Rapidata Has Eliminated the Biggest Bottleneck in AI Development – Human Feedback

Rapidata, a company that accelerates AI development through scalable, on-demand data labeling via digital ads, led by Jason Corkill, Luca Strebel, Marian Kannwischer, Mads Alber, and the team, has raised an $8.5 million seed round co-led by Canaan Partners and IA Ventures with participation from Acequia Capital and BlueYard.

Rapidata addresses a key AI bottleneck: the slow manual collection of human feedback used to train and improve models. Its platform provides fast, global, on-demand human data, reducing timelines from weeks to hours. The funding will scale Rapidata’s human data network and support rising demand from AI companies seeking quicker and more reliable feedback for training and validation.

According to Jason Corkill of Rapidata, scalable near-real-time human judgment removes a major constraint on AI progress by enabling continuous feedback loops and daily model improvement instead of release-cycle iteration.

As AI development accelerates, timely human feedback has become a key bottleneck. Despite rapid advances in compute and model design, collecting high-quality judgments and validation data remains slow, costly, and operationally complex, with traditional feedback cycles often taking weeks or months and delaying iteration for teams building systems such as Rapidata.

Rapidata enables AI teams to collect large volumes of human feedback at high speed and scale through a continuously available global network, replacing static annotation pools. Feedback cycles that once took months can now be completed in days or even within a single day, significantly shortening development timelines and enabling near-real-time model iteration.

According to Lily Clifford of Rime, the platform from Rapidata enables real-world testing of voice models with targeted global users in days rather than months, accelerating iteration and supporting rapid growth.

Viren Tellis of Uthana noted that Rapidata enables large-scale evaluation and feedback for human-motion foundation models, removing limits of internal or overseas human testing and preventing iteration slowdowns.

Rapidata integrates directly into AI development workflows, allowing teams to request targeted human feedback on demand. Short opt-in tasks are distributed through widely used consumer apps, reaching tens of millions of users daily without disrupting their experience. The system builds trust and expertise profiles to match questions with relevant respondents, delivering high-quality data at scale without managing custom annotation operations.

Jared Newman of Canaan Partners emphasized that scalable human judgment is essential across AI lifecycles and said Rapidata is positioned to serve demand spanning foundation models, enterprise AI, and emerging AI-driven products.