Scale Data Pipelines

Optimize your data annotation spend by annotating what matters most.

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Trusted by the world’s most ambitious AI teams.Meet our customers

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    Better together

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    Optimize Spend

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    Accelerate Development

  • Visualize Data

  • Curate for Annotation

  • Annotate Data

  • Enhance Instructions

  • Review Annotation Quality

  • Manage & Evaluate

“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

“As Nuro works to ensure efficient deliveries as safely as possible, we depend on tools like Nucleus to curate edge cases which we can use to train ever more accurate and capable models.”

Jack Guo

Head of Autonomy Platform, Nuro

“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

“Motive encounters all manners of surprising edge cases in real world data collection, so when it comes to knowing we’re labeling the most valuable subset of our collected data, we turn to Scale Nucleus. Its intuitive visualizations, query engine and Autotag help our teams improve both data quality and models, all in the same motion.”

Ali Rehan

Engineering Manager AI/Vision Products, Motive

“Scale Rapid truly stands out from other data labelling solutions in the market. We used it for text categorization and were impressed by the turnaround time and thoughtful design of the workflow for taxonomy creation, quality lab and built-in metrics for auditing along with the other features such as API access, price estimator, etc. which together make it a great self-serve solution.”

Nikhil Raju

Senior ML Engineer, Grata

“Once our data was uploaded, we were able to train, validate and deploy 3 classifiers to our full dataset to identify police cars, ambulances, and firetrucks in just 4 hours, identifying a large number of these rarer case images to send to labeling. This process would have taken weeks without Nucleus.”

Varun Sundar Rabindranath

ML and Perception Engineer, Magna International

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