A new startup, Rapidata, has emerged to revolutionize the process of training AI models using reinforcement learning from human feedback (RLHF), reducing development cycles from months to days with near real-time feedback. Historically, this process has been a logistical headache and PR nightmare for AI companies, relying on fragmented networks of foreign contractors and static labeling pools. Rapidata‘s platform “gamifies” RLHF by pushing review tasks to nearly 20 million users of popular apps, allowing AI labs to iterate on models in near-real-time.
Rapidata‘s platform treats RLHF as high-speed infrastructure rather than a manual labor problem. The company has announced its emergence with an $8.5 million seed round co-led by Canaan Partners and IA Ventures, with participation from Acequia Capital and BlueYard, to scale its unique approach to on-demand human data. According to Rapidata‘s CEO and founder, Jason Corkill, the platform makes “human judgment available at a global scale and near real time, unlocking a future where AI teams can run constant feedback loops and build systems that evolve every day instead of every release cycle.” The platform can process 1.5 million human annotations in a single hour, with feedback cycles reduced to hours or even minutes.
The core innovation of Rapidata lies in its distribution method, leveraging the existing attention economy of the mobile app world. By partnering with third-party apps like Candy Crush or Duolingo, Rapidata offers users a choice: watch a traditional ad or spend a few seconds providing feedback for an AI model. This “crowd intelligence” approach allows AI teams to tap into a diverse, global demographic at an unprecedented scale. Rapidata currently reaches between 15 and 20 million people, with 50% to 60% of users opting for the feedback task over a traditional video advertisement.
The company is positioning itself as an infrastructure layer that eliminates the need for companies to manage their own custom annotation operations. Jared Newman of Canaan Partners suggests that this infrastructure is essential for the next generation of AI. “Every serious AI deployment depends on human judgment somewhere in the lifecycle,” Newman said. “As models move from expertise-based tasks to taste-based curation, the demand for scalable human feedback will grow dramatically.” Rapidata is also enabling “online RLHF”, moving human judgment directly into the training loop, and integrating via API directly with the GPUs running the model.
The impact of Rapidata‘s platform is significant, with the potential to revolutionize the way AI models are trained and developed. With $8.5 million in new funding, the company plans to move aggressively to ensure that as AI scales, the human element is no longer a bottleneck, but a real-time feature. As Jason Corkill noted, “Society is in constant flux… If they simulate a society now, the simulation will be stable for and maybe mirror ours for a few months, but then it completely changes, because society has changed and has developed completely differently.” By creating a distributed, programmatic way to access human brain capacity worldwide, Rapidata is positioning itself as the vital interconnect between silicon and society.
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