Labelbox is a data-centric AI platform that helps teams create, curate, and manage high-quality training data for machine learning and generative AI. It brings labeling software, data exploration, and human workforce services together in one system, aimed at teams that want to both build datasets and continuously improve them based on model performance.
The platform supports labeling across multiple modalities, including image, video, text, audio, and document data, with AI-assisted pre-labeling to speed up annotation. Beyond labeling, Labelbox emphasizes data curation, letting teams search, filter, and prioritize which data to label using model predictions and embeddings. A model diagnostics layer connects labeled data back to model behavior, and a Python SDK supports programmatic data operations. Labelbox also operates an on-demand labeling service, drawing on a vetted network of subject-matter experts and linguists to generate and review data, which is increasingly used for RLHF, preference ranking, and evaluation of large language models.
In the AI data supply chain, Labelbox positions itself as a hub that connects raw data, labeling workflows, human experts, and model feedback. Buyers range from computer vision teams building task-specific models to organizations fine-tuning and aligning generative models. Compared with pure services companies, Labelbox leads with software and self-serve tooling, while its managed expert workforce lets customers scale annotation without building their own labeling operations. The combination is intended to shorten the loop between collecting data, improving it, and measuring the effect on model quality.
Multimodal