Yes. The biggest change over the last couple of years is that localization has shifted from "translation after development" to continuous localization integrated into CI/CD. The pipelines that tend to scale well combine:
- A Translation Management System (TMS) to manage strings and translators.
- Machine translation for the first draft.
- AI-assisted review for terminology and tone.
- Human review for customer-facing content.
- Automation that syncs changes directly from your code repository.
A practical architecture looks like this:
Git
↓
Extract strings (i18next, ICU, gettext, etc.)
↓
TMS (source of truth)
↓
MT + Translation Memory + Glossary
↓
AI QA (tone, terminology, placeholders)
↓
Human review (high-value languages/content)
↓
Automatic PR or deployment
Translation management tools
These are the platforms I see used most often:
| Tool | Best for | Notes |
|---|
| Lokalise | SaaS products | Excellent integrations, developer-friendly |
| Crowdin | Apps & open source | Strong GitHub/GitLab workflows |
| Phrase | Enterprise | Mature workflow automation |
| Transifex | Larger organizations | Good API and collaboration features |
| Weblate | Self-hosting | Great open-source alternative |
AI that actually helps
Rather than replacing translators, AI works best for:
- generating first-pass translations
- preserving brand voice
- checking terminology consistency
- catching placeholder errors (
{name}, %s, HTML tags)
- flagging untranslated strings
- suggesting gender/plural forms
Many teams now use GPT-4.x/5-class models alongside translation memories instead of relying solely on Google Translate or DeepL.
Translation memory is a huge win
If your product contains recurring phrases like:
- "Sign in"
- "Reset password"
- "View invoice"
you shouldn't pay—or wait—to translate them repeatedly.
Translation Memory (TM):
- reduces cost
- keeps wording consistent
- speeds up new releases dramatically
Organize strings well
Avoid:
"button1": "Submit"
Prefer:
"checkout.submit_order": "Submit order"
"profile.delete_account": "Delete account"
Context-rich keys make translations much more accurate.
Use ICU formatting
Instead of:
"You have " + count + " messages"
Use ICU MessageFormat:
You have {count, plural,
one {# message}
other {# messages}
}
This handles plural rules correctly across dozens of languages.
Automate everything
A common GitHub workflow:
- Developer merges code.
- CI extracts new strings.
- Strings are pushed automatically to the TMS.
- Machine translation pre-fills new entries.
- Reviewers approve only changed content.
- Completed translations open a pull request automatically.
- Deployment includes updated locales.
This removes most manual copying and pasting.
Keep linguists informed
Translation quality improves significantly when translators can see:
- screenshots
- where a string appears
- character limits
- product descriptions
- glossary definitions
Context often matters more than the translation engine.
My recommended stack
For a modern SaaS product:
- Frontend: i18next or FormatJS (ICU)
- Backend: Locale-aware formatting libraries
- TMS: Lokalise or Crowdin
- Machine translation: DeepL or an LLM-based workflow
- Version control: GitHub integration
- QA: Automated placeholder, glossary, and consistency checks
- Human review: Only for customer-facing or marketing content
This approach scales well from a handful of languages to dozens while keeping engineering overhead manageable.
If you're building a web app, mobile app, or documentation site, I can suggest a pipeline tailored to that specific stack (for example, React + i18next, Flutter + ARB files, or Next.js with App Router).
