In this tutorial, we'll turn a synthetic persona into a deployed Korean agent — from filtering the dataset to inference — in about 20 minutes using hosted APIs.

Nemotron-Personas-Korea was generated using NeMo Data Designer, NVIDIA's open-source compound AI system for synthetic data. The pipeline pairs a Probabilistic Graphical Model (Apache-2.0) for statistical grounding with Gemma-4-31B for Korean-language narrative generation. Population data comes from KOSIS (2020–2026 releases); name distributions come from the Supreme Court of Korea.

Nemotron-Personas-Korea is the latest addition to the Nemotron-Personas Collection, which also covers the USA, Japan, India, Singapore (with AI Singapore), Brazil (with WideLabs), and France (with Pleias). If you're building a multilingual agent that serves Korean users alongside other markets, you can blend personas across countries in the same pipeline.

Most agents today are identity-blind. They follow instructions without any grounding in who they're serving. For example, an agent that books a Korean hospital appointment using US scheduling conventions, or addresses a 60-year-old patient in 반말 (“banmal,” informal language), doesn't just feel wrong. It fails.