Labelbox is a leading training data platform for the enterprise investing in accelerated global expansion and engineering innovations
Labelbox’s software platform is designed to facilitate the entire training data iteration loop that improves ML model performance.
Labelbox was founded in 2017 by Manu Sharma, Daniel Rasmuson, and Brian Rieger. The company is headquartered in San Francisco, California.
Labelbox integratess a collection of tools to annotate data and train AI models, conduct error analysis to identify data on which the model performs poorly, refine annotations found to be incorrect or ambiguous, supplement data through augmentation or additional data collection and then test the model and repeat the error analysis in a continuous loop that improves model performance.
Labelbox automates the process with a web-based platform that pre-labels data and allows enterprises to collaborate easily across databases, BPOs and labeling services regardless of time zone or geography.
Labelbox is currently being used by industries as diverse as agriculture, insurance, healthcare, media, and military intelligence with customers that include ArcelorMittal, Chegg, Genentech and Warner Brothers.
Labelbox is backed by Andreessen Horowitz, First Round Capital, SoftBank Vision Fund 2, Gradient Ventures, Kleiner Perkins, B Capital Group, Databricks Ventures, and others. The company raised $110M in Series D round on Jan 06, 2022. This brings Labelbox's total funding to $189M to date. The latest round mints Labelbox a unicorn with a valuation of $1B post-money.