Crowdin Kaynakları

Snov.io is a sales automation platform used by over 3 million users to find, verify, and engage with leads. Snov.io currently supports 9 languages on its website and 5 languages within its platform, with some high-traffic pages translated into as many as 14 languages. Behind this massive operation is Olena Holovach, Snov.io’s Localization Manager. Starting 6 years ago as a localization pioneer, Olena has transformed the company’s approach to localization into a highly structured, AI-driven workflow. <table> <thead> <tr> <th class="align-baseline">SUMMARY</th> <td></td> </tr> <tr> <th>Category</th> <th>Details</th> </tr> </thead> <tbody> <tr> <td> <strong>AI Translation</strong> </td> <td>GPT-4o + Custom AI prompts</td> </tr> <tr> <td> <strong>Languages</strong> </td> <td>9+ on the website and 5+ platform languages</td> </tr> <tr> <td> <strong>Crowdin Feature</strong> </td> <td> Managed balance – allows Snov.io to use AI providers without managing individual API keys </td> </tr> <tr> <td> <strong>Key Metric (ROI)</strong> </td> <td>SEO performance and translated word count</td> </tr> </tbody> </table>

For as long as most of us can remember, the localization industry has run on a single, stubborn unit of measurement: the words. You count them, you price them, you invoice them. Simple. But what happens when a large language model can draft thousands of those words in seconds, and a post-editor can process them by the hour? That’s the uncomfortable question at the heart of a recent conversation on The Agile Localization Podcast, where host Stefan Huyghe sat down with Diego Cresceri, Founder and CEO of Creative Words, to talk about what comes after the per-word era.

Most teams hit the same wall when expanding to new markets. The content strategy is ready. Translations are in progress. But every new locale requires engineering work to set up, deploy, and maintain. Add five markets, and you've added five times the coordination overhead. The problem isn't translation. It's the gap between translated content and live pages. This post covers how product and engineering teams can close that gap using Builder.io and Crowdin together, so marketing and content teams can manage localization across markets without creating a parallel engineering workstream for every region.

Every night, while you sleep, your brain does something remarkable. It replays the day to extract signal from noise and form long-term memory. Raw sensory data gets compressed into patterns. Patterns become rules. Rules become the intuition you wake up with. Neuroscientists call this memory consolidation. We call it dreams. Crowdin has been observing recent developments in AI coding tools and we figured out that the very same principle might work for Crowdin. Every time a human editor corrects an AI translation, something valuable is created: information about what kind of translations are acceptable for a particular project. Source text. Machine output. Human decision. Our industry almost always throws this away. We ran experiments, and it seems this is a great way to automate the improvement of expected translation quality.

Today more than ever, businesses must communicate across borders and languages to engage diverse audiences. As companies expand into new regions, the need for efficient and scalable language solutions becomes more critical than ever. However, achieving effective multilingual communication isn’t as simple as just translating content. It requires an integrated, strategic approach: Language Operations (LangOps). LangOps goes beyond mere translation – it is a comprehensive framework for managing and scaling a business’s multilingual content, ensuring quality and consistency across all global markets. This article will explore the components of LangOps, how to build a scalable language strategy, and how modern tools like translation management systems (TMS) can enable businesses to execute their global communication strategies more effectively.