Jarvi is a recruitment platform serving 300+ agencies across France and Europe. Fast product development is core to how the team competes – but fast development creates a localization problem: content goes out of sync before the translation cycle catches up.

Two problems, one root cause#

Jarvi's localization challenges were really one problem in two forms.

The first: translation quality. Jarvi had established human-translated baselines for their French and European markets. Before switching to automated localization, they ran a head-to-head test: Lingo.dev's localization engine against their existing human-translated content. The result surprised the team.

"The results surprised us – Lingo.dev's translations were actually more accurate than our human translations," says Quentin Decré, Co-founder.

The reason is structural. Human translators work from the source text without product context. A localization engine configured with Jarvi's recruitment domain vocabulary – the specific terms for "applicant tracking," "placement," "sourcing pipeline" – applies that context to every request. The localization engine knows the product. Most individual translators don't.

The second: sync. "What kills you isn't just the translation time – it's remembering to translate everything as you ship features," explains Decré. "Our content was constantly out of sync, which hurt our expansion to new markets."

Every new feature meant a new translation task. In a team moving fast, those tasks accumulated. Untranslated strings meant French agencies saw gaps. European expansion required content that tracked the product in real time.

Localization connected to the development workflow#

Jarvi configured a localization engine with their recruitment domain terminology and connected it to their GitHub Actions workflow. Now, when a pull request merges, the CI/CD pipeline triggers the localization engine. Translated strings are committed back to the repository automatically.

The development culture change was more significant than the technical change.

"We used to constantly worry about keeping translations in sync. Now we just build features, and localization happens automatically. For a product that needs to move fast and serve recruiters across multiple countries, this was exactly what we needed."

Results#

• Zero developer time on translation management
• Faster feature shipping – localization no longer a gating step
• AI translations that outperformed prior human-translated baselines in accuracy
• Consistent terminology across all languages via the localization engine's glossary
• GitHub Actions integration: every PR includes localized strings

One downstream effect: with localization automated, the team started investing more in markdown content for documentation and SEO. Content that previously would have required manual translation overhead now gets localized automatically on every push.

Jarvi continues to expand across Europe, exploring additional localization engine features including screenshot-based translation for visual content.