Computer Vision Data Annotation for a US Autonomous Mobility Startup

The challenge

• A 2.4 million image backlog blocking machine learning training cycles and product release milestones
• Intersection over Union (IoU) accuracy stuck at 89%, below the 95% threshold required for model deployment confidence
• Previous vendor delivering inconsistent taxonomy application across a distributed annotator team
• Data pipeline latency of 11 days from annotation submission to model training availability
• Cost per annotated frame trending prohibitively expensive at scaling volume

The Corpshore approach

• Recruited 34 annotators from the Iloilo tech corridor with structured visual pattern recognition training
• Deployed a three-layer QA system combining peer review, senior lead review and adversarial spot-check
• Built a custom annotation workflow integrating directly with the client's ML training pipeline via API
• Established weekly taxonomy calibration sessions with the client's ML engineering team
• Implemented a gold-standard reference dataset for continuous annotator training and drift detection

Results and impact