How a global community platform made localization invisible and scaled into new markets

The client is a global community platform that lets creators, entrepreneurs, and brands build and monetize digital communities across web, iOS, and Android. Tens of millions of members take part in communities worldwide, and the product is built to foster engagement, learning, and connection at scale.

As demand grew across Europe and Latin America, the platform still ran entirely in English. Communities were forming in Spanish, German, and other languages while the interface around them stayed in English, which made the product a harder choice in exactly the markets it wanted to win. Serving members in their own language had become a condition of continued growth.

The task was to restart a localization effort that had stalled, without letting translation become a drag on engineering.

The groundwork was partly there. The codebase already had internationalization foundations, with strings marked for translation. But the previous translation vendor had gone out of business and stopped supporting the libraries the platform depended on, which left localization stalled with no active pipeline across web, iOS, or Android.

The cost of staying English-only was rising. International demand was growing across Europe and Latin America, yet communities forming in other languages were navigating an English interface. That mismatch made the platform a weaker choice in the very markets it was trying to enter, and the goal was unambiguous: expand into new markets by delivering fully localized experiences.

The obvious fix was the one leadership wanted to avoid. A manual translation process would have worked against product velocity, turning every copy change into a translation task that engineers, designers, and PMs had to track and coordinate. At the platform's release cadence, that overhead would have throttled development. Any solution had to scale continuously, fit into existing workflows, and stay out of the team's way.

Rather than treat localization as a content problem to manage, Lumenalta®architected it as an automated system embedded directly in the software development lifecycle. The guiding principle was simple: translation should just happen. Engineers, designers, and PMs should never have to think about localization for it to occur.

Working alongside the client's team, Lumenalta®:

The first decision mattered as much as the architecture. Because a vendor's collapse had caused the original stall, resilience led the selection criteria: Crowdin brought a supported platform, built-in AI translation, and a marketplace of professional translators on the same system, so vendor risk would never again become a single point of failure.

Lumenalta®built a continuous localization system that lives inside the development pipeline, combining AI speed with human oversight so translation keeps pace with the product without anyone managing it.

An AI-embedded localization pipeline in CI/CD. Localization runs inside the pull request lifecycle. When a contributor opens a PR, the system detects whether copy has changed; if it has, a GitHub Action extracts the new strings and pushes them to Crowdin, AI engines generate translations across every supported language, and the results sync back into the same PR. Merging the PR ships all translations simultaneously across platforms. Because everyone at the company has code access, a PM or designer can trigger the same flow with a copy change of their own. From the developer's side, nothing changes: they open a PR as usual, and the translations arrive in it. Localization became an invisible layer of the pipeline rather than a task anyone owns.

Per-language model optimization. Rather than route every language through one engine, translation quality was tuned per language. Comparative testing across multiple frontier models (Anthropic, OpenAI, Gemini) identified the strongest performer for each, with glossaries and style guides embedded in the prompts to hold tone, brand voice, and terminology. First-pass AI output was already high quality before any human reviewed it.

Human-in-the-loop quality governance. Speed mattered, but so did cultural and contextual accuracy. Automated triggers compare new copy against style guides, flag high-impact or sensitive strings, and route only those to professional translators in Crowdin's marketplace. Reviewer edits sync back to the codebase through daily automated workflows, so human oversight raises quality without interrupting sprint cycles.

Standardized across every platform. The same localization process runs across iOS, Android, and web (Ruby on Rails), keeping the experience consistent regardless of device.

Engineering judgment at the core. AI handled extraction, translation, synchronization, and monitoring; senior engineers designed the GitHub Actions architecture, set the thresholds for escalating a translation to human review, evaluated system trade-offs, and owned performance and reliability across platforms. Automation amplified the team rather than replacing it.

The system shipped and adoption began immediately. But the more durable result is the capability the platform now owns: continuous, automated localization that opens new markets without trading away engineering velocity.

Early traction

A capability unlocked

Localization went from a stalled initiative, orphaned by a failed vendor, to a fully automated, AI-powered system that runs without anyone having to think about it. The platform can now serve its global communities in their own language, treat each new market as a matter of configuration rather than a project, and build the regional pricing and marketing strategies that come next, all without slowing the product down.