Rapidata: The $8.5 Million Startup Gamifying AI Training by Paying You to Play Candy Crush Instead of Watching Ads

In a twist that would make even the most cynical tech observer do a double-take, a new startup is turning your casual gaming sessions into AI training sessions—and they’re paying you for it. Meet Rapidata, the company that’s transforming the tedious process of training artificial intelligence into something that feels suspiciously like playing mobile games.

The Human Bottleneck in AI’s Golden Age

While Silicon Valley breathlessly announces new AI breakthroughs daily, there’s a dirty little secret lurking beneath the hype: AI models still can’t function without armies of human beings telling them what’s what. It’s the ultimate irony of our AI-obsessed moment—machines that seem poised to replace humans still need humans to teach them how to be less robotic.

The process, known as reinforcement learning from human feedback (RLHF), is essentially AI tutoring. After an AI model is trained on massive datasets, it still produces outputs that range from slightly off to completely unhinged. Enter human contractors, typically hired in bulk from developing nations, who spend their days rating and ranking AI outputs like some dystopian quality control system. These workers, often paid wages that spark controversy and headlines about exploitation, are the unsung heroes making sure your AI assistant doesn’t suggest you wear a toaster to your next business meeting.

The Traditional Model Was Broken—Like, Really Broken

Historically, this human feedback process has been a logistical nightmare that would make any project manager weep. AI companies would contract with fragmented networks of foreign workers, creating static pools of annotators concentrated in specific low-income regions. The media loves to highlight the exploitative wage structures, and frankly, they’re not wrong. But there’s another problem that gets less attention: it’s incredibly slow.

We’re talking weeks or months to get a single batch of feedback. In AI development time, that’s approximately seventeen eternities. While GPUs get faster and models get bigger, the human feedback pipeline remained stubbornly medieval, creating a bottleneck that frustrated AI engineers who could push code updates at light speed but had to wait an eternity for human input.

The $8.5 Million Solution: Turn Ads Into AI Training

Enter Rapidata, a startup that just emerged from stealth mode with $8.5 million in seed funding co-led by Canaan Partners and IA Ventures. Their revolutionary idea? Why not turn those annoying mobile game ads into opportunities for AI training?

Here’s how it works: instead of watching a 30-second ad for that mobile game you’ll never download, you can opt to complete a quick AI feedback task. These tasks pop up in popular apps like Duolingo or Candy Crush, asking you to spend a few seconds judging whether an AI-generated response sounds natural or which of two summaries is more professional. Complete the task, and you get rewarded—effectively getting paid to help train the next generation of AI models.

The numbers are staggering. Rapidata’s platform reaches between 15 and 20 million users globally and can process 1.5 million human annotations in a single hour. That’s not a typo. While traditional methods took weeks or months, Rapidata can provide feedback in hours or even minutes. It’s like upgrading from dial-up internet to fiber optic, except instead of downloading movies faster, we’re making AI less terrible at sounding human.

From Beer to Billions: The Origin Story

The genesis of Rapidata reads like a Silicon Valley screenplay, minus the Ping-Pong tables and kombucha taps. Founder Jason Corkill was studying at ETH Zurich, working in robotics and computer vision, when he hit the wall that every AI engineer eventually faces: the data annotation bottleneck.

“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. “Always when you needed humans or human data annotation, that’s kind of when your project was stopped in its tracks.”

The frustration was universal among AI developers. You could push through coding challenges with late nights and energy drinks, but when you needed large-scale human annotation, everything ground to a halt. You’d have to contract with annotation services and then wait weeks for results while your model sat idle, like a race car stuck in the pit during a crucial moment.

The Technology: Turning Digital Footprints Into Training Data

Rapidata’s core innovation isn’t just clever—it’s elegant in its simplicity. Instead of building a new workforce from scratch, they’re leveraging the existing attention economy of mobile apps. By partnering with third-party apps, they offer users a choice that feels almost too good to be true: watch an ad or help train AI.

“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. The conversion rate is surprisingly high—between 50% and 60% of users opt for the feedback task over traditional video advertisements.

This “crowd intelligence” approach allows AI teams to tap into a diverse, global demographic at an unprecedented scale. The platform builds trust and expertise profiles for respondents over time, ensuring that complex questions are matched with the most relevant human judges. And while users are tracked via anonymized IDs to ensure consistency and reliability, Rapidata doesn’t collect personal identities, maintaining privacy while optimizing for data quality.

Online RLHF: Moving the Feedback Loop Into the GPU

The most significant technological leap Rapidata is enabling is what Corkill describes as “online RLHF.” Traditionally, AI training has been a disconnected, batch-oriented process: train the model, stop, send data to humans, wait weeks for labels, then resume. This creates a “circle” of information that often lacks fresh human input, like trying to navigate a city using a map that’s months out of date.

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. The model calculates some output, immediately requests human feedback, gets the answer, and applies that loss—all in near real-time.

“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 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 rather than simulated preferences.

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’s 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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