Localization Tips & Product UpdatesPage 8

In August, we delivered 365 updates and completed 523 tasks. We've presented audio generation and localization with the Crowdin Dubbing Studio. We also continue to enhance our AI tools. Our Agentic AI is now smarter, with new capabilities for editing source texts, adding and managing comments, while the Context Harvester now gathers context with much more accuracy. This release features new integrations with leading AI models and editor updates, including a new UI for localizing non-text file formats (e.g., images and videos), a new way to switch between files, and more.

Speed and accuracy in localization can mean the difference between delighting a user and losing them entirely. Agile localization has transformed how language professionals work. But while much of the focus has been on enterprise tools and large-scale vendor operations, freelance translators are carving out their own space, leveraging technology to deliver high-quality results while building lasting client relationships. On a recent episode of The Agile Localization Podcast, host Stefan Huyghe sat down with Simona Kisser, a technical translator and software localization specialist, to talk about the evolving role of freelance linguists. From the importance of context to the art of balancing speed and quality, Simona shared practical insights from over a decade in the field. Listen to the new episode on: - Apple Podcasts - Spotify - YouTube

Model Context Protocol (MCP) is a technology that allows you to connect large language models (LLMs) to external, dynamic data sources and tools to provide more contextual information. Unlike Retrieve Augmented Generation (RAG), which accesses static documents, MCP connects to live resources that change constantly, such as code repositories or content management systems. This dynamic access helps LLMs provide more accurate and context-rich outputs. MCP usage can reduce "hallucinations" in which the AI generates incorrect information.

What if there was a way to make AI in localization not just faster and more cost-effective, but also smarter? Enter the Agentic AI era, which transforms how we approach translation and localization. <Aside type="caution"> We’re saying goodbye to Agentic AI and upgrading everything to Crowdin Copilot. Learn more about Crowdin Copilot features. </Aside> In this article, we'll explore: - What Agentic AI is. - How it differs from earlier AI versions. - How Agentic AI works. - Its impact on the localization industry. - Crowdin's approach to Agentic AI (features, examples, and how it helps translators).

Expanding your digital business across borders might sound complex, but it's more possible than ever. Despite this, fewer than 1% of U.S. companies currently sell overseas. That leaves a massive, largely untapped opportunity for digital-first businesses willing to take the leap. !Import and export But going global isn’t just about being online. It’s about connecting with real people, in different countries, on their own terms. That means understanding how they browse, buy, and engage (and shaping your business around that). Translation alone won’t cut it. You need experiences that feel truly local. Let’s break down what it takes to build a sustainable, scalable global expansion strategy in today’s digital economy.

The world is overflowing with content, and much of it needs translation. The global machine translation market reflects this trend, growing rapidly and projected to reach around $4.8 billion by 2032. Localization managers always struggle to balance translation speed, quality, and cost. That’s why picking the right MT software is important for creating efficient and affordable translation workflows. This guide compares the 5 MT engines, focusing on quality, speed, and customization, to help localization managers make informed decisions. - Machine translation software is a computer program that automatically translates text or speech, primarily using Neural Machine Translation (NMT) for more natural results. - Top MT Engines: DeepL Translator, Google Translate, Microsoft Translator, Amazon Translate, ModernMT. - Choosing the Right Tool: Consider language support, cost-effectiveness, customization needs, and integration capabilities with existing systems like a Translation Management System (TMS). - Centralize MT Management: Crowdin acts as a centralized platform that allows localization teams to connect, manage, and use various MT engines from a single interface.

If AI can translate entire websites in seconds, where does that leave human translators? According to Marco Trombetti, Co-founder and CEO of Translated, we're just getting started. In this episode of The Agile Localization Podcast, hosted by Stefan Huyghe and powered by Crowdin, Marco breaks down why the future of translation isn't a battle between man and machine, but a collaboration where the best of both worlds shines. From adaptive MT to specialized models like Lara, Marco shares how AI and humans can elevate each other, especially in a multilingual, multicultural world that demands both speed and nuance. Listen to the new episode on: - Apple Podcasts - Spotify - YouTube