Revolutionary AI-powered vehicle damage assessment
Trusted by professionals worldwide
Built on cutting-edge research and trained on thousands of real-world cases, OXRED Shield delivers unmatched accuracy and reliability for insurance professionals.
State-of-the-art computer vision algorithms identify damage with 95%+ accuracy across 6 different damage categories.
Process claims in under 3 seconds instead of hours. Dramatically reduce processing time and operational costs.
Pinpoint exact damage locations with pixel-perfect accuracy using advanced segmentation technology.
Detect inconsistencies and potential fraud with AI-powered analysis that never misses a detail.
Comprehensive insights and reporting to optimize your claims process and reduce costs.
Seamlessly integrate with existing claims management systems through our robust API.
Our streamlined 4-step process transforms complex damage assessment into a simple, automated workflow that saves time and reduces costs.
Simply upload high-resolution vehicle images through our intuitive interface or API integration.
Supports JPG, PNG, HEIC formats up to 50MP resolution
Our advanced computer vision algorithms analyze the images using deep learning models trained on thousands of cases.
Processing completed in under 3 seconds with 95%+ accuracy
The system identifies and localizes damage across 6 categories with pixel-perfect precision and severity assessment.
Detects dents, scratches, cracks, glass, lamp, and tire damage
Receive comprehensive reports with visual overlays, damage summaries, and cost estimates for immediate decision-making.
Exportable in PDF, JSON, or integrate directly into your system
Our technology delivers measurable improvements to insurance operations
Built on solid academic foundations and industry best practices
Based on peer-reviewed computer vision research from leading institutions
Adheres to IEEE standards for image processing and machine learning applications
Built on established open-source computer vision libraries and frameworks
Learn more about the research:CarDD Dataset Paper
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