Amir Kamran and Amir Soleimani from Taus unpack Quality Estimation (QE) as the missing link between machine translation at scale and human-quality outcomes. They frame QE as an automated “second opinion” that flags what can ship, what needs a quick AI touch-up, and what should go to a linguist—shifting human effort to the tricky, high-risk bits.

In this conversation, we explore where this matters most (high-volume content, real-time chat, regulated use cases), how QE reshapes workflows after MT, and why language access still demands human oversight in sensitive domains. They close with what’s next: lighter, task-focused models, more agent-style automation, and a broader definition of quality that favors fitness-for-purpose over perfection.