The startup, which announced the investment on Monday, has raised a total of $135 million since debuting in 2019. Venture capital firm Addition and investment giant BlackRock led the latest funding with additional participation by Greylock, GV (formerly Google Ventures), and Lightspeed.

Snorkel AI specializes in data labeling, the annotating of information for training a machine-learning system to recognize and act on patterns its technology discovers in other data sets. For instance, financial services firms could use Snorkel AI to label documents with keywords so that machine-learning systems could analyze other data sets for similar information, explained Snorkel CEO Alexander Ratner.

Human experts, like financial analysts or lawyers, determine which labels Snorkel AI’s software should apply. Software automates much of the labeling to avoid the time-consuming and expensive process of manually labeling huge data sets.

While at Stanford, Snorkel AI caught the attention of tech giants like Google, which maintain enormous data sets for training machine-learning models, or formulas. The team coauthored academic papers with Google about different data labeling techniques that helped the project gain visibility in the A.I. research community.

Kevin Petrie, the vice president of research at Eckerson Group, said that one of Snorkel AI’s strengths is its popularity with data scientists and its roots in academia, which gives the company credibility. A big challenge, however, is that the startup is so young and small that it hasn’t developed a reputation with large potential customers, which have plenty of other data-labeling options to choose from.

Big tech companies like Amazon have started offering data labeling services, as well as midsize business software companies including Appen. Additionally, several data labeling startups have emerged in recent years, such as Labelbox and CloudFactory. Data labeling rival Scale AI recently raised $325 million at a valuation of $7.3 billion, underscoring how venture capitalists consider data annotation to be critical as more companies adopt machine learning. 

Ratner’s strategy is for Snorkel AI to catch on with big companies, which don’t want to use contract or outsourced workers to hand-label data. Many data labeling companies and consulting firms like Accenture hire contract workers to annotate data, which could create privacy risks for regulated companies like financial services firms, he explained.

Some of Snorkel AI’s customers and partners include Apple, Intel, and Stanford Medicine.

Ultimately, Ratner, also a University of Washington computer science assistant professor, said that Snorkel AI can become a one-stop shop for companies to gather data, label data, and then create machine-learning models from that information. The money raised in the latest funding will be used to hire more staff to further develop the company’s technology.

“For A.I. to live up to the hype, we need to move beyond a totally manual way of data labeling,” Ratner said.