Labellerr

Labellerr is an AI-assisted data labeling platform designed to produce high-quality, scalable training data for machine learning and computer vision models. It combines human-in-the-loop annotation workflows with automation to help teams create accurately labeled datasets for tasks such as image, video, text, and document understanding. The primary purpose of Labellerr is to streamline the end-to-end data annotation lifecycle so data science and ML teams can focus on model development rather than manual labeling operations.

Labellerr supports a wide range of annotation types, including bounding boxes, polygons, semantic and instance segmentation, keypoints, text classification, named entity recognition, and document field extraction. The platform offers project management tools for task assignment, progress tracking, and quality control, including consensus labeling, review workflows, and annotation guidelines. It integrates with cloud storage and MLOps pipelines, enabling direct import/export of datasets and model-assisted labeling using active learning or pre-labeling from existing models. Role-based access control, detailed analytics, and audit trails help teams maintain data security, compliance, and consistent labeling standards at scale.