Rapidata is an AI infrastructure company that helps organizations collect high-quality human feedback for training, evaluating, and improving artificial intelligence models. As modern AI systems increasingly rely on reinforcement learning from human feedback (RLHF), preference testing, and model evaluation, obtaining reliable human judgments has become one of the biggest bottlenecks in AI development. Rapidata addresses this challenge by providing an on-demand platform that enables AI companies to gather large volumes of human feedback in hours instead of weeks.
The company's platform distributes short, opt-in evaluation tasks through a global network of digital channels, allowing millions of participants to provide annotations, rankings, preference data, and validation feedback at scale. This approach eliminates many of the operational challenges associated with traditional data labeling vendors while significantly reducing the time and cost required to collect high-quality training data. Rapidata supports use cases including model evaluation, reinforcement learning from human feedback, dataset validation, quality assurance, and continuous AI improvement for foundation models and enterprise AI applications.
Founded by Jason Corkill, Marian Kannwischer, Luca Strebel, and Mads Alber, Rapidata is building infrastructure that treats human intelligence as a scalable resource for AI development. Its platform is already used by AI labs and technology companies to accelerate model iteration and improve output quality across text, image, video, and multimodal AI systems. By shortening feedback cycles from months to days—or even hours—Rapidata enables organizations to develop more capable AI products while reducing operational complexity and speeding up deployment.