SuperAnnotate is a platform designed to support the full lifecycle of AI data development, from data annotation and labeling to quality assurance and model evaluation. It provides tools for annotating images, video, text, audio, and documents, and supports both human-in-the-loop workflows and automated annotation using AI-assisted labeling. Teams can manage annotation projects, assign tasks to internal or outsourced annotators, track quality metrics, and integrate data pipelines with downstream ML workflows. The platform includes an SDK and API for programmatic access, enabling engineering teams to embed annotation workflows into existing infrastructure. SuperAnnotate also offers a managed annotation service, connecting customers with vetted annotation teams. It targets organizations across the AI development spectrum, from research teams building training datasets to enterprises running large-scale data operations for production AI systems. The platform supports a range of data modalities and annotation types, including bounding boxes, segmentation masks, keypoints, named entity recognition, and more.
Target audience and deployment
- Startup
- SMB
- Mid-market
- Enterprise
- Cloud
- On-premise
- API
Key features
Use cases
- Annotate training data for computer vision models
- Automate annotation pipelines with AI-assisted labeling
- Manage annotation workforce and quality assurance
- Annotate text and NLP training data
- Evaluate and benchmark large language models
- Integrate annotation workflows into ML pipelines via API
Best for
- ML engineers who need to build and manage large-scale training data pipelines
- Data annotation managers who need to coordinate and quality-control labeling teams
- AI researchers who need structured datasets across multiple data modalities
- Enterprise AI teams who need to automate and scale data labeling operations
Integrations
Developer
Python SDK, REST API
AI models included
SAM (Segment Anything Model), YOLO
Databases
AWS S3, Google Cloud Storage, Azure Blob Storage