Human Evaluation and Labeling
Search Relevance Labeling at Scale
Customer Story 01 · Human Evaluation and LabelingThe Challenge:
A global search giant needed more than 2 million accurately labeled examples to improve how its engine ranks video, image, news, and landing pages. The rules changed every week, and reviewers also had to judge AI-written content, in nine languages.
Effective Solution:
Volga built nine native-language teams of 2,000+ linguists, each led by a senior linguist, so a Russian user's idea of a "recent" article was judged by a native Russian speaker. A guideline team joined weekly client syncs, turned every change into clear instructions, and piloted new rules with a small group first. Quality was checked in three layers: automated flags, language-lead audits, and expert escalation of the hardest cases to the client.
Results:
Better search results reached real users within weeks, built on judgments that held up even as the rules changed every week.
AI Feedback Triage Quality Layer
Customer Story 02 · Human Evaluation and LabelingThe Challenge:
A software powerhouse gets thousands of user comments every week, most of them short, vague, and messy. It had built an AI to sort them, but needed people to check its work, fix its mistakes, and show where it could be trusted.
Effective Solution:
Volga built the human layer behind the AI. Reviewers checked every routing decision, corrected wrong tags, and removed spam and duplicates. Volga set up clear roles: triagers, auditors, product-area specialists, and a team to get approved issues to engineers quickly. Volga tracked correct calls, wrong calls, and manual fixes separately, so success meant accuracy, not just volume.
Results:
Engineers stopped sifting through noise and started receiving issues that were already checked and in the right place.
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