Translation and Localization Tools

Most localization workflows are broken. Why are we still managing localization like it’s 2009? Developers get buried in translation files. Translators get screenshots in Slack. PMs send Excel sheets and hope for the best. The problem is that most i18n setups were never designed for fast-moving teams. I don't know about you, but I don't want another spreadsheet called “FINAL_FINAL_TRANSLATIONS_v3.xlsx”. It felt like shipping code with one hand tied behind our backs. I came across Tolgee, an open-source tool that completely changes how localization works. Instead of writing JSON files by hand or juggling outdated strings, you can translate the text right there, by clicking on it in the app or in the browser. You can check the repo here 👉 https://tolg.ee/anvzeh Some things I found compelling: • In-context translation directly in the UI • Chrome + Figma integrations for non-dev contributors • SDKs for React, Vue, Angular, Svelte, and mobile • Machine translation + translation memory baked in • Works locally, in the cloud, or self-hosted • Tolgee AI Translator: gives better translations by using screenshots and real context. It’s the first time I’ve seen localization feel like a real-time, collaborative part of the dev loop, not a separate phase that slows everything down. Curious, how do you handle localization today?

A localization lead I work with had a recurring headache: PowerPoint decks would come back translated, but every diagram, screenshot and chart was still in English. The Microsoft Build update to Azure Translator in Foundry Tools tackles exactly that kind of gap — translating more of what enterprises actually produce, not just the easy text. A few things from this announcement that stood out to me: Image translation is now GA in both synchronous and batch modes. Send an image, get a translated image back with layout preserved — powered by Azure AI Vision plus Translator. Batch PDF translation now uses Azure AI Document Intelligence, so multi-column layouts, tables and footnotes survive the round trip instead of collapsing into approximations. XML DITA and XLIFF 2.0 are now natively supported in both sync and async. Semantic markup and translation units stay intact, so output drops back into your CMS or CAT tool without cleanup. For Word and PowerPoint, setting translateTextWithinImage to true means embedded images get translated alongside the document text. Fully localized decks, not half-translated ones. All on the same Document Translation endpoints — no new SDKs to learn. Which of these would unblock your localization pipeline first? #MicrosoftFoundry #AzureAI #Localization #DocumentTranslation

❗ Another tool for Localization Engineering! A while ago, I published an article on using batch scripting for automating tasks in localization engineering with the Okapi Framework (https://lnkd.in/dzx2-JY3) , such as prepping files for translation and quality control. Today, I’m excited to introduce you to an amazing set of tools from Maxprograms: OpenXLIFF Filters (https://lnkd.in/dbzkAqAw). OpenXLIFF Filters is an open-source and free set of Java filters for creating, merging, and validating XLIFF 1.2 and 2.0 files. Some of its standout features include: ➡ Creating XLIFF files without proprietary markup. ➡ Merging translated XLIFF files to generate translated documents. ➡ Validating XLIFF files. ➡ Generating statistics with word and segment counts, plus a graphical display of match distribution. ➡ Combining multiple XLIFF files into a larger one. ➡ Pseudo-translating XLIFF files to test conversion/merge processing. ➡ Copying the content of <source> elements to new <target> elements for all untranslated segments. ➡ Approving all segments that contain translations. ➡ Removing all <target> elements from an XLIFF file. ➡ Exporting approved segments as TMX. These tools come in the format of batch files, making their integration into larger and more complex scripts super easy and convenient for enhancing automation. My favorites are: ➡ xliffchecker.bat: Checks the validity of XLIFF files and points out any issues that need attention. ➡ analysis.bat: Generates reports from our files. ➡ pseudotranslate.bat: Performs quick and clean pseudo-translations. ➡ exporttmx.bat: Creates TMX files from approved segments in XLIFF files, ready for use in our CAT tool. If you’re into localization engineering, these tools are a game-changer! 💡

Choosing a TMS in 2026 is no longer about features. It’s about how well your stack integrates with AI, automation, and real-time localization workflows. I broke down the latest Q1 2026 updates across the leading Translation Management Systems 👇 Here’s what’s actually shaping the market: ➡️ Smartling is doubling down on RAG + hallucination detection + developer tooling ➡️ Crowdin is pushing into AI-native workflows (Vibe Coding, AI dubbing, agent training) ➡️ Phrase is building the bridge between AI agents and TMS (MCP servers, BYOE, AI QA profiles) ➡️ Lokalise is focusing on AI orchestration + contextual translation at scale ➡️ Bureau Works (now wrks) is redefining UX with confidence scoring + meaning-based QA 💡 The shift is clear: We’re moving from 👉 “translation tools” to 👉 AI-powered localization operating systems If you’re evaluating a TMS, ask yourself: • Does it support AI translation workflows (MTPE, MTLE, GenAI)? • Can it integrate with LLMs like GPT or Gemini via RAG? • Does it give you quality control over AI output? • Can your dev team plug into it easily? Because in 2026, 👉 the best TMS is the one that orchestrates AI, not replaces humans. I go deeper into all of this in the full breakdown 🎥 👉 Which TMS are you currently using — and why? #Localization #TranslationManagementSystem #TMS #AIinLocalization #MachineTranslation #GenAI #RAG #Crowdin #Smartling #Phrase #Lokalise #BureauWorks #Globalization #SaaS #ContentLocalization