AI Automated Game Translationおもてなし

Play'n GO faced significant obstacles in scaling their game localization operations while maintaining translation quality across multiple languages. The organization struggled with inconsistent terminology—phrases like "Tavern Night" were translated differently within the same language, undermining brand consistency. Terms that should remain untranslated, such as "Lady Death" or capitalized words like "GAME," were either omitted or rendered inconsistently across translation cycles.

The existing translation workflow, which relied on Excel spreadsheet exports and manual human review, proved insufficient for their expanding game portfolio. Without dedicated localization testers fluent in all supported languages, Play'n GO depended on external certification labs to identify translation errors during final testing—a reactive approach that created bottlenecks. Additionally, the organization lacked automated quality assurance mechanisms and had no systematic way to track or measure translation consistency against established standards.

The manual validation process, combined with compliance requirements from regulatory bodies, meant that achieving market-ready translations typically took four to six weeks per game. This timeline constraint restricted Play'n GO's ability to launch titles simultaneously across multiple jurisdictions and increased pressure on their game design and QA teams.

Play'n GO partnered with Takara to develop a generative AI-powered proof of concept leveraging AWS infrastructure and services. The solution built upon Amazon Bedrock's multilingual capabilities, employing Lambda functions to orchestrate a sophisticated translation workflow that maintained glossaries and enforced "do-not-translate" rules.

The architecture enabled automated processing of game UI strings, marketing content, and help documentation while injecting approved terminology lists and contextual constraints directly into the translation pipeline. Rather than replacing human expertise, the system positioned native-speaker linguists as quality reviewers focused on high-value assessment rather than routine string conversion.

The approach incorporated a knowledge base system to support consistent terminology across games and established validation metrics—including term consistency checks against human-translated gold standards and post-edit distance measurements—to quantify translation quality. The solution maintained Play'n GO's existing user interface, ensuring continuity while upgrading backend capabilities to support simultaneous multi-language translations without queuing restrictions.

The proof of concept demonstrated measurable improvements in translation efficiency and quality. By automating routine translation work while preserving human oversight for cultural nuance and compliance verification, the system reduced external translation costs by an estimated 30 to 40 percent compared to traditional agency workflows.

The validated metrics showed term consistency achieving 90 percent alignment with professional human translations, while post-edit distance remained below 10 percent—indicating that machine-generated output required minimal human revision to meet quality standards. This efficiency gain supported a projected 50 to 60 percent reduction in time-to-market for translation workflows.

The solution enabled simultaneous localization across multiple language scripts—Latin, Cyrillic, and right-to-left systems—within a single processing cycle, effectively multiplying translation capacity by up to 10 times compared to sequential manual workflows. Compliance risks diminished as automated consistency checks reduced language-related certification failures by an estimated 80 percent, while internal design and QA resource demands decreased by approximately 70 percent.

The framework positioned Play'n GO to scale localization operations in parallel with game development expansion, supporting faster jurisdictional market entry and enabling a more consistent, culturally appropriate player experience across global markets.

Resource translation can be applied to any translation where there are strings stored in resource files for example for software applications.