Rapidata Raises €7.2M to Scale Human Feedback for Faster AI Development – News Directory 3

Zurich-based startup Rapidata has raised €7.2 million in seed funding to build a global network that delivers real-time human feedback to AI models. Founded in 2023, the company aims to accelerate AI development by making high-quality human judgment readily available on demand, helping teams iterate faster and align systems more closely with real-world expectations.

Turning Human Insight into AI Velocity

While modern AI systems can generate remarkable text, images, and code, they often falter on nuance, context, and judgment—the strengths of human cognition. Many leading models address this gap through reinforcement learning from human feedback (RLHF), a technique in which people review model outputs and provide preferences or ratings to guide future behavior.

The bottleneck has not been the concept of RLHF itself, but the ability to gather reliable feedback quickly and at scale. Traditional approaches rely on static pools of annotators or slow-moving data labeling pipelines. These methods can be costly, time-consuming, and difficult to adjust as models and requirements change.

Rapidata’s On-Demand Feedback Network

Rapidata’s platform offers AI teams instant access to a continuously available, global network of participants. Instead of queuing tasks in fixed cohorts or segmented markets, developers can draw on real-time human input that expands or contracts with their needs. This model is designed to:

• Reduce feedback cycles from weeks or months to hours or days
• Maintain consistency and quality across diverse tasks and domains
• Support rapid iteration, evaluation, and fine-tuning of models
• Provide broader geographic and demographic coverage when required

The result is a more dynamic and responsive feedback loop, enabling AI systems to evolve continuously rather than waiting for major release cycles. Beyond simple data labeling, Rapidata focuses on facilitating ongoing human-in-the-loop processes that strengthen model reliability, safety, and alignment.

Funding to Expand Network and Integrations

With the newly raised seed capital, Rapidata plans to scale its human feedback network by onboarding more contributors around the world and strengthening the infrastructure that connects this network to AI development workflows. Priority areas include:

• Expanding participant reach to improve coverage and availability
• Enhancing quality controls, task routing, and reviewer calibration
• Deepening integrations with popular ML ops and evaluation tools
• Streamlining developer experience with APIs and real-time dashboards

The company’s goal is to make high-quality human signal as accessible and dependable as cloud compute—turning feedback into a scalable resource that any AI team can tap when needed.

Positioning Within the AI Infrastructure Landscape

Rapidata’s emergence reflects a broader shift in AI infrastructure, where human judgment is recognized as a foundational layer rather than an afterthought. Alongside efforts in areas such as data governance, synthetic data, and evaluation tooling, a robust human feedback network is increasingly seen as critical to building trustworthy and adaptable AI systems.

Across Europe, a wave of startups is tackling different pieces of this stack. Companies focused on governance, synthetic data generation, specialized evaluations, and tooling are helping AI developers move faster while keeping safety, compliance, and performance in view. Rapidata is carving out the human-feedback layer within this evolving ecosystem.

“Human Feedback as the Limiting Factor”

“Human feedback has become the limiting factor in AI progress. Rapidata removes that ceiling by making 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,” said Jason Corkill, CEO and founder of Rapidata.

What This Means for AI Teams

For practitioners, the promise of Rapidata’s approach is the ability to validate and refine models as quickly as they can be trained. Faster, higher-quality feedback can help teams:

• Detect and correct failure modes earlier in development
• Improve model alignment with product and policy goals
• Adapt to new domains and edge cases with less friction
• Shorten the path from research to production

As AI systems become more capable—and more deeply embedded in products—human feedback serves as a key control mechanism to ensure usefulness, safety, and real-world relevance. By scaling access to that feedback, Rapidata aims to help unlock the next wave of AI progress.