Building Voice AI on Bhashini: The Challenges We Faced and Why We Still Use It
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.
Key takeaways
- Bhashini is India's government-built language AI platform, launched by MeitY in July 2022, covering all 22 scheduled Indian languages across speech and text.
- The hard parts are real: code-mixed speech like Hinglish, accent and dialect variation, uneven model quality across languages, and the low-latency demands of real-time voice.
- We still build on it for its breadth, its sovereign India-built foundation, and public models that keep improving behind standardised ULCA APIs.
- Tomatovation uses Bhashini inside JATAS AI, live as the official AI Voice Communicator at Ganpat University, as the Indian-language capability within a worldwide offering.
What is Bhashini?
Bhashini, short for Bhasha Interface for India, is India's National Mission on Natural Language Translation. The Government of India's Ministry of Electronics and Information Technology (MeitY) launched it in July 2022, and the Digital India Corporation, through its Digital India Bhashini Division, manages it.
Its goal is to make digital services and content accessible in Indian languages, through both voice and text, so that more people can take part in the digital economy and the digital language divide narrows. It covers the 22 scheduled, constitutionally recognised Indian languages, and it is aligned with the broader Digital India programme.
For builders, Bhashini exposes three core AI services, along with supporting modules, and everything sits on ULCA, the Universal Language Contribution API. ULCA is an open, open-sourced, standardised data and API platform where each model carries a unique Model ID and a try-out interface for integrators. The underlying models are contributed by Indian research institutions such as IIT Bombay, IIT Madras, IIIT Hyderabad, and CDAC.
- Automatic Speech Recognition (ASR) to turn speech into text.
- Neural Machine Translation (NMT) to translate between languages.
- Text to Speech (TTS) to produce natural spoken output.
- Supporting modules such as disfluency correction, named entity recognition, and OCR for Indian scripts.
The challenges we faced building on Bhashini
None of the points below is a criticism of Bhashini. They are the honest realities of doing Indian-language voice AI at production quality, and we planned our engineering around each one rather than wishing it away.
The through-line is that real conversations are messy. People switch languages mid-sentence, speak with strong regional accents, call from noisy places on weak connections, and use names and terms no general model has seen. A production voice system has to stay accurate through all of that.
- Code-mixed speech, for example Hinglish, is genuinely hard, because training data and tooling for code-switched language remain limited.
- Accent, dialect, and regional variation within a single language change how the same word sounds from one speaker to the next.
- Model quality varies across languages, since widely spoken languages tend to have stronger models than low-resource ones.
- Real-time voice needs low latency, and chaining ASR, then business logic, then TTS adds latency pressure at every step.
- Noisy, low-bandwidth telephony audio degrades recognition accuracy.
- Domain-specific vocabulary, such as proper names, product terms, and admissions jargon, needs special handling.
- As an evolving public platform, model versioning by Model ID and throughput considerations mean production use needs real engineering around it.
Why we still build on Bhashini
Weighed against those challenges, the reasons to stay are strong. Bhashini gives us a sovereign, India-built stack aligned with the Digital India programme, which suits organisations that care about their data staying within an Indian public-good ecosystem.
It also offers breadth that is hard to match by stitching together foreign APIs: all 22 scheduled languages, across voice and text, in a single ecosystem. Because the public models keep improving and are reached through standardised ULCA APIs, our platform benefits as the ecosystem itself matures, without us re-plumbing everything each time.
In short, the trade-off favours Bhashini. The challenges are solvable with good engineering, while the coverage, the sovereign foundation, and the steady improvement of the models are difficult to replicate any other way.
The benefits of Bhashini for Voice AI
Put simply, Bhashini lowers the barrier to serving speakers of many Indian languages, including low-literacy users who are far better served by voice and TTS than by text on a screen. That combination of reach and inclusion is what makes it compelling for voice.
- Coverage of all 22 scheduled Indian languages across both speech and text.
- A sovereign, India-built foundation aligned with the Digital India programme.
- Continuously improving public models accessed through open, standardised ULCA APIs.
- Lower cost and effort to reach many languages, including low-literacy users through voice and TTS.
- Per government reporting, 350+ AI models covering 20+ languages.
How Tomatovation delivers accurate Indian-language voice AI
We treat Bhashini as a powerful capability, not a finished product, and we build the orchestration that turns it into a dependable experience. In practice that means caching, sensible fallbacks between models, monitoring, and dedicated handling for domain vocabulary and noisy telephony audio, so that a real, real-time conversation stays smooth for the caller.
This is exactly how JATAS AI, our Voice Communicator platform, works. JATAS AI is live as the official AI Voice Communicator at Ganpat University, where it handles the admission lifecycle in the languages callers actually speak. Bhashini is the Indian-language capability inside that platform, and JATAS AI itself is part of a worldwide offering, so Indian-language voice is one strength among many rather than the whole story.
Frequently Asked Questions
What is Bhashini?
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.