globalize.now translates with large language models and reviews every translation with a second AI pass. Four tools shape the result, and all of them live in your project.
Glossary
Project → Glossary. Add a source term, its required translation, and the languages it applies to. Glossary terms are protected during translation, so product names, feature names and legal wording stay exactly as you decided. You can import a glossary in bulk instead of typing entries.
Style guides
Project → Style Guides. One per target language: tone, formality (for example du vs Sie, tu vs usted), terminology preferences, what to leave in English. If a language has none yet, the dashboard offers to draft one from your own source content in one click; edit it and save.
Translation memory
Project → Translation Memory. Every approved translation is stored. When the same source string appears again — another push, another component, another project file — the stored translation is reused and not billed. Translations you edit by hand and push back are treated as approved and win over ours.
QA review
Every job runs a quality-review pass on each target language after translation. It checks the translation against the source for meaning, placeholders and variables, glossary compliance and style-guide fit, and flags anything that doesn't pass. Each language's results are summarised on the project dashboard in three buckets — good, passable, and needs attention — and you can turn on a QA issues notification (webhook or email) in project settings.
Editing translations after they're generated
Translations arrive in a pull request that changes only your locale files. Edit them there like any other code change, or later in your repository. To make your edits authoritative, set Existing translations → Every string, on every push in project settings: your files win, and the translation memory is updated from them.
Which models
Translation and review currently run on Anthropic's Claude models. We choose the model for quality and cost; per-job model selection isn't available today. Text is sent to the model provider for translation only and is not used to train models.
How good is it?
Better than phrase-based machine translation because the model sees the whole string, its placeholders and your glossary and style guide at once; cheaper and faster than a human translator; not a replacement for a native review where the stakes demand one. The QA pass exists to make the cases that need a human visible rather than silent.