Labelbox provides data-centric AI infrastructure for training and evaluating large-scale models, operating both a software platform and a managed workforce of over 1 million knowledge workers across 40+ countries through its Alignerr expert marketplace. Founded in 2018 and backed by $189 million in funding, the company serves over 80% of leading AI labs in the United States alongside Fortune 500 enterprises, positioning itself at the intersection of annotation tooling, frontier data services, and human-in-the-loop labeling workflows.
The Labelbox Platform integrates three components: enterprise annotation tools for in-house labeling operations, frontier data labeling services for specialized AI use cases, and the Alignerr marketplace for access to domain experts. Technical focus areas span reinforcement learning data generation, RLHF (Reinforcement Learning from Human Feedback) workflows, model evaluation datasets, and robotics training data. The architecture treats data labeling as a production system problem - managing throughput, quality control, and workforce coordination at scale rather than as a one-time annotation task.
Labelbox's operational model addresses the bottleneck between model development cycles and training data availability. By maintaining a global expert network alongside software tooling, the company provides both the infrastructure for teams to manage their own labeling pipelines and the capacity to outsource specialized data work. This dual approach targets the reliability and latency constraints of organizations building production AI systems, particularly those requiring domain expertise (medical imaging, legal reasoning, code generation) or high-volume synthetic data generation for model alignment.