Fonctionnalités de Crowdin

Last year, we introduced Agentic AI to the localization world. It was a milestone designed with a singular focus: mastering linguistic tasks and assisting linguists within the Editor. But the vision was always bigger. Now, we are excited to introduce Crowdin Copilot – a new generation of AI that doesn't just translate your strings but helps you manage your entire localization ecosystem. By bridging the gap between LLMs and the Crowdin API, Crowdin Copilot can execute complex workflows that previously required manual labor, custom scripts, or engineering support. It doesn't just tell you information about your projects, it can act on them.

Turo is the car rental marketplace that is reinventing rental to unlock independence for all. But unlike booking a room, car sharing requires a unique layer of confidence. Guests aren't just looking for a place to stay; they are trusting a vehicle to move them safely through the world. This focus on reliability has fueled Turo's massive growth into a $1 billion business. Today, the company successfully operates across the US, Canada, the UK, France, and Australia. For Turo, localization is the "bare minimum" required to build this essential trust with both hosts and guests. Leading this global expansion is Gideon Hod, Turo's Director of Product Operations. Gideon views his role as the center of a massive feedback loop, bridging the gap between high-level product strategy and the boots-on-the-ground reality of local users. While traditional product management often focuses on top-down roadmaps, Gideon’s approach to Product Ops is built on velocity and execution. Under his direction, Turo sped up their workflow shifting from a manual localization process to a high-speed, AI workflow within Crowdin. <table> <thead> <tr> <th class="align-baseline">SUMMARY</th> <td></td> </tr> <tr> <th>Metric</th> <th>Traditional Workflow</th> <th>AI Workflow</th> <th>Improvement</th> </tr> </thead> <tbody> <tr> <td> <strong>Time to Market</strong> </td> <td>3–4 Months</td> <td>1 Week</td> <td> <strong>90% faster</strong> </td> </tr> <tr> <td> <strong>Total Cost</strong> </td> <td>$10,000+ (Est.)</td> <td>AI Tokens + QA</td> <td> <strong>98% cheaper</strong> </td> </tr> <tr> <td> <strong>Human Intervention</strong> </td> <td>100% Manual Review</td> <td>Near-zero (Fallback only)</td> <td> <strong>Almost 100% AI Translations</strong> </td> </tr> </tbody> </table>

There’s a running joke in the translation industry: two linguists can’t agree on whether a translation is correct. At Crowdin, we don’t think the problem is the linguists – it’s that different humans (and AIs) have different quality standards by default. If requirements are communicated clearly enough, two people and an AI should arrive at the same conclusion. We also see this reflected in the data: the same LLM model, on very similar content, produces wildly different post-edit distances across different clients. This isn't because the model is inconsistent, but because different clients have different expectations. Some require good-enough compliance, while others require strict adherence to specific national spelling rules. Without a shared standard, every variant becomes a potential critical error. While our AI Pipeline and Copilot can produce high-quality, coherent translations within a single file, the challenge has always been: How do we guarantee consistency across thousands of files? The answer is Style guides.

Translation management systems used to be the center of everything in localization. If you wanted to translate, review, manage, or ship content, you went through your TMS. However, in The Agile Localization Podcast, Jourik Ciesielski, CTO at ELAN Languages, makes it clear that this model is starting to break. AI hasn’t just added new capabilities. It’s exposed deeper limitations in how TMS platforms are designed and how teams think about them.

Based on original research combining a B2B survey of localization, engineering, product, and security professionals, Reddit community discussions, and publicly available industry data. The question is no longer "whether" but "how". AI translation is no longer an experiment. It has become an operational baseline for enterprise teams managing multilingual content at scale. Based on our original survey of 152 B2B professionals across the US and Canada, roughly 95% of respondents already use AI or machine translation in some capacity – with nearly half doing so frequently and about 18% using it for every translation task. Nearly 9 in 10 require or prefer bring-your-own API keys. Over 91% already have governance frameworks in place or underway. And 1 in 5 report quality incidents since introducing AI translation. But adoption alone tells only part of the story. What matters now is how companies implement AI translation when the stakes include data security, regulatory compliance, brand consistency, and production reliability. The central finding of this research is clear: enterprise teams do not treat AI translation as a plug-and-play capability. They treat it as a managed process – one that demands governance, quality controls, cross-functional oversight, and platform-level orchestration. In other words, the industry has moved past the debate over whether AI translation works. The real conversation is about how to make it work safely, predictably, and at scale.