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. 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.

"The users are asked, 'Hey, would you rather instead of watching ads and having, you know, companies buy your eyeballs like that, would you rather like annotate some data, give feedback?'" Corkill explained. According to Corkill, between 50% and 60% of users opt for the feedback task over a traditional video advertisement.

This "crowd intelligence" approach allows AI teams to tap into a diverse, global demographic at an unprecedented scale.

The global network: Rapidata currently reaches between 15 and 20 million people.

Massive parallelism: The platform can process 1.5 million human annotations in a single hour.

Speed: Feedback cycles that previously took weeks or months are reduced to hours or even minutes.

Quality control: The platform builds trust and expertise profiles for respondents over time, ensuring that complex questions are matched with the most relevant human judges.

Anonymity: While users are tracked via anonymized IDs to ensure consistency and reliability, Rapidata does not collect personal identities, maintaining privacy while optimizing for data quality.

Online RLHF: Moving into the GPU

The most significant technological leap Rapidata is enabling is what Corkill describes as "online RLHF". Traditionally, AI is trained in disconnected batches: you train the model, stop, send data to humans, wait weeks for labels, and then resume. This creates a "circle" of information that often lacks fresh human input.

Rapidata is moving this judgment directly into the training loop. Because their network is so fast, they can integrate via API directly with the GPUs running the model.

"We've always had this idea of reinforcement learning for human feedback… so far, you always had to do it like in batches," Corkill said. "Now, if you go all the way down, we have a few clients now where, because we're so fast, we can be directly, basically in the process, like in in the processor on the GPU right, and the GPU calculate some output, and it can immediately request from us in a distributed fashion. 'Oh, I need, I need, I need a human to look at this.' I get the answer and then apply that loss, which has not been possible so far".

Currently, the platform supports roughly 5,500 humans per minute providing live feedback to models running on thousands of GPUs. This prevents "reward model hacking," where two AI models trick each other in a feedback loop, by grounding the training in actual human nuance.

Product: Solving for taste and global context

As AI moves beyond simple object recognition into generative media, the requirements for data labeling have evolved from objective tagging to subjective "taste-based" curation. It is no longer just about "is this a cat?" but rather "is this voice synthesis convincing?" or "which of these two summaries feels more professional?".

Lily Clifford, CEO of the voice AI startup Rime, notes that Rapidata has been transformative for testing models in real-world contexts. "Previously, gathering meaningful feedback meant cobbling together vendors and surveys, segment by segment, or country by country, which didn’t scale," Clifford said. Using Rapidata, Rime can reach the right audiences—whether in Sweden, Serbia, or the United States—and see how models perform in real customer workflows in days, not months.

"Most models are factually correct, but I'm sure you're you have received emails that feel, you know, not authentic, right?" Corkill noted. "You can smell an AI email, you can smell an AI image or a video, it's immediately clear to you… these models still don't feel human, and you need human feedback to do that".

The economic and operational shift

From an operational standpoint, Rapidata positions itself as an infrastructure layer that eliminates the need for companies to manage their own custom annotation operations. By providing a scalable network, the company is lowering the barrier to entry for AI teams that previously struggled with the cost and complexity of traditional feedback loops.

Jared Newman of Canaan Partners, who led the investment, 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".

A future of human use

While the current focus is on the model labs of the Bay Area, Corkill sees a future where the AI models themselves become the primary customers of human judgment. He calls this "human use".

In this vision, a car designer AI wouldn't just generate a generic vehicle; it could programmatically call Rapidata to ask 25,000 people in the French market what they think of a specific aesthetic, iterate on that feedback, and refine its design within hours.

"Society is in constant flux," Corkill noted, addressing the trend of using AI to simulate human behavior. "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. 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.

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