AI Vehicle Damage Detection and Repair Estimation

Vehicle damage assessment is a process where speed and accuracy are both non-negotiable. Insurers need fast, consistent, defensible estimates. Fleet operators need scalable inspection workflows that don't depend on adjuster availability. Repair networks need estimates that match real-world labor and parts costs without manual rekeying. Traditional approaches fail on at least one of these dimensions, and often all three.

NeenOpal has built and deployed AI-powered damage detection and repair estimation for automotive and insurance clients. The solution processes vehicle images through a computer vision pipeline trained on real-world damage scenarios, maps identified damage to component-level specifications by make, model, and year, and generates structured repair estimates with line-item breakdowns ready for adjuster review or direct system submission.

What We Deliver • Computer vision models trained to detect and classify damage types including dents, scratches, cracks, structural deformation, and full panel replacement requirements • Component-level damage mapping against vehicle make, model, and year specifications for estimate accuracy • Automated repair estimate generation with line-item breakdown across parts, labor, and finish categories • Confidence scoring and flagging for edge cases requiring human adjuster review before submission • Integration with claims management systems, repair network portals, and customer-facing mobile channels • AWS-native deployment keeping image data and estimates within your security and compliance boundary

Where This Applies • Insurance carriers processing first notice of loss claims and reducing time-to-settlement • Fleet operators managing condition monitoring and damage billing across distributed vehicle assets • Automotive repair networks standardizing estimation quality and turnaround across locations • Rental and leasing companies automating return condition documentation and damage charge workflows

Why NeenOpal • Deployed for automotive clients achieving 50% faster onsite damage assessment compared to manual inspection workflows • Computer vision models tuned for real-world conditions including variable lighting, angle, and image resolution from mobile submissions • Repair estimation logic configurable to client-specific labor rate tables and preferred parts pricing sources • AWS-native architecture ensures all image and estimate data remains within your security perimeter