Artificial intelligence is advancing rapidly — but building reliable AI systems still depends on something very human: feedback from real people. To train, test, and improve models, teams must collect large volumes of human judgments, preferences, and validation data. Traditionally, this process has been slow, costly, and difficult to scale, often delaying how quickly new AI capabilities reach the real world.
Canaan company, Rapidata, is changing that. The company enables AI teams to gather high-quality human feedback on demand from a global network of participants, compressing development cycles from months into days — and sometimes hours. Instead of waiting for periodic evaluation rounds, teams can continuously test, refine, and improve their models as they build.
This approach is especially important as AI moves beyond research labs into everyday products and services. Faster access to diverse human input helps organizations identify issues earlier, validate performance in real-world contexts, and deploy systems with greater confidence.
VentureBeat recently explored how Rapidata’s platform works, why scalable human feedback has become a critical piece of modern AI infrastructure, and what this shift means for the future of AI development.