Labelbox provides data infrastructure and training data services for AI development, operating since 2018 with $189 million in funding from Kleiner Perkins, Andreessen Horowitz, and SoftBank Vision Fund. The company's platform addresses the data labeling and annotation requirements for organizations building AI systems, with particular capabilities in reinforcement learning data generation, model evaluation, and robotics datasets. Their technical stack integrates three core solutions: enterprise annotation tools for on-platform labeling, managed frontier data labeling services, and an expert marketplace connecting customers to specialized labelers.
For robotics applications, Labelbox's platform enables the creation of training datasets required for perception, manipulation, and navigation systems. The company supports reinforcement learning from human feedback (RLHF) workflows and provides infrastructure for iterative model evaluation - both critical for developing robotic systems that must operate reliably in unstructured environments. Their architecture allows robotics teams to manage the data pipeline from raw sensor data through annotation to model training and validation.
The company operates Alignerr, a network of over one million knowledge workers distributed across more than 40 countries, providing access to domain expertise for specialized labeling tasks. This expert network supports the creation of training data requiring technical judgment or domain knowledge - common requirements in robotics applications where understanding physical constraints, safety boundaries, and task semantics matters. Labelbox reports partnerships with over 80% of leading AI labs in the United States and serves customers ranging from Fortune 500 companies to research institutions.