Case Study
SuperAnnotate

Rapidly connecting multilingual AI annotators to power next-generation training data

How Drafted helped SuperAnnotate — a leading AI data infrastructure company — quickly source qualified contributors across languages and technical domains.

About SuperAnnotate

SuperAnnotate is a leading AI data infrastructure company trusted by enterprises and AI labs worldwide. Their platform enables teams to create high-quality training and evaluation data for machine learning models — spanning annotation, RLHF (Reinforcement Learning from Human Feedback), supervised fine-tuning, and LLM evaluation. Ranked the #1 AI data company on G2, SuperAnnotate works with major technology companies to help build and improve the AI systems shaping the future.

The Challenge

To meet the demands of frontier AI training, SuperAnnotate needed a fast, reliable pipeline of qualified annotators who could handle specialized tasks — from creative English writing and mathematical reasoning to multilingual work in languages like Spanish, Arabic, and Japanese. The contributors also needed to be credentialed, vetted, and ready to onboard quickly across multiple geographies.

How Drafted Delivered

Drafted activated its institutional talent network to rapidly connect SuperAnnotate with contributors matched to their exact specifications. Leveraging our direct partnerships with universities and a global pool of verified candidates, we sourced annotators across a range of task types and languages — with contributors onboarding from the United States, United Kingdom, Australia, Japan, Egypt, Bulgaria, and beyond.

Languages

English, Spanish, Arabic, Japanese & more

Technical

JavaScript, Math & creative reasoning tasks

Global Reach

Contributors from 7+ countries, onboarded fast

The Result

Drafted quickly filled SuperAnnotate's pipeline with qualified, multilingual annotators ready to contribute to high-stakes AI training workflows. By tapping into our credentialed talent network, SuperAnnotate was able to move fast — scaling their contributor base across languages, programming tasks, and creative domains without sacrificing quality or vetting standards.

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