Translation Quality at Scale: Platform vs Intelligence

Published 2026-02-21
Tech stack: Crowdin · Figma · Linear · ICU MessageFormat · GitHub Actions
localizationi18ntranslation qa
CrowdinFigmaLinearICU MessageFormatGitHub Actions

Most localization stacks optimize for either throughput or quality assurance. Durable systems require both.

A platform layer provides coverage across keys, locales, and environments. An intelligence layer enforces correctness through glossary controls, context disambiguation, UI constraints, and locale conventions.

The implementation pattern is practical: enrich keys with context metadata, run QA checks before publish, and expose context directly in translator workflows.

The goal is not perfect translation. The goal is fewer high-impact user-facing errors and faster review cycles with clearer quality gates.