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AI-Assisted Localization

1 post · Aug 2026

What this trail covers

Atlassian is scaling its localization process to keep pace with AI-driven development acceleration. This involves leveraging AI to draft translations and implementing tooling to fix source code issues before translation. The goal is to reduce rework, lower localization costs, and maintain translation quality by combining AI drafting with professional human review. The system gathers context for AI prompts, tracks Translation Edit Rate (TER) to improve AI models, and uses i18n validation to catch

All Atlassian trails
  1. Scaling localization at Atlassian: keeping translation at the pace of AI-era developmentThis post details the expansion of Atlassian's AI-Assisted Localization capabilities by introducing a two-pronged approach: AI drafting of translations and tooling to fix source code issues before translation. The AI drafting process involves gathering context (developer notes, similar strings, glossary terms, style rules) to generate initial translations, which are then reviewed and approved by professional translators. The system tracks Translation Edit Rate (TER) to measure draft quality and provide feedback for model improvement, aiming for up to 50% cost savings. The internationalization (i18n) validation system catches and guides developers to fix source code problems like untranslated text, fragmented sentences, and missing plural rules. This includes providing richer context in string descriptions for both AI and human reviewers. Over 20,000 existing issues were cleared by AI-driven bulk fixes and code review guidance.