San Francisco-based data training platform Labelbox announced Thursday that it raised $110 million in a Series D round led by SoftBank’s Vision Fund 2.
Labelbox operates as an AI data training platform for enterprises. The company’s software works on a continuous loop in order to annotate and train data models, conduct error analysis, refine annotations, test and repeat. All of this is done with an aim to improve machine learning model performance.
With Labelbox, customers can accelerate their iteration cycles by up to 800 percent, according to the company.
“It’s not just about annotation,” Brian Rieger, co-founder and president of Labelbox, said in a statement. “We cover this entire iteration loop on a single platform, continually optimizing the data with a focus on getting more and more efficient over time.”
Instead of having companies build out their own data labeling suites, which can be quite costly, the Labelbox platform acts as mission control for data scientists that need to work with dispersed annotation teams. The company’s software enables enterprises to link up with databases and labeling services around the world at any time.
“Data is the new oil and labelling is one of the most essential parts of the refinery,” Robert Kaplan, investment director at SoftBank Investment Advisers, said in a statement.
Labelbox aims to improve ML performance at several leading businesses including Chegg and Warner Brothers. The platform has been deployed across a myriad of industries including agriculture, healthcare, media and military intelligence.
Following the latest raise, Labelbox plans to grow its headcount. The company is now hiring for 25 remote roles across its engineering, sales and product teams, to name a few.
Labelbox has raised $189 million in venture capital financing to date, according to the company.
