A Structural Accessibility Gap
India has 22 officially recognised languages and over 19,500 dialects. Its digital economy, however, has been built almost entirely in English — creating a structural accessibility gap that affects hundreds of millions of people who transact, work, and communicate in their mother tongue. That gap is now closing fast, and the implications for enterprise AI are significant.
The Models Closing the Gap
In 2025, Sarvam AI released IndicVoice — a speech recognition model trained on over 5,000 hours of audio data across 10 Indian languages. Krutrim, Ola's AI venture, launched a multilingual LLM that outperforms GPT-4 on Hindi, Bengali, and Tamil benchmarks. The government's Bhashini platform now enables real-time translation across 22 languages and has been integrated into several state-level citizen service portals.
Commercially Significant Use Cases
The enterprise use cases are immediate and commercially significant.
- •Insurance companies deploying AI voice agents in Odia, Marathi, and Kannada to reach rural policyholders
- •Microfinance institutions using vernacular chatbots for loan repayment reminders with higher response rates
- •E-commerce platforms seeing measurable conversion lifts when discovery and support switch to the user's preferred language
A Fundamental Capability, Not a Feature
The global context reinforces the urgency. Meta's SeamlessM4T model supports 100+ languages in speech-to-speech translation. Google Translate now covers over 240 languages.
Key Takeaway
Language is not a localisation feature. For Indian enterprises with mass-market ambitions, it is a fundamental capability.
By Grey Platforms
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