Editorial illustration for Rapidata aims to cut model cycles from months to days, cites data‑annotation woes
Rapidata Slashes AI Model Training to Days, Not Months
Rapidata aims to cut model cycles from months to days, cites data‑annotation woes
Rapidata is positioning itself as a fast‑track for AI teams that are sick of waiting months for model iterations. The startup promises near‑real‑time reinforcement learning from human feedback, aiming to shrink development cycles to a matter of days rather than the usual, drawn‑out timelines. At the heart of that ambition lies a problem that many practitioners silently acknowledge: the grind of labeling and curating data.
Even with sophisticated pipelines, the moment a project needs human‑generated annotations, progress stalls. That bottleneck isn’t just an inconvenience; it can dictate whether a product ever reaches market. For the company’s co‑founder, the issue is personal.
He’s spent years in robotics, AI and computer vision, earned his degree at ETH in Zurich, and has repeatedly run into the same roadblock. As he put it in a recent interview, “—”
"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.
Rapidata promises to shrink model‑training cycles from months to days, betting on near‑real‑time RLHF to sidestep the annotation bottleneck that still drags most AI projects. Yet RLHF, at its core, remains a tutoring system that leans heavily on human contractors to rank and rate outputs after a curated‑data pre‑training phase. Corkill, a veteran of robotics, AI and computer‑vision work, voiced a familiar frustration: “Whenever you needed humans or human data annotation, that’s when…”.
The company’s narrative therefore hinges on whether faster feedback loops can truly reduce the volume of human‑in‑the‑loop work or merely accelerate it. If annotation speed improves, developers might see shorter iteration loops; if not, the promised timeline could prove optimistic. Unclear whether the “near real‑time” claim will translate into measurable gains across diverse model families.
The tension between automation hype and persistent human dependence stays front‑and‑center, leaving the impact of Rapidata’s approach an open question.
Further Reading
- Supervised Fine Tuning (SFT) | Curated Datasets at Scale | Rapidata - Rapidata
- Rapidata: The Venture Leader Technology powering AI with rapid and scalable human data processing - Venture Lab Switzerland
- Recommender Systems Lead - Rapidata - Rapidata
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Common Questions Answered
How does Rapidata aim to transform model evaluation and training cycles?
Rapidata seeks to dramatically reduce model development timelines from months to days by providing near-real-time reinforcement learning from human feedback. The platform enables AI teams to quickly obtain detailed human annotations and insights, addressing the traditional bottleneck of data labeling and model iteration.
What specific challenges does Rapidata address in AI model development?
Rapidata targets the critical pain point of human data annotation, which traditionally stalls AI projects by creating lengthy delays in model training and evaluation. By offering programmatic access to large-scale human feedback and real-time annotation capabilities, the platform helps AI teams overcome the frustrating roadblocks associated with obtaining high-quality, nuanced human insights.
What types of model evaluation insights can researchers obtain through Rapidata?
Rapidata provides detailed model performance evaluations across multiple dimensions, including realism, aesthetics, and alignment with text prompts. The platform allows researchers to collect rich, multi-dimensional feedback such as Likert scale ratings on criteria like image coherence, style, and prompt alignment, enabling more comprehensive model assessment.