaks Hindi, Tamil, Kannada, Bengali, and more.

### Stack Overview

1. **LiveKit**
  - Real-time media engine with SIP trunking for phone integration
  - WebRTC backbone for low latency and high efficiency
  - Multi-call handling in a single process
  - Plugin-friendly, which lets us integrate non-native tools like Sarvam
2. **Sarvam STT** (Speech-to-Text)
  - High-accuracy support for many Indian languages
  - Requires buffered chunks rather than true streaming
  - Custom plugin captures LiveKit audio and sends it to Sarvam’s API in near real time
3. **OpenAI GPT-4.1**
  - Natural-language understanding and response generation
  - Processes transcribed text and returns contextual replies
4. **Sarvam TTS** (Text-to-Speech)
  - Natural-sounding voices in Indian languages
  - Accepts full text, returns audio, and streams it back via LiveKit
5. **Silero VAD** (Voice Activity Detection)
  - Detects when the caller speaks or pauses
  - Enables turn-taking logic including support for barge-in (i.e. user interruptions)
  - Implements smart buffering to balance speed and accuracy

### End-to-End Flow

1. Caller speaks → LiveKit captures audio
2. Audio → Sarvam STT → Transcribed text
3. Text → GPT-4.1 → Generated response
4. Response → Sarvam TTS → Synthesized audio
5. Audio → LiveKit → Played back to the caller

---

## Key Learnings

- **Feasibility**: Real-time multilingual voice bots are achievable today.
- **Local-Language Support**: Sarvam fills a critical gap in Indian STT and TTS.
- **Infrastructure**: LiveKit simplifies media handling and scales efficiently.
- **Integration**: Custom glue code is still needed for real-time handling and API limitations.

---

## Next Steps

1. **Quality Testing**: Evaluate bot responses across diverse datasets.
2. **Brand Voice**: Define name, tone, and personality for the bot.
3. **Localization**: Enhance local-language fluency for a more natural feel.
4. **Scale Testing**: Stress-test at production scale to validate performance.

---

### Open Questions

- Do you think voice bots will become the future of communication?
- Would you feel comfortable speaking with a brand’s voice bot?

Feel free to share any feedback or ideas!