Key Insights:

• Google and Microsoft launched AI assistants in multiple Indian languages, targeting India’s diverse linguistic landscape.
• Local start-ups like Sarvam AI are innovating generative AI tools for Indian businesses, securing significant investments.
• India is a test bed for AI technologies, offering global expansion opportunities for customized AI solutions.

Global technology firms, alongside local start-ups, are focusing on adapting artificial intelligence platforms to cater to India’s diverse linguistic landscape. With a population of 1.4 billion and thousands of languages and dialects, the AI industry sees vast potential in creating tools that function across various Indian languages. Key players in this endeavor include Microsoft, Google, and local start-ups such as Sarvam AI and Krutrim.

Microsoft and Google are leading the charge, with Google recently launching its Gemini AI assistant in nine Indian languages. Microsoft’s Copilot AI assistant supports 12 Indian languages, and the company is further developing smaller, cost-effective language models at its Bengaluru research center. These models are designed to run on smartphones, making them more suitable for regions with limited connectivity.

Local Start-ups and Investments

Local start-ups are also making strides in this sector. Sarvam AI, founded in Bengaluru last year, is developing a comprehensive suite of generative AI tools tailored for Indian businesses. The start-up has secured $41 million in funding from prominent investors, including Peak XV, Lightspeed Venture Partners, and Sequoia’s former India arm.

Lightspeed partner Hemant Mohapatra emphasized the growing importance of local AI companies in response to governments’ increasing focus on developing “sovereign AI.” He noted that training AI models on local data necessitates the involvement of Indian companies focused on domestic use cases. “If you’re training a foundation model in India on Indian citizen data, audio, video, text, different languages, then it has to be an Indian company,” Mohapatra explained.

Adapting Existing Models for Local Use

Rather than creating large language models (LLMs) from scratch, Indian companies are concentrating on adapting existing models for local languages and preferences. Given the substantial resources required to develop LLMs, this approach is more feasible. Sarvam AI, for example, is leveraging voice data instead of text to cater to the communication habits of many Indians who prefer audio messages.

Lightspeed partner Bejul Somaia pointed out the challenges in bridging the gap between existing models and real-world applications in India. He emphasized the need for an ecosystem to support the adaptation of underlying model capabilities for the Indian market. “In a market like India, you’re going to need to have a little bit of an ecosystem that springs up to enable companies to use the underlying model capabilities,” Somaia stated.

Testing Ground for Future Expansion

India’s diverse and expansive market also serves as an effective testing ground for new AI technologies. Tanuja Ganu, a manager at Microsoft Research in Bengaluru, noted that successfully validating AI tools in India could facilitate their expansion to other regions. “It’s using India as a test bed, validating some of the technology in India, and seeing how we can expand it to other parts of the world,” she said.

This strategy benefits India and provides valuable insights for deploying AI solutions globally. By addressing the unique challenges of the Indian market, companies can develop robust AI tools that can be adapted for use in other countries with similar needs.

The race to adapt chatbots and AI voice assistants for India’s many languages is poised to open lucrative new markets. The collaboration between global tech giants and local start-ups highlights this sector’s potential for innovation and growth. 

Microsoft’s Puneet Chandok emphasized making AI accessible and relevant for Indian users. “Make [AI] simple and easy to use and get it to the hands of all these customers and partners,” he stated, adding that contextualizing AI for the Indian context is crucial for its success.

Editorial credit: Shivashankara / Shutterstock.com

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