Ango is a platform for creating, managing, and automating AI data labeling and annotation workflows to efficiently build and scale machine learning training datasets.
Ango is an end-to-end platform for AI data labeling, annotation, and workflow automation designed to support computer vision, NLP, and multimodal machine learning projects. It centralizes dataset preparation, quality control, and task orchestration so teams can build and maintain high-quality training data at scale. The platform is built to serve both technical and non-technical users involved in data operations and model development.
Key capabilities include a browser-based annotation interface with support for images, video, audio, documents, and text, as well as specialized tools for bounding boxes, polygons, segmentation, keypoints, OCR, and classification. Ango provides workflow automation for task assignment, review, consensus, and QA, enabling teams to define custom pipelines and maintain consistent labeling standards. It integrates with existing data lakes and MLOps stacks via APIs and SDKs, and supports versioning, audit trails, and analytics to monitor labeling performance and data quality over time. Role-based access control and project-level permissions help manage large, distributed teams securely and efficiently.
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