'Bharat Gen', Indigenous AI-Based Model For Indian Languages, Launched

New Delhi:
Science and Technology Minister Jitendra Singh launched 'Bharat Gen', an indigenously developed artificial intelligence-based multimodal Large Language Model (LLM) for Indian languages, here on Monday.
Developed under the National Mission on Interdisciplinary Cyber-Physical Systems (NM-ICPS) and implemented through TIH Foundation for IoT (Internet of Things) and IoE (Internet of Everything) at IIT Bombay, Bharat Gen aims to revolutionize AI development across India's linguistic and cultural spectrum, Mr Singh said.
The initiative is supported by the Department of Science and Technology (DST) and brings together a consortium of leading academic institutions, experts, and innovators.
"Launched India's first-of-its-kind, indigenously developed, Artificial Intelligence (AI) driven , government-funded Multimodal "Large Language Model" (LLM) for Indian languages.
"BharatGen" is not a mere technology venture but indeed a national mission to create AI that is… pic.twitter.com/KlL3iZOfXU
— Dr Jitendra Singh (@DrJitendraSingh) June 2, 2025
Mr Singh described Bharat Gen as a "national mission to create AI that is ethical, inclusive, multilingual, and deeply rooted in Indian values and ethos".
The platform integrates text, speech, and image modalities, offering seamless AI solutions in 22 Indian languages.
"This initiative will empower critical sectors such as healthcare, education, agriculture, and governance, delivering region-specific AI solutions that understand and serve every Indian," Mr Singh said.
The minister recounted a success story from his own constituency Udhampur where an AI doctor communicates fluently in the patient's native language.
"It not only builds trust but has a placebo-like psychological effect, enabling better care in remote regions connected with superspeciality hospitals across India," he said.
Mr Singh emphasised the transformative role of Generative AI in grassroots governance, citing the integration of multilingual feedback systems into platforms like CPGRAMS to enhance citizen engagement and grievance redressal.

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