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The future of translation is an orchestration
Get practical insights, expert tips, and emerging trends on AI-powered translation, workflow design, and language operations. Learn how to move from one-off translations to scalable systems that improve over time.
What is AI Translation and how it differs from Machine Translation
Most teams think they’re already using AI for translation, but most aren’t. This story explains the real difference between AI translation and machine translation, how modern systems learn from human feedback, and why it’s changing the future of global communication.
What is Machine Translation Post‑Editing (MTPE)
Machine Translation Post‑Editing (MTPE) is a hybrid translation method that combines automated translation with human editing. It’s ideal for high‑volume, time‑sensitive or budget‑conscious projects, delivering faster turnaround and lower costs while maintaining quality. This guide explains what MTPE is, the differences between light and full post‑editing, when to use them and best practices for success.
Best practices to create a terminology database
A terminology database is not just a glossary. It is a system that defines meaning, enforces consistency, and improves translation quality over time. This guide explores best practices for structuring terms, adding context, governing updates, and integrating terminology with translation memory and human review. Learn how platforms like TextUnited turn terminology into a scalable, self-improving system.
Language Quality Assurance (LQA) for AI translation
AI translation enables speed but not guaranteed quality. This article explains how LQA introduces structured evaluation, terminology control, and feedback loops to improve translation quality over time.
Language Quality Assurance (LQA) vs human review: Why you need both for scalable translation quality
Language Quality Assurance (LQA) and human review are often used together but serve different roles in translation workflows. This article explains how they differ, when to use each, and how combining both creates scalable, consistent, and high-quality multilingual content.