Rapidata emerges to shorten AI model development cycles from months to days with near real-time RLHF

Despite growing chatter about a future when much human work is automated by AI, one of the ironies of this current tech boom is how stubbornly reliant on human beings it remains, specifically the process of training AI models using reinforcement learning from human feedback (RLHF).

At its simplest, RLHF is a tutoring system: after an AI is trained on curated data, it still makes mistakes or sounds robotic.

Human contractors are then hired en masse by AI labs to rate and rank a new model's outputs while it trains, and the model learns from their ratings, adjusting its behavior to offer higher-rated outputs.

This process is all the more important as AI expands to produce multimedia outputs like video, audio, and imagery which may have more nuanced and subjective measures of quality.

Historically, this tutoring process has been a massive logistical headache and PR nightmare for AI companies, relying on fragmented networks of foreign contractors and static labeling pools in specific, low-income geographic hubs, cast by the media as low wage — even exploitative.

It's also inefficient: requiring AI labs wait weeks or months for a single batch of feedback, delaying model progress.

Now a new startup has emerged to make the process far more efficient: Rapidata 's platform effectively "gamifies" RLHF by pushing said review tasks around the globe to nearly 20 million users of popular apps, including Duolingo or Candy Crush, in the form of short, opt-in review tasks they can choose to complete in place of watching mobile ads, with data sent back to a commissioning AI lab instantly.

As shared with VentureBeat in a press release, this platform allows AI labs to "iterate on models in near-real-time," significantly shortening development timelines compared to traditional methods.

CEO and founder Jason Corkill stated in the same release that Rapidata 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."" Rapidata treats RLHF as high-speed infrastructure rather than a manual labor problem.

Today, the company exclusively announced to us at VentureBeat 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.

The pub conversation that built a human cloud The genesis of Rapidata was born not in a boardroom, but at a table over a few beers.

When Corkill was a student at ETH Zurich, working in robotics and computer vision, when he hit the wall that every AI engineer eventually faces: the data annotation bottleneck. "Specifically, I've been working in robotics, AI and computer vision for quite a few years now, studied at ETH here in Zurich, and just always was frustrated with data annotation," Corkill recalled in a recent interview. "Always when you needed humans or human data annotation, that's kind of when your project was stopped in its tracks, because up until then, you could move it forward by just pushing longer nights.

But when you needed the large scale human annotation, you had to go to someone and then wait for a few weeks".

Frustrated by this delay, Corkill and his co-founders realized that the existing labor model for AI was fundamentally broken for a world moving at the speed of modern compute.

While compute scales exponentially, the traditional human workforce—bound by manual onboarding, regional hiring, and slow payment cycles—does not.

Rapidata was born from the idea that human judgment could be delivered as a globally distributed, near-instantaneous service.

Technology: Turning digital footprints into training data The core innovation of Rapidata lies in its distribution method.

Rather than hiring full-time annotators in specific regions, Rapidata leverages the existing attention economy of the mobile app world.