Nemotron-Personas-India: Synthesized Data for Sovereign AI

India represents one of the world's largest AI opportunities — with over 700 million internet users, a multitude of languages, and a rapidly growing developer ecosystem. Yet, most open datasets reflect Western norms and English-only contexts, creating a data gap that limits AI adoption in India's multilingual, multi-script environment.

Today, we're releasing Nemotron-Personas-India , the first open synthetic dataset of Indic personas aligned to India's real-world demographic, geographic, and cultural distributions. Licensed under CC BY 4.0 , this dataset offers a privacy-preserving, regulation-ready foundation for scaling AI systems that reflect Indian society—without relying on sensitive personal data.

Built with NeMo Data Designer , NVIDIA's enterprise-grade synthetic data generation microservice, Nemotron-Personas-India extends our global collection of Sovereign AI datasets. It builds on the success of our US and Japan persona datasets and includes new features designed specifically for India's culturally rich landscape.

This dataset integrates seamlessly with Nemotron models and other open-source LLMs, making it easy to fine-tune AI systems for Indian use cases—from multilingual chatbots to culturally-grounded specialized copilots.

All personas are fully synthetic. While grounded in real-world distributions from the 2011 Census and Parsed Indian Electoral Rolls data, no data is tied to any living or deceased individual. This ensures developers can safely train AI systems without privacy risks or regulatory barriers.

This release complements our earlier suite of Hindi evaluation datasets — including ChatRAG-Hi , IFEval-Hi , MT-Bench-Hi , GSM8K-Hi , and BFCL-Hi — supporting a complete pipeline from synthetic data generation to rigorous model evaluation for Indian AI systems.

Produced using NeMo Data Designer , NVIDIA's microservice for synthetic data generation. This compound AI system enables generation with complex Jinja templating, Pydantic validation, structured outputs, automated retries, and supports multiple generation backends – the necessary tooling to scale a synthetic dataset of this size. We also leveraged the following models:

This dataset was aligned to India’s official demographic distributions from the 2011 Census and expanded to include attributes essential for trustworthy AI training: