Datacurve raises $15 million to take on Scale AI project

As AI companies mature, the fight for high-quality data has become one of the most competitive areas in the industry, with companies like Mercor, Surge, and, most notably, Alexandr Wang’s Scale AI launching. But now that Wang moved on to run AI in MetaMany funders see an opportunity and are willing to fund companies with compelling new strategies for collecting training data.

Y Combinator graduate Data curve is one such company, which focuses on high-quality data for software development. On Thursday, the company announced a $15 million funding round led by chemistry major Mark Goldberg with participation from employees at DeepMind, Vercel, Anthropic and OpenAI. The Series A follows a $2.7 million seed round, which attracted investment from former Coinbase CTO Balaji Srinivasan.

Datacurve uses a “bounty hunter” system to attract skilled software engineers to complete datasets that are difficult to source. The company is paying for those contributions, having distributed more than $1 million in bonuses so far.

But co-founder Serena Gee (pictured above with co-founder Charlie Lee) says the biggest motivation isn’t financial. For high-value services like software development, the pay will always be much lower for data work than in traditional employment – ​​so the most important benefit to a company is a positive user experience.

“We are treating this as a consumer product, not a data classification process,” Ge said. “We spend a lot of time thinking about: How can we improve it so that the people we want will be interested and come onto our platform?”

This is especially important as post-training data needs become more complex. While previous models were trained on simple datasets, today’s AI products rely on them Complex RL environmentswhich must be built through specific and strategic data collection. As environments evolve, data requirements become more dense in terms of quantity and quality – a factor that can give high-quality data collection companies like Datacurve an advantage.

As an early-stage company, Datacurve focuses on software engineering, but Ge says the model could just as easily be applied to fields like finance, marketing, or even medicine.

“What we are doing now is creating an infrastructure to collect post-training data, so that it attracts and retains high-caliber people in their own fields,” says Ji.