India’s artificial intelligence journey is no longer about catching up — it’s leading in areas that matter. While the world races to scale billion-parameter models, India is quietly building intelligence that works for a billion people. It’s a different kind of ambition not defined by size, but by relevance.
In a country where over 20 official languages and hundreds of dialects coexist, where connectivity ranges from urban 5G to rural 2G, and where digital literacy is a spectrum, AI can’t just be smart, it must be attuned. It’s a challenge and an opportunity that India is uniquely positioned to take on.
AI Rooted in Context: BharatGPT and the Language Imperative
At the heart of India’s linguistic AI revolution is BharatGPT, a large language model developed by CoRover.ai. The idea behind it is simple but bold, AI should speak the language of the people. With support for over 22 Indian languages in text and more than 14 in voice, BharatGPT aims to make AI accessible across the country’s vast linguistic landscape.
What sets BharatGPT apart isn’t just its multilingual capabilities, but its contextual intelligence. Built atop transformer architectures, it’s optimized for the nuances of Indian dialects, cultural phrasing, and low-bandwidth environments. It can help a farmer inquire about subsidies in Bhojpuri, a student in Tamil Nadu inquire about government scholarships or a citizen file a digital grievance in Canada.
The Ground Reality: Challenges with Clarity
Despite the momentum, India isn’t pretending to build ChatGPT-scale models. And that’s okay.
India is not yet operating at a global scale, because the country’s needs are different. The real challenge is not building the biggest model, but the right model. Data scarcity in Indian languages, especially dialects, makes it hard to train robust, inclusive systems. Public datasets are fragmented, and annotations are expensive and often inconsistent.
Then there’s the computing. Training large models needs high-performance GPU clusters and robust infrastructure - luxuries not readily accessible to Indian academia and many startups. But instead of stalling innovation, this has sparked it. Indian researchers are working on smaller, optimized models, domain-specific transformers, and hybrid architectures tailored for deployment in edge devices and rural environments.
What’s Happening right now
India has the talent! Indian universities produce world-class AI researchers, and startups are deeply rooted in real-world challenges - from telemedicine in Tier 2 cities to AI-powered education in local languages.
Government initiatives like the IndiaAI Mission and Bhashini aren’t just slogans, they’re building blocks of a thriving ecosystem. Bhashini, for instance, aims to create foundational language models and translation tools for every Indian language.
There’s also strong momentum in open collaboration. Open-source efforts like AI4Bharat, IndicNLP, and multilingual datasets created by institutions like IIT Madras are driving community-led progress that scales far beyond institutional walls.
Why India's AI Matters Globally
Many of these efforts resonate beyond national borders—especially in regions facing similar challenges: linguistic diversity, limited connectivity, and populations underserved by English-first tech.
An AI that can understand a semi-literate user in Chhattisgarh? That’s a model ready for Southeast Asia, Africa, or Latin America.
An edge-optimized diagnostic tool that works offline? That’s the future of healthcare AI in the Global South.
Ethics, Trust, and the Need for Indigenous Evaluation
As AI systems expand, the question isn’t just can we build them, but should we trust them?
There’s a strong case for homegrown frameworks for ethics, bias, and data governance. Imported standards are unable to work in a society where identity is complex, and language carries social meaning. With legislation like the Digital Personal Data Protection (DPDP) Bill taking shape, Indians can in responsible innovation.
What’s Next
This AI moment isn’t a one-off, it’s the start of lasting change. The momentum is moving beyond just building models and towards creating systems that actually serve people in governance, education, agriculture, public infrastructure, and more.