Building Homegrown AI for a Digital India

India’s emergence as a global digital power now hinges on its ability to build artificial intelligence systems that are indigenous, inclusive, and aligned with national priorities. As AI increasingly shapes governance, public services, industry, and citizen engagement, the need for homegrown foundational models has become important. These models must be trained on Indian languages, local data, and real-world contexts to ensure relevance and effectiveness.

Built with the vision of creating AI systems specifically for India, Sarvam AI is an organization that is developing artificial intelligence tailored to India’s needs by building foundational components and applying them to the country’s unique linguistic, enterprise, and governance requirements. The company has built a full-stack AI platform, with everything developed, deployed, and governed entirely in India. These enterprise grade platforms reflect the country’s linguistic diversity and are designed to support public service delivery. Its work directly addresses long-standing barriers in accessibility, multilingual communication, and dependence on foreign AI infrastructure.

Driving Digital Self-Reliance through Indigenous AI Models

Strengthening indigenous AI infrastructure is central to India’s vision of technological sovereignty, digital self-reliance, and inclusive growth. In an era where artificial intelligence shapes governance, economic competitiveness, and citizen services, building AI systems rooted in local languages, datasets, and regulatory frameworks ensures that innovation aligns with national priorities and societal needs. Indigenous AI development not only safeguards strategic autonomy but also fosters economic resilience and equitable access to emerging technologies.

In this context, Sarvam AI stands out as one of the 12 organisations selected under the Innovation Centre pillar of the IndiaAI Mission to develop indigenous foundational models, with financial and compute support amounting to ₹246.72 crore. The company is building large language and speech models (LLMs) tailored for Indian languages and public service delivery, with capabilities such as voice-based interfaces, document processing, and citizen-centric applications that enhance accessibility and ease of use. By developing homegrown AI models aligned with national objectives, Sarvam AI is reducing reliance on foreign AI systems while strengthening the open-source ecosystem and enabling innovation across startups, academia, research institutions, and industry.

An AI model is a computer program trained on vast amounts of data to recognize patterns, make predictions, or generate new content, acting like a digital brain.

 

Sarvam AI’s models include:

1. Bulbul (Text-to-Speech): Available in 11 Indian languages with 39 distinct speaker voices.
2. Saaras (Speech-to-Text): Supports all 22 scheduled languages, 8kHz telephony audio, and code-mixed speech.
3. Vision (Document Understanding): Tailored for 22+ Indian languages, mixed scripts, and handwritten text

Through these foundational capabilities, Sarvam AI demonstrates how India-centric AI can evolve into scalable, resilient, and population-scale digital infrastructure, enhancing public service delivery, improving linguistic accessibility, and reinforcing India’s journey toward a globally competitive AI ecosystem.

 

Full-Stack Sovereign AI Ecosystem of Sarvam AI

Sarvam AI has built a comprehensive, full-stack sovereign AI ecosystem designed to serve enterprises, governments, developers, and creators across India. Developed end-to-end within the country  spanning compute infrastructure, foundational models, platforms, and real-world applications. The ecosystem reflects commitment to technological self-reliance in artificial intelligence.

 

An AI stack is the complete set of tools and systems that work together to build and run AI applications. These applications range from everyday tools such as Siri and Alexa, to advanced systems used in healthcare diagnostics, financial fraud detection, and transportation.

The Sarvam AI ecosystem consists of:

1. Sarvam for Conversations: Enterprise-grade (high capacity) conversational AI delivering human-like, culturally fluent voices in 11 Indian languages. Handles over 100 million+ interactions with <500ms latency, deploys within <24 hours, and achieves up to 10x ROI.
2. Sarvam for Work: A unified enterprise AI platform that accelerates value creation through an AI-assisted build–debug–optimize cycle. Open and modular, it integrates seamlessly with any model, data source, or infrastructure.
3. Sarvam AI for Content: Enables multilingual video dubbing with voice cloning and precise audio-visual sync, along with document translation that preserves layout and tone, supported by built-in quality review and editing tools.
4. Sarvam AI for Edge Intelligence: Delivers compact, low-latency multimodal AI for real-world deployment, combining edge and cloud inference to power real-time assistants, on-device NLP, and high-speed translation and summarisation.

Through this integrated architecture, Sarvam AI is not merely building applications but establishing a scalable digital backbone for India’s AI future. By converging infrastructure, language intelligence, enterprise capability, and edge deployment into one sovereign ecosystem, it positions India to innovate independently, deploy responsibly, and compete globally, while ensuring that advanced AI remains accessible, secure, and aligned with national development priorities.

 

Strategic Partnerships For Public Service Delivery

Sarvam AI’s institutional collaborations are transforming indigenous innovation into measurable public value across India. By working closely with national and state governments, the company is embedding advanced AI capabilities into critical service delivery systems.

1. UIDAI (Unique Identification Authority of India) partnered with Sarvam AI to enhance Aadhaar services using AI-driven voice interaction, real-time fraud detection, and multilingual support. A custom GenAI stack will operate within UIDAI’s secure, on-premise infrastructure, supporting 10 Indian languages with real-time enrolment feedback and fraud alerts.
2. The Government of Odisha in collaboration with Sarvam AI are establishing a 50MW AI-optimized Sovereign AI Capacity Hub to serve as a national compute backbone. It will support AI use cases in mining, industrial safety, and Odia-language skilling, contributing to the sovereign compute grid.
3. The Government of Tamil Nadu and IIT Madras, in collaboration with Sarvam, are developing Digital Sangam, India’s first Sovereign AI Research Park, anchored by a 20MW AI data center to integrate advanced compute, research, and startup incubation for large-scale AI applications.

Collectively, these initiatives demonstrate how coordinated public partnerships can deploy homegrown AI infrastructure at population scale.

 

Conclusion

Sarvam AI represents a critical pillar of India’s domestic AI ambitions, aligning innovation with national priorities under the IndiaAI Mission. By developing indigenous large language and speech models in Indian languages, Sarvam AI is expanding access to digital services, strengthening open innovation, and reducing reliance on foreign AI systems. It is also growing role in public service delivery, startup enablement, and responsible AI frameworks reflects the government’s vision of technology as a tool for inclusive growth. Sarvam AI is steadily helping lay the foundation for a future-ready, multilingual, and people-centric AI ecosystem for a Viksit Bharat.

References:

Sarvam AI

• https://www.sarvam.ai/blogs/sarvam-launch
• https://www.sarvam.ai/blogs/partnerships-with-indian-states/
• https://www.sarvam.ai/products/conversational-agents - from website, as of Feb 20

Ministry of Electronics and Information Technology

• https://www.pib.gov.in/PressReleasePage.aspx?PRID=2227612
• https://www.pib.gov.in/PressReleasePage.aspx?PRID=2112485&reg=3&lang=2

PIB Headquarters:

• https://www.pib.gov.in/PressNoteDetails.aspx?ModuleId=3&NoteId=157204&reg=3&lang=1

Click here to see pdf 

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PIB Research

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