AI-Driven Localization in Practice

Research, Analysis, and Perspectives

Translation memory alone is no longer enough. AI needs context to deliver consistent, brand-aligned translations that improve over time. This article explores why context-centric orchestration is reshaping the future of localization.

Invited to speak at ai-PULSE 2025, Europe’s premier AI conference, Translated CEO Marco Trombetti focuses on the architecture behind most modern language models and explains why they perform well in many scenarios, yet struggle when precision, accountability, and trust are non-negotiable

Learn how to integrate our translation AI, Lara, into your existing workflows and combine it with language professionals through TranslationOS. This guide outlines integration models, key features, and best practices for managing linguistic assets such as Translation Memories and glossaries to ensure consistent, high-quality translations.

With the integration of Lara via API, TranslationOS overcomes the limitations of traditional translation memories by enabling full document-level context. Localization managers can now design custom workflows that combine the most reliable AI and professional translation, applying the right level of human review based on the relevance and purpose of each content type.

Our translation AI, Lara, now runs on new hardware co-designed by Translated and Lenovo, a global leader in high-performance computing, specifically built for translation. This setup outperforms leading generic LLMs in the translation task in both quality and speed, unlocking new use cases in localization.

Our Tech Evangelist, Kirti Vashee, explores the critical role of data quality in AI, how quality data drives AI accuracy, reliability, and fairness, and why localization professionals must adopt a data curation strategy to optimize AI-driven workflows.