Top AI Product

There’s a bottleneck in AI that nobody outside the industry talks about much: getting human feedback at scale. Sure, we’ve got massive compute clusters and clever model architectures, but when it comes to actual humans evaluating AI outputs — ranking which image looks more natural, which text sounds better — the process is painfully slow and expensive. That’s the problem [Rapidata](https://www.rapidata.ai/) is going after, and their approach is honestly one of the more creative things I’ve seen lately.

Here’s the idea. You know those ads you skip through in Duolingo or Candy Crush? Rapidata replaces some of those ad slots with tiny RLHF tasks. Instead of watching a 30-second ad for car insurance, users can opt into a quick “which of these two images looks more realistic?” comparison. The app developers get paid, users get a less annoying experience than traditional ads, and AI labs get human feedback flowing in at a rate of about 100,000 responses per hour. Everybody wins.

The company just came out of stealth with an [$8.5 million seed round](https://venturebeat.com/data/rapidata-emerges-to-shorten-ai-model-development-cycles-from-months-to-days/) led by Canaan Partners and IA Ventures, with Acequia Capital and BlueYard also participating. [VentureBeat broke the story](https://venturebeat.com/data/rapidata-emerges-to-shorten-ai-model-development-cycles-from-months-to-days/) on February 19th, and it’s been picking up coverage from [EU-Startups](https://www.eu-startups.com/2026/02/zurich-based-ai-infrastructure-startup-rapidata-raises-e7-2-million-to-scale-global-human-feedback-network), [The Next Web](https://thenextweb.com/news/zurichs-rapidata-raises-e7-2m-to-build-a-real-time-human-feedback-network-for-ai), and others since then.

What makes this more than just a clever distribution hack is the “online RLHF” angle. Traditional feedback collection happens in batches — you gather a dataset, annotate it, wait weeks or months, then feed it back into training. Rapidata lets AI teams pipe human judgments directly into their training loops through an API. They’ve got a [Python SDK on GitHub](https://github.com/RapidataAI/rapidata-python-sdk) and solid [documentation](https://docs.rapidata.ai/) that makes integration pretty straightforward. They even shipped a fun side project called [human-use](https://github.com/RapidataAI/human-use) — an MCP server that lets AI agents ask real humans for input on the fly.

Based in Zurich and founded in 2023, Rapidata has built up a network touching roughly 20 million mobile app users. The real question is whether this can maintain quality at that kind of scale — crowdsourced feedback is only useful if it’s actually reliable. But the early signs are promising, and if they pull it off, the gap between “we trained a model” and “we trained a model that humans actually prefer” could shrink dramatically.