The MedTech industry operates in one of the world’s most demanding content environments. Companies manage large volumes of highly regulated content across IFUs, labels, technical documentation, clinical materials, training content, and digital channels. As product portfolios grow and more markets and languages are added, traditional document-based workflows become increasingly difficult to control.
Janus Worldwide’s Linguistic Quality Assurance (LQA) Program combines a structured evaluation framework with a trigger-driven strategy to ensure localized content consistently meets industry standards. At its core, the program defines LQA Triggers — specific events or conditions that prompt targeted quality checks for human and AI-generated output. Triggers are configurable and tailored to each client’s risk profile, language pairs, subject areas, and operational cadence.
LQA is a structured and objective evaluation method that ensures localized content meets high standards of accuracy, fluency, terminology, style, and cultural relevance. It systematically identifies, categorizes, and scores errors to guarantee consistency and suitability for the intended purpose. It is especially crucial for high-risk, regulated sectors such as Healthcare, Pharmaceuticals, Civil Engineering, Finance, Law, Mobility, Logistics, Automotive, and virtually any industry where precision and compliance are essential.
The QRD Template is more than a formatting framework. It defines how SmPC content is structured and aligned across multilingual regulatory submissions. Upcoming QRD updates provide a valuable opportunity to review translation workflows and identify areas where consistency, compliance, and process control can be strengthened.
Under MDR, translation is not just a linguistic task. It is part of controlled documentation. Texts such as labels, IFUs, packaging text, and other product information cannot be treated as “just translations”.
The Janus AI Ecosystem now supports the uploading of translation memories (TMs) and glossaries for use in the AI-powered translation process. Thanks to this new functionality, the system analyzes the text in the original language and compares it with data from the TM and glossary. In addition, all grammatical forms of words (declension, conjugation, cases) are taken into account. Therefore, if a glossary term appears in the text in any form, the system will see it. As a result, only those terms directly present in the text and those translation memory segments most similar to the sentences in the text are added to the prompt. This reduces the amount of data transferred, speeds up the translation, and makes it more accurate.
Video has long become a universal language of business communication: presentations, webinars, promotional videos, and training courses, and they all require high-quality adaptation for a global audience. But how can you ensure your content sounds just as good in every language without having to choose between quality and speed?
Strategy does not guarantee business success. But the absence of strategy almost certainly guarantees failure.
In our industry, we often talk about translation quality as the key success factor for any project. But I can say with certainty that the final result doesn’t begin when the translator opens a file – it starts much earlier, at the stage of technical file preparation for translation.
2025 was the first year when the language services industry truly felt the cold breath of AI. This impact was further intensified by today’s geopolitical reality, especially the slowdown in global trade caused by tariffs and sanctions.