Scaling Search and Localization Evaluation for a Global Media Ecosystem
Discover how Welo Data partnered with a global tech company to scale multilingual evaluations to 724k tasks in 19 locales, achieving 99% translation accuracy and 852% revenue growth.
Summary
A leading global technology company required scalable, culturally fluent human evaluation for its multilingual media ecosystem. Within one quarter, Welo Data enabled delivery of over 724,000 evaluation tasks — a 471% quarter-over-quarter increase — while expanding to 19 locales and launching new multilingual content workflows. Through automation, linguist-led QA, and dynamic resourcing, Welo Data delivered both scale and quality across high-stakes, multilingual evaluations.
Background
The client’s media evaluation program ensures that users across the globe are served accurate, relevant, and culturally appropriate content. Human evaluators assess:
- Search query relevance across multiple content types (apps, music, video, podcasts)
- Translation accuracy and cultural nuance
- Explicit content labeling and parental advisory tagging
- Transliteration and language pair fidelity — including complex pairs like Korean↔Japanese and Spanish↔English
These evaluations inform the optimization of search and recommendation engines, ensuring a high-quality user experience across diverse markets.
Challenge
The client faced two major challenges:
- Search Evaluation at Scale – Validating whether search results were accurate and contextually relevant across dozens of languages and digital storefronts.
- Localization Judgment – Assessing nuanced translations and cultural explicitness for lyrics and media metadata, including transliteration accuracy and explicit content detection.
The complexity was compounded by volatile task volumes and hard-to-source locales such as Japan and Korea, introducing both operational and quality risk.
Solution
Welo Data deployed a globally distributed evaluator network, supported by advanced QA systems, linguist expertise, and training innovations:
- Workforce Growth – Onboarded 2,577 analysts across 10 evaluation types and 19 locales, including 345 specialists for a new multilingual lyrics workflow.
- Automation & QA Infrastructure –
- Detection tools flagged suspected AI-generated or missing comments, triggering real-time feedback.
- Golden Sets and multi-coverage audits calibrated raters and improved quality.
- Training & Engagement –
- Targeted micro-trainings (“pills”), daily onboarding, and open office hours.
- Centralized disagreement resolution hub to ensure consistency.
- Performance incentives to drive retention in high-complexity locales.
- Adaptive Resourcing – Cross-trained raters across multiple content verticals, reducing idle time and maintaining throughput despite fluctuating demand.
Results
Scale Achieved:
- 724,000+ tasks delivered in Q3
- 29,500+ evaluator hours logged
- 19 active locales, including Japan, Korea, and China
- 345+ lyrics evaluators onboarded in two weeks
Quality Gains:
- Translation & transliteration consensus up to 99%
- Explicit content detection consensus at 94%
- Golden Set accuracy improved to 83% (Music)
- Multi-coverage accuracy reached 88%
- Baseline audit scores for search evaluation reached 87%
Operational Milestones:
- Weekly evaluator engagement exceeded 10 hours
- Japan ramped from 4,000 to 26,000 video tasks in one month
Business Impact:
- 852.78% revenue increase quarter-over-quarter
Why it Matters
This engagement demonstrates how pairing multilingual depth with sophisticated QA infrastructure can transform high-volume evaluation programs into high-accuracy, globally scalable systems. By combining human-in-the-loop expertise with automation, the client scaled from a limited pilot to a high-performing multilingual evaluation engine — positioning the program as a benchmark for localized, expert-level AI evaluation.
With over 1 million tasks forecasted and a 90% accuracy target in the next quarter, the program is now a reference model for executing trustworthy AI evaluations at scale.
Key Challenges | Welo Data Solutions
Key Challenges
- Scaling Search Evaluation Across Dozens of Locales – Large volume of multilingual search relevance tasks with volatile demand, especially for hard-to-source markets like Japan and Korea.
- Maintaining Quality While Expanding Rapidly – Risk of inconsistent judgments and QA drift due to fluctuating volumes and multiple evaluation types.
- Nuanced Localization Requirements – Transliteration, explicit content detection, and lyric translation accuracy required deep cultural fluency.
- High-Complexity Market Ramp-Up – Japan’s demand for video content evaluations jumped 6.5x in one month.
Welo Data Solutions
- Distributed Evaluator Network – Rapid onboarding of 2,577 analysts across 19 locales, with cross-training to maintain throughput during demand spikes.
- Advanced QA Infrastructure – Golden Sets, multi-coverage audits, and real-time detection tools to flag suspected AI-generated or incomplete evaluator output.
- Specialized Linguist-Led Workflows – 345 specialists onboarded in two weeks for a new multilingual lyrics workflow, supported by daily onboarding, micro-trainings, and open office hours.
- Adaptive Resourcing – Cross-trained raters across verticals to shift capacity where needed, ensuring both speed and accuracy.
Conclusion
By combining multilingual depth, linguist-led QA, and automation-driven oversight, Welo Data transformed a limited pilot into a global, high-performing evaluation program. The client achieved 724,000+ completed tasks in one quarter, 99% consensus on translation/transliteration, and an 852% revenue increase — all while expanding into high-complexity locales like Japan and Korea without sacrificing quality.
This engagement now serves as a benchmark for high-volume, culturally fluent AI evaluation, proving that with the right blend of human expertise and QA infrastructure, enterprise AI teams can scale both speed and trustworthiness in their multilingual systems.