AI for a Billion Voices: Taking AI where there is Maximum Impact
summary
Krutrim AI launches a foundational model trained on 2 trillion Indic tokens plus English, rolled out six months ago and used by millions via an app and cloud APIs; a bespoke custom tokenizer cuts token-to-word ratios that break other models, improving cost and accuracy for dozens of Indian languages; multimodal voice and vision integration is underway; evaluations show lower hallucination rates and better translation/QA than fine‑tuned Llama variants and competitive recall against web‑connected search models; a full stack (chips, AI cloud, APIs) supports scaling to grassroots users.
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chapters (8)
- 0:00Introduction to AI for billions
- 1:12Challenges of multilingual AI
- 3:37Why fine-tuning fails for India
- 6:05Building India’s foundational model
- 7:50Problems with existing tokenizers
- 9:35Ensuring factual accuracy in AI
- 12:25Building India’s full AI stack
- 13:50Opportunities for developers
