Use Cases

Computer Vision

  • Object Detection

    • Autonomous Vehicles and ADAS

    • Product Identification

    • Damage and Defect Detection

    • Health Diagnostics

  • Classification

    • Search and Ad Relevance

    • Recommendation Systems

    • Precision Agrigulture

    • Policy Enforcement

  • Text Recognition

    • License Plate Identification

    • ID Verification

    • Product Cataloging

How it works

Easy to Start, Optimize and Scale

Draw a box around each rooftop and pool.

2 callback_url: '',
3 instruction: 'Draw a box around each rooftop and pool.',
4 attachment: '',
5 objects_to_annotate: ['pool', 'rooftop'],
6 with_labels: true,
7 min_width: 30,
8 min_height: 30
9}, (err, task) => {
10 // do something with task
Run Extraction
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    ML-Powered Data Labeling

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    Automated Quality Pipeline

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    Comprehensive Label Support

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    Operational Excellence

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    Data Input Flexibility

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    Configurable Tasks

Quality Assurance

Best-In-Class Quality

Super Human Quality

Image tasks submitted to the platform are first pre-labeled by our proprietary ML models, then manually annotated and reviewed by highly trained workers depending on heuristic and learned error detectors, ml model confidence scores, and other quality metrics.

The resulting accuracy is consistently higher than what a human or synthetic labeling approach can achieve independently.

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