The Multilingual Challenge
Most out-of-the-box Voice AI solutions are built for American English. When applied to Indian accents or regional languages, they fail catastrophically. The transcription is garbled, the latency is unbearable, and the generated voice sounds robotic.
To build effective voice agents for the Indian market, you need a specialized stack.
The Latency Problem
For a voice agent to feel natural, the time from when the user stops speaking to when the AI starts replying must be under 700 milliseconds. Anything longer feels awkward and causes users to interrupt the bot.
To achieve this, we cannot rely on standard APIs. We use specialized hardware (Groq LPU inference engines) and optimized open-source models.
The Krinok Voice Tech Stack
For our KrinoK-Voice project, we built a system that supports 11 Indian languages with sub-second latency. Here is how:
- Orchestration: Pipecat. An open-source framework designed specifically for real-time multimodal AI.
- Speech-to-Text (ASR): Deepgram or localized models from Sarvam AI, which are specifically trained on Indic languages and code-mixing (e.g., "Hinglish").
- LLM: Llama 3 running on Groq for near-instantaneous token generation.
- Text-to-Speech (TTS): ElevenLabs for hyper-realistic voices, or localized Indic TTS providers.
Real Use Cases for Indic Voice AI
The applications in India are massive, particularly for reaching the "next billion users" who prefer voice over typing:
- Automated inbound customer support in regional languages.
- Outbound appointment booking and confirmation calls for clinics.
- Lead qualification for real estate and insurance.
Want to deploy a multilingual voice agent? Contact Krinok.