Case Study: TrainAI achieves scalable, culturally authentic multilingual chat data with RWS

TrainAI delivers 1,075 multilingual conversations with RWS and cuts costs by 40%

TrainAI, a global AI technology organization, faced the challenge of building a large-scale, multilingual chat dataset that authentically reflected how people message in India. They needed to generate realistic conversations with natural code-mixing between English and Hindi, but traditional manual data collection methods were costly and risked inauthenticity. They engaged RWS and its TrainAI service to find a solution.

RWS implemented a hybrid solution combining AI-powered synthetic data generation with human linguistic and cultural expertise. This approach delivered 1,075 compliant multilingual conversations, achieving a 40% cost saving compared to traditional methods. The result was a project-ready, culturally authentic dataset that exceeded client expectations and created a reusable pipeline for future projects.