India’s BharatGen: Building an Indigenous ChatGPT for Multilingual Governance

With generative AI like ChatGPT and Deepseek taking over the world by storm, these tools have captured global imagination, from writing essays, drafting emails, coding apps, and even simulating human conversation with surprising fluency. However, while these models are powerful, they often speak with a distinctly Western accent, linguistically, culturally, and contextually. For a country as linguistically rich and culturally complex as India, that’s a problem. With over 1.4 billion people, 22 official languages, and hundreds of dialects, India’s digital transformation demands more than just translation. It needs AI that can think in Indian languages, understand Indian realities, and assist in everything from public service delivery to village-level governance. That’s where BharatGen comes in.

The Idea Behind BharatGen

Most AI models out there today are brilliant, but they usually speak one language fluently: English. And while that’s great for many, it leaves out a massive chunk of the world, especially a country like India, where linguistic diversity is the foundation. That’s where BharatGen steps in. It’s India’s answer to a big, global question: How do we build AI that truly speaks our languages, understands our culture, and serves our people?

India is Multilingual, and So Should Our AI Be

India has 22 official languages and hundreds of dialects spoken across its vast geography. Yet, most AI tools today barely scratch the surface when it comes to supporting even a handful of these. BharatGen is being built specifically to change that.

Whether it's someone in a village in Odisha speaking Sambalpuri or a government officer in Tamil Nadu issuing notices in Tamil, BharatGen aims to understand, process, and respond in these local languages, naturally and accurately.

Culture Matters in AI

Language isn’t just about words it’s about context, tone, and cultural meaning. Global models often miss the mark on Indian nuances. Ever tried asking a Western AI model to explain jugaad or panchayat dynamics? It gets...awkward.

BharatGen is being trained on India-centric data. Think government documents, policy drafts, local news, and even real-world dialect samples. It’s being taught to think like India, not just translate words into Hindi or Telugu.

Self-Reliance: Not Just a Buzzword

The idea of Atmanirbhar Bharat (self-reliant India) isn’t just about making physical goods, it’s also about owning our digital infrastructure. Right now, most AI models we use are built abroad, using foreign data, and governed by foreign rules.

BharatGen changes that. It’s fully homegrown, developed by Indian research institutions like IIT Bombay, and backed by our own government. That means we control the data, the ethics, the direction — everything.

Everyone Gets to Build With It

One of the most exciting things? It’s designed to be open-source and collaborative. That means developers, startups, researchers, including students, can build on top of it.

This opens the floodgates for innovation. Think hyper-local apps, AI-powered translators, or education tools in tribal languages. BharatGen becomes the platform. India becomes the builder.

Technological Architecture

So how is BharatGen actually built? Let’s peel back the layers and explore the fascinating stuff.

Built by India, for India (Under a National Mission)

BharatGen is spearheaded by the TIH Foundation for IoT and IoE at IIT Bombay, operating under the National Mission on Interdisciplinary Cyber‑Physical Systems (NM‑ICPS), DST. Over 25 institutions, including IITs, IIIT Hyderabad, and IIM Indore, are part of its consortium. This is a national-scale mission with institutional accountability and governance baked in.

Model Design: Multilingual, Multimodal, Modular

At its core, BharatGen is a decoder‑based Transformer model (think GPT‑style), with architectures ranging from 1.5 billion to 40 billion parameters, optimized for Indian languages and use cases. However, the platform is multimodal, handling speech, images, and written content seamlessly. That lets it support voice bots, image-to-text OCR for regional scripts, and much more.

From Data to Model: Bharat Data Sagar & Compute Backbone

One pillar of BharatGen’s architecture is its data infrastructure, Bharat Data Sagar, a curated, consent-based repository of text, speech, dialects, and visuals from all parts of India, including tribal languages and niche dialects.

On the compute side, BharatGen leans on India’s advanced HPC infrastructure: AIRAWAT-PSAI, the country’s largest AI supercomputer (13 petaflops), plus access to thousands of state-of-the-art GPUs (AMD Instinct MI200/MI300, NVIDIA H100/H200) hosted under the IndiaAI compute ecosystem.

Efficiency & Indian Context: Smart Fine-tuning

Given that many Indian languages are data-poor, BharatGen uses data-efficient learning techniques. These include subset selection, knowledge distillation, mixture-of-experts, and curriculum learning, all aimed at delivering high-quality performance with limited data support. They’re also implementing Indian-context SFT (supervised fine-tuning) and reinforcement learning to ensure outputs feel locally relevant and context-aware.

Ecosystem Integration & Future-Ready Design

Architecture-wise, BharatGen is also modular and built for growth. It supports API‑based access, containerized deployment (e.g. via Docker), and optimization frameworks like ONNX and TensorRT for low-resource environments. It’s designed to integrate with India’s digital public infrastructure (DPI) systems (think DigiLocker, ONDC, UPI) as a multilingual AI layer for government and citizen platforms.

Concluding Thoughts

BharatGen is the beginning of a long, ambitious journey toward building a truly inclusive digital future. As India continues to expand its AI capabilities, the real challenge lies not just in scaling the technology, but in embedding it meaningfully across sectors, from agriculture and healthcare to education and public service.

The roadmap ahead involves refining the model with broader datasets, adding support for more dialects, enhancing real-time multimodal interaction, and ensuring deployment even in low-resource settings. Perhaps most importantly, it calls for continued collaboration between government, academia, startups, and civil society to keep BharatGen rooted in openness, ethics, and inclusivity.