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Microsoft joins hands with Gen AI startup Sarvam AI for Indic voice LLM stack


Tech giant Microsoft and generative AI (Gen AI) startup Sarvam AI have collaborated to make the latter’s Indic voice large language model (LLM) available on Azure.

The partnership aims to enable the startup to leverage Azure AI and Azure Infrastructure to build and deploy their voice LLM stack.

Sarvam AI, which is building Gen AI models targeting Indic languages and contexts, aims to make the development and deployment of Gen AI apps in India more accurate and cost effective.

Sarvam AI’s Indic voice LLM will initially be available in Hindi. The startup is working to expand coverage to more Indian languages.

“At Microsoft, we are committed to enabling AI for everyone, empowering India’s transformation into an AI-first nation,” said Puneet Chandok, President of Microsoft India and South Asia.

“Through our collaboration with Sarvam AI, we are not just supporting homegrown innovation–we are fostering a future where every individual, regardless of their language or background, can benefit from the power of voice-driven AI solutions,” he added.

Sarvam AI will use the software giant’s cloud and AI infrastructure to train, host, and scale its LLMs quickly and efficiently. Through this partnership, the companies will also look at ways to better support Indian languages in Microsoft’s Generative AI frontier language models and to deploy them at scale. 

They also plan to collaborate to help enterprises adopt generative AI quickly and responsibly.

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“We are very excited to collaborate with Microsoft to make advanced AI technology accessible and impactful for everyone in India,” said Pratyush Kumar, Co-founder of Sarvam AI.

In December, Sarvam AI raised $41 million in Series A round led by Lightspeed Venture Partners and supported by Peak XV Partners and Khosla Ventures.

Co-founded by Kumar and Vivek Raghavan, Sarvam AI aims to develop the “full-stack” for Generative AI, which comprises research-led innovations in training custom AI models to enterprise-grade platforms for authoring and deployment.


Edited by Megha Reddy



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