The comprehensive data annotation platform.

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“Very quickly, our engineers liked what they saw and we asked Scale to ramp 10X throughput in a matter of weeks. Scale’s been able to support the extra throughput request and allowed us to do great research.”

David Garber

Product Manager, TRI

“Properly labeling and counting timber isn't the most common deep learning use case, so we turned to Scale Rapid for our somewhat unique image data labeling needs. Scale's team was able to adapt to our requirements and deliver high-quality labeled data on schedule. Scale Rapid removes the pain and time burden of manually labeling data on a tight timeframe!”

Scott Gregg

CEO and Founder, TimberEye

“Scale Rapid has made it easier for us to gather annotations at a good price point. The UI is simple to navigate, and the built in worker evaluation pipeline and batch options saves us time and helps enforce best practices so that we can get high-quality training data.”

Cassandra Ung

Software Engineer, Square

“OpenAI threw a bunch of tasks at Scale AI with difficult characteristics, including tight latency requirements and significant ambiguity in correct answers. In response, Scale worked closely with us to adjust their QA systems to our needs.”

Geoffrey Irving

Member of Technical Staff, OpenAI

“Scale AI has been a critical part of our AI development cycle: its powerful data platform fits into our data pipeline seamlessly providing efficient annotations for large volumes of data, its talented engineering and QA teams help delivering high quality of challenging data, and its customer operations team work very closely with us to help reaching our development goal successfully and smoothly.”

Fiona Hua

Lead Perception Engineer, Sea Machines Robotics

  • flexibility

    Any use case. Any task.

  • powered data labeling

    ML-powered data annotation

  • ribbon

    Automated quality pipeline

Super Human Quality

Annotation tasks submitted to the Scale platform are pre-labeled by our proprietary ML models(when applicable) or labeled from scratch. All tasks receive additional layers of both ML-based checks and human review based on confidence scores. The resulting quality is consistently higher than what a human or automated labeling approach can achieve independently.

Quality assurance