Labelbox

Labelbox supports images, video, text, audio, PDFs, documents, geospatial tiles, and conversational data. Each modality has a purpose-built editor with specialized tools like bounding boxes, polygons, semantic segmentation, NER, temporal classifications, and multimodal chat interfaces.

Labelbox Foundry provides pre-loaded frontier models that generate pre-annotations on your data. Labelers then review and correct these predictions instead of annotating from scratch, significantly accelerating throughput while maintaining quality through human oversight.

The multimodal chat editor enables live or offline evaluation of LLMs. You can compare up to 10 models in a chat arena, capture human preference judgments, perform step-by-step reasoning analysis, and generate rubric-based scores across text, code, vision, and audio tasks.

Labelbox delivers RLHF and reinforcement learning data through expert-crafted scoring rubrics, preference pairs, and reward signals. The Alignerr network provides domain specialists across coding, science, finance, and more to produce high-quality post-training data at scale.

Yes. Labelbox offers a Python SDK, GraphQL API, and native integrations with 25+ data sources including AWS S3, Google Cloud Storage, and Azure Blob. You can use delegated access or signed URLs to keep data in your own cloud environment without transferring assets.