The Swiss branch of the World Wide Fund for Nature (WWF) has partnered with AI translation provider DeepL to streamline communication across its multilingual operations.
The collaboration highlights how large language models (LLMs) are supporting countries like Switzerland, where four national languages—German, French, Italian, and Romansh—make effective communication a complex yet crucial challenge.
The partnership will see translation technology deployed across various WWF Switzerland teams, enabling more fluid internal communication and enhanced outreach to diverse linguistic communities.
“DeepL’s technology is a game-changer for us, enabling our teams to communicate more effectively,” said Pierette Rey, deputy head of corporate communications at WWF Switzerland. “This is particularly important in a multilingual country such as Switzerland and in view of our international activities.”
Externally, the translation tech firm will assist WWF’s communications team with translating media materials, including its flagship magazine used to raise public awareness and drive fundraising.
Internally, DeepL’s Microsoft add-in will help staff translate emails, reports, and presentations, streamlining daily workflows. The organisation’s Environmental Department will also use the tool to facilitate international research collaboration.
Wildlife trust uses IoT and 5G to protect African birds
Jarek Kutylowski, DeepL CEO and founder, said: “Our technology will empower their teams to engage and collaborate across languages, driving positive change for the environment, regardless of where their teams are based and who they’re engaging with.”
DeepL’s platform – which claims over 200,000 business and government users globally, offers ‘human-like translation in both written and spoken formats.
The partnership follows a similar deployment of LLMs by the European Union’s Directorate-General for Translation, which recently began using AI-powered translation tools to assist in drafting multilingual policy documents, preserving linguistic nuance across all 24 official EU languages.
This project leveraged the Euramis database, a repository of over 100 billion tokens from professionally translated EU documents, to train the LLM.
