Proven Results for AI Teams
See how we've helped teams improve annotation quality, reduce rework, and accelerate model training.
~40–60%
reduction in labeling rework
Retail Shelf Intelligence
A major retail analysis company needed to annotate millions of shelf images with product-level bounding boxes and SKU classification.
Read case study >99.2%
annotation accuracy on DICOM
Medical Imaging Triage
A health-tech startup building an AI triage system needed high-accuracy DICOM annotation for chest X-rays and CT scans.
Read case study >3×
faster QA cycle time
Autonomous Perception QA
An autonomous driving company needed to scale their 3D point cloud annotation while maintaining strict quality standards.
Read case study >98.1%
segmentation accuracy
Food Grain Segmentation
An AgTech company needed pixel-level segmentation of grain types for automated quality grading in grain processing facilities.
Read case study >95%+
material classification accuracy
Waste Sorting Segmentation
A waste management AI company needed accurate segmentation of recyclable materials to power automated sorting systems.
Read case study >97%
audio event accuracy
Audio Labeling for Robotics
A social robotics company needed fine-grained audio event labeling to improve human-robot interaction and voice understanding.
Read case study >98.5%
person detection accuracy
Security Camera Detection
A smart security company needed real-time person detection annotation across thousands of hours of multi-camera footage.
Read case study >96%
field marking detection
Ice Hockey Field Marking
A sports analytics firm needed precise field marking and player tracking annotation for ice hockey broadcast footage.
Read case study >99%+
extraction accuracy
Healthcare Document AI
A health-tech company needed structured extraction from clinical documents, discharge summaries, and pathology reports.
Read case study >99.5%
defect detection rate
Manufacturing Defect Detection
A global manufacturer needed AI-powered visual inspection to detect surface defects, cracks, and assembly errors on production lines.
Read case study >Ready to Build Better Training Data?
Talk to our team about your annotation needs, quality requirements, and timelines.
