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.
Deliver exceptional data and superior performance with Welo Data.