Datacurve Raises $15 Million to Rival ScaleAI

As AI companies progress, the competition for top-notch data has escalated, leading to the emergence of firms like Mercor, Surge, and the prominent ScaleAI, established by Alexandr Wang. With Wang’s recent transition to AI leadership at Meta, numerous investors are spotting market opportunities and are keen to support innovative firms that offer distinct approaches to obtaining training data.

One notable contender is Datacurve, a Y Combinator alumnus that focuses on sourcing premium data for software development. Recently, Datacurve raised $15 million in a Series A funding round, spearheaded by Mark Goldberg at Chemistry, with backing from employees of DeepMind, Vercel, Anthropic, and OpenAI. This Series A follows an initial $2.7 million seed round, which saw contributions from former Coinbase CTO Balaji Srinivasan.

Datacurve utilizes a “bounty hunter” model to recruit skilled software engineers willing to take on challenging datasets. The company compensates these contributors, having already disbursed over $1 million in bounties.

However, co-founder Serena Ge underscores that the main incentive goes beyond financial gain. In high-value domains like software development, compensation for data tasks is considerably lower than conventional employment — thus, the real advantage lies in delivering an outstanding user experience.

“We consider this a consumer product, not just a data labeling service,” Ge noted. “We invest substantial effort into optimizing the user experience to ensure that the individuals we wish to attract are eager to join our platform.”

This emphasis becomes crucial as the need for post-training data becomes more complex. While earlier models operated on simple datasets, modern AI products rely on intricate reinforcement learning environments that require strategic and precise data collection. As these environments grow in complexity, the demand for both the quantity and quality of data increases — potentially giving firms like Datacurve an edge in high-quality data gathering.

Currently, Datacurve is concentrating on software engineering; however, Ge maintains that this model could easily be adapted to areas such as finance, marketing, or even healthcare.

“Our primary aim is to develop an infrastructure for post-training data collection that not only attracts but also retains highly skilled professionals in their fields,” Ge asserted.