Bhashini, or Bhasha Interface for India, is India's National Mission on Natural Language Translation. The Government of India's MeitY launched it in July 2022, and the Digital India Corporation manages it. It exposes speech recognition, translation, and text to speech for Indian languages.

Which languages does Bhashini support?

Bhashini covers the 22 scheduled, constitutionally recognised Indian languages, across both voice and text. Per government reporting it has delivered 350+ AI models covering 20+ languages.

What are the main challenges of building on Bhashini?

The biggest ones are code-mixed speech such as Hinglish, accent and dialect variation, uneven model quality across languages, latency pressure in real-time voice, and noisy telephony audio. Domain-specific vocabulary such as proper names and product terms also needs special handling.

Why use Bhashini instead of foreign speech APIs?

Bhashini is a sovereign, India-built stack aligned with Digital India, and it offers breadth that is hard to match by stitching foreign APIs together: all 22 scheduled languages across voice and text in one ecosystem. Its public models keep improving and are reached through standardised ULCA APIs.

Does Tomatovation use Bhashini?

Yes. We use Bhashini inside JATAS AI, our Voice Communicator platform, which is live as the official AI Voice Communicator at Ganpat University. Bhashini provides the Indian-language capability within our worldwide offering.

How is Bhashini accessed by developers?

Bhashini is built on ULCA, the Universal Language Contribution API, an open, open-sourced, standardised data and API platform. Each AI model is exposed with a unique Model ID and a try-out interface for integrators.

Back to Blog

28 Jul 2026•7 min read•AI & Voice

Building Voice AI on Bhashini: The Challenges We Faced and Why We Still Use It

By Tomatovation

BhashiniVoice AIIndian LanguagesSpeech RecognitionJATAS AI

Bhashini is India's National Mission on Natural Language Translation, a government-built platform that lets software understand and speak the 22 scheduled Indian languages across both voice and text. We build on it inside JATAS AI, our Voice Communicator, and this article explains the real challenges we hit along the way and why we still choose it.

Voice AI in Indian languages is genuinely hard. In the sections below we define what Bhashini is, walk through the integration problems we faced, cover the benefits that keep us building on it, and show how we orchestrate around its gaps to deliver accurate voice experiences as one capability inside a worldwide offering.