COMPANY PROFILE

Labelbox

Explore full profile

Data-centric AI platform for building intelligent applications

San Francisco, United StatesFounded 2018AI

Valuation $250–500M Last recorded · 2022

Revenue $10–50M Latest reported FY

Headcount 100–500 Current team size

Total funding $100–500M Disclosed equity funding

Unlock exact values

About Labelbox

Labelbox offers a data-centric AI platform for creating and managing labeled data used in machine learning applications. The company was founded in 2018 by Manu Sharma, Brian Rieger, and Dan Rasmuson, who brought experience from aerospace and software engineering to address the data labeling bottleneck in AI development. Sharma, the CEO, and his co-founders identified the need for high-quality training data while working at companies like Planet Labs and DroneDeploy. They initially built and launched the product on nights and weekends, gaining rapid traction on Reddit before securing venture funding. To date, Labelbox has raised $189 million from investors including Andreessen Horowitz, SoftBank Vision Fund, and Gradient Ventures.

Read full description

The company operates on a Software-as-a-Service (SaaS) business model, generating revenue primarily through tiered subscriptions based on data volume, number of users, and required features. It also offers professional services, such as custom integrations and training. The platform serves a wide range of clients, from small businesses to Fortune 500 enterprises like Walmart, Procter & Gamble, and Genentech, across sectors including automotive, healthcare, retail, and geospatial.

The Labelbox platform is designed to function as a comprehensive data factory, providing an integrated solution with three core components: an enterprise software platform, a frontier data labeling service called Alignerr, and an expert marketplace. Key features include tools for data annotation across various formats like images, video, and text; workflow automation; quality control analytics; and real-time collaboration. The platform supports model-assisted labeling, which uses AI to accelerate the annotation process, and integrates with ML pipelines through an API-first approach. It also provides tools for model evaluation, including comparing LLM outputs and generating datasets for fine-tuning.

Source: Dealroom company data