Datacurve raises $15 million to take on Scale AI

As AI companies mature, the battle 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. But now that Wang has made the transition to AI at Meta, many funders see an opening – and are willing to fund companies with compelling new training data collection strategies.

The Y Combinator graduate Data curve is one such company that focuses on high-quality data for software development. On Thursday, the company announced a $15 million Series A round led by Chemistry’s Mark Goldberg with participation from employees from DeepMind, Vercel, Anthropic and OpenAI. The Series A comes after a $2.7 million seed round invested by former Coinbase CTO Balaji Srinivasan.

Datacurve uses a ‘bounty hunter’ system to recruit skilled software engineers to complete the most difficult to obtain data sets. The company pays for these contributions and has distributed more than $1 million in incentives to date.

But co-founder Serena Ge (pictured above with co-founder Charley Lee) says the biggest motivation isn’t financial. For high-value services such as software development, the pay for data work will always be much lower than for conventional work. So, the company’s main advantage is a positive user experience.

“We are treating this as a consumer product, not as a data labeling operation,” Ge said. “We spend a lot of time thinking about: How can we optimize it so that the people we want are interested and end up on our platform?”

This is especially important as data needs become more complex after training. While previous models were trained on simple data sets, today’s AI products rely on complex RL environments, which must be constructed through specific and strategic data collection. As environments become more sophisticated, data requirements become more intense, both in quantity and quality – a factor that could give high-end data collection companies like Datacurve an edge.

As a start-up company, Datacurve focuses on software engineering, but Ge says the model can just as easily be applied to areas like finance, marketing or even medicine.

“What we’re doing now is creating a post-training data collection infrastructure that attracts and retains highly competent people in their own domains,” says Ge.