The Data Layer for Indian AI
Building data infrastructure that makes AI actually work for India—not translated, but natively understood.
The Indic Token Wall
High-quality training data for Indian languages is scarce. Most models are trained on translated content, web scrapes, or synthetic data—none of which captures the cultural depth and linguistic nuance of how Indians actually communicate.
The result? Models that produce linguistically passable but culturally hollow outputs. They can generate grammatically correct Hindi or Kannada, but they miss the context, the code-switching, the institutional knowledge, and the regional variations that define real Indian communication.
Worse, the synthetic data being generated to fill this gap comes from these same misaligned models. Every generation compounds the problem—cultural context erodes, institutional knowledge gets lost, and the gap between AI capability and real-world utility widens.
