To ensure accurate and safe generative outputs from its healthcare chatbot, a leading U.S. tech company partnered with iMerit to evaluate and refine a new medical dataset using Retrieval-Augmented Generation (RAG). Leveraging a team of nurses and a board-certified physician, iMerit developed a scalable, expert-driven workflow to audit medical definitions for accuracy, safety, and usefulness—achieving 99% consensus and over 72% in project cost savings.

This case study highlights how iMerit's domain-specific RLHF and human-in-the-loop evaluation helped the client reduce risk, improve quality, and scale responsibly in a high-stakes healthcare environment.