Labelbox is a collaborative training data platform for machine learning applications. It is the simplest platform to train and operate machine intelligence.

Labelbox solves the problem of taking artificial intelligence and machine learning initiatives from research and development into production.

Labelbox was founded in 2017 by Manu Sharma, Daniel Rasmuson, and Brian Rieger. The company is headquartered in San Francisco, California.

Labelbox created the world’s first training data platform that acts as a central hub for data science teams to create and manage training data with internal or external labeling teams.

Labelbox is the complete solution for your training data problem with fast labeling tools, human workforce, data management, a powerful API and automation features. It's end-to-end platform to create the right training data, manage the data and process all in one place, and support production pipelines with powerful APIs.


Labelbox is currently being used by industries as diverse as agriculture (where it is used to identify weeds in fields, for example), to insurance (to spot risks), to sports analysis (to track the spin on balls or the actions of players on the field), to healthcare (identifying tumors or cells).


Labelbox is backed by Andreessen Horowitz, First Round Capital, Gradient Ventures, Kleiner Perkins and others. The comany raised $25M in a Series B financing on Feb 04, 2020. This brings Labelbox's total funding to $39M to date.



  • Year founded: 2017
  • Funding Info: $39M over 3 Rounds (Latest Funding Type: Series B)
  • Yearly Revenue: NA
  • Employee Size: 11-50
  • Business Valuation: NA
  • City/Town: San Francisco
  • State: California
  • Country: United States
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