BharatGen and the Rise of Indigenous AI: How India’s GenAI Startups and Policies Are Rewriting the Global AI Playbook
India’s GenAI ambitions are gaining momentum with BharatGen and homegrown LLM startups. Explore government strategies, ecosystem growth, and industry frontrunners reshaping the AI landscape.
NewsSutra
In a significant pivot from being a passive consumer of Western AI models, India is now rapidly evolving into a producer—and possibly, a global contender—in the generative AI (GenAI) space. At the heart of this shift is BharatGen, a government-backed initiative designed to nurture homegrown large language models (LLMs), infuse cultural-linguistic relevance, and anchor sovereign AI capabilities within the country.
But this story isn’t just about government ambitions. A wave of Indian startups, university-led consortia, and major IT players are accelerating efforts to create India-centric GenAI tools, shaking up global assumptions that Silicon Valley or Beijing must always lead the charge.
Let’s unpack how BharatGen fits into the broader strategy, profile some of the leading Indian LLM platforms, explore key government interventions, and spotlight the industrial braintrust driving India’s GenAI revolution.
What Is BharatGen? India’s AI Sovereignty Project
BharatGen, launched earlier this year under the aegis of Ministry of Electronics and Information Technology (MeitY) and Digital India Corporation, is not just another AI toolkit. It is a strategic LLM infrastructure project aimed at building foundational models trained on Indic languages, Indian cultural data, and localized contexts.
Core Objectives:
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Create open-source LLMs that support over 22 official Indian languages
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Develop models optimized for governance applications, such as voice-based public service access
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Provide compute resources for Indian startups and researchers via National AI Computing Grid
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Promote responsible and inclusive AI, in sync with India’s evolving Digital India Act
According to MeitY, BharatGen is “India’s answer to GPTs—infused with Indian datasets, designed for India-first applications, and built to democratize GenAI innovation.”
BharatGen is also offering cloud-based sandbox environments for developers to fine-tune base models like BharatGPT and IndicCoder on real-world use cases in agriculture, education, and justice delivery.
The Startup Scene: India’s LLM Innovators Step Forward
1. Sarvam AI – Bengaluru-based and among the earliest GenAI startups to create India-specific LLMs trained on Devanagari, Hindi-English code mix, and Indian administrative vocabulary. It recently secured funding from Lightspeed Ventures and is collaborating with IIT Madras.
Visit sarvam.ai to explore their latest model releases and toolkits.
2. KissanAI – Focuses on building GenAI for agri-advisory services, offering voice-driven crop guidance models in Telugu, Punjabi, and Marathi. Its deployment in Telangana is being scaled via Agritech Innovation Fund 2.0.
3. Gan.ai – A Mumbai-based startup enabling hyperlocal video personalization using LLMs. It powers dynamic voiceovers and dialect-specific subtitles, helping brands reach Tier-2 and Tier-3 audiences more effectively.
4. IndicNLP & AI4Bharat – While not commercial startups, these research groups at IIT Madras and IIIT-Hyderabad are crucial for building open-source corpora and LLM baselines like IndicBERT and Samanantar for Indian languages.
The AI4Bharat portal hosts multilingual datasets and pretrained models freely accessible to developers.
These platforms are playing a pivotal role in challenging the dominance of OpenAI, Meta, and Google by focusing on India-first use cases—from legal document summarization to grievance redressal bots in local dialects.
Government’s Multi-Pronged Support: Infrastructure, Policy, and Grants
The Indian government’s GenAI push is anchored in both policy architecture and fiscal incentives. Here’s how:
1. National Data Governance Framework Policy (NDGFP)
This policy creates a regulatory framework for anonymized public datasets to be shared with startups and academia through India Datasets Platform. It is enabling LLMs to access high-quality, non-English corpora from Panchayat records to public grievances.
Learn more about the framework via India Stack.
2. Digital India Innovation Fund (DIIF)
With an allocation of ₹2,500 crore, this fund is backing early-stage GenAI innovators focusing on education, agriculture, language translation, and judicial digitization.
Startups receive:
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Cloud credits from National Supercomputing Mission (NSM)
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Access to government-hosted GPU clusters
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Mentorship under Startup India GenAI Accelerators
3. Responsible AI Guidelines
India’s AI policy emphasizes transparency, traceability, and bias checks. Every BharatGen-supported LLM must comply with:
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Bias auditing tools to ensure inclusivity across caste, gender, and language
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Mandatory opt-out mechanisms for citizens contributing to datasets
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Explainability features for government-deployed bots
Industry Leaders Jump Into the Fray
India’s GenAI ambition is receiving strong support from tech majors:
1. TCS & Infosys
Both giants are investing in proprietary fine-tuning stacks that sit atop BharatGen’s base models. TCS’s Ignio 2.0 now integrates Indic reasoning layers for compliance use cases in banking and health.
2. Reliance Jio
Jio is reportedly working on its own LLM to power multilingual customer service chatbots, in partnership with BharatGen. The model will be trained on JioSaavn data for culturally contextual prompts.
3. HCLTech
Has launched the “BharatLLM Lab” at its Noida campus to co-create GenAI governance tools for state governments.
These collaborations aim to accelerate commercialization, ensuring LLMs don’t remain confined to labs or research papers.
Challenges Ahead: Compute, Talent, and Dataset Quality
Despite the momentum, India’s GenAI journey is not without hurdles.
1. Compute Bottleneck
India currently has fewer than 10,000 operational AI GPUs, compared to China’s 5 lakh+ and the U.S.’s several million. While NSM clusters are helping, cloud GPU costs remain prohibitive for smaller startups.
2. Talent Scarcity
There is an acute shortage of LLM engineers, RLHF specialists, and tokenization experts. Top AI talent continues to migrate to Meta, Google, and Anthropic due to higher pay and international exposure.
3. Language Representation Bias
Languages like Santhali, Bodo, and Maithili are still underrepresented in training datasets. While AI4Bharat is making progress, there is a need for structured linguistic data donations from civil society and universities.
Global Relevance: India’s GenAI for the Global South
What makes BharatGen and India’s LLM initiatives unique is their applicability beyond Indian borders. Countries in Africa, Southeast Asia, and Latin America face similar challenges in language diversity, infrastructure constraints, and cultural specificity.
India’s model of open-source, multilingual, low-compute GenAI can become a template for AI development in the Global South, reducing dependency on Western tech monopolies.
As Union IT Minister Ashwini Vaishnaw recently stated, “India doesn’t just want AI for itself. We want to shape the AI discourse for the 6 billion people who’ve been on the periphery of this revolution.”
Conclusion: The Age of BharatGPT Is Near
India’s GenAI story is no longer a future fantasy—it’s unfolding, byte by byte, code by code, from IIT labs to rural hackathons. With BharatGen, a supportive regulatory environment, and a rising generation of AI entrepreneurs, India is on track to become not just an AI consumer, but a meaningful creator in the global intelligence economy.
The next two years will determine whether BharatGen lives up to its promise. But one thing is clear: India’s GenAI movement is rooted in purpose, powered by policy, and propelled by a billion voices seeking digital relevance.
