Blog
Deep dives on AI-generated codebases, localization architecture, and how to keep your product multilingual without rewrites.
We audited our own five-locale marketing JSON. Cyrillic in German, formal Sie and vous on a dev tool, invented euros, encoding false friends, and shipping verbs that meant parcels. Then we wrote policy and a verify script so it regresses less quietly.
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Install agent skills, run five prompts, and go from hardcoded English to synced locales and a language switcher—without weeks of manual i18n work.
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English reaches a fraction of the world's online users, yet half the web stays English-only. Here's the conversion data, the ROI case, and why AI-built apps make 'later' more expensive than ever.
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Map the full stack in three layers — i18n architecture, translation management, and execution engines — so you know which tool to reach for first when your app is still mostly hardcoded English.
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Lokalise and Crowdin shine at translation workflows — once your app already speaks in keys and locale files. Most AI-built products skip that step. Here is how to think about tooling when your codebase is still the wild west.
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You do not need to throw away an AI-built codebase to support new languages. Start with i18n architecture, extract strings into keys and locale files, then layer glossaries and automated translation with real QA.
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Localization didn't start with AI, It didn't even start with TMS's, It started decades ago with tools built for translators, not product teams. And that legacy still shapes how the industry operates today.
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Traditional localization tools assume your app is already internationalized. Most modern AI-generated apps are not.
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