INSURANCE
Auto Damage Estimation
Auto Damage Estimation. What it takes to run it.
Instant repair cost estimation from user-submitted photos.
// 01 · The question
“Repair or total, and roughly what does it cost?”
// 02 · Why it is hard
The photographs come from claimants, on phones, in car parks, at whatever angle and in whatever light. There is no capture standard to rely on and no second chance to reshoot.
// 03 · What the labels have to be
Instance segmentation
Decided before the first frame
Estimating runs on panel and severity: which panel, how much of it. The mask supplies the affected area per panel, which is the quantity the estimate is built from, and a box cannot separate a dented door from the shadow beside it.
This is the decision that is expensive to reverse. The geometry has to match what the answer contains, and finding out it does not means labelling the set a second time.
// 04 · How we run it
Three parts of one loop, on this job.
LexAnnotate
Damage Segmentation
Annotate vehicle damage, property defects, and workplace safety conditions with precision bounding boxes and segmentation. Human-in-the-loop verification reduces blind spots in complex edge cases.
LexInsight
Risk Profiling
Link visual damage assessments to claims data, vehicle databases, and property records. Track damage progression over time for workers' comp and commercial property monitoring.
LexAlert
Accuracy Alerts
Monitor deployed claims models as vehicle fleets evolve. Detect accuracy drift when new vehicle types, lighting conditions, or inspection regions change. Auto-retrain before the drift reaches the claims desk.
// 05 · What breaks it after launch
Move the repair-versus-replace threshold or change the parts schedule and the correct answer moves while the photographs stay identical. The model keeps applying last year's rule with full confidence.
It is not the only one that can get this use case, it is the one that usually gets it first. All ten conditions.
// 06 · In insurance
Claims Automation Stability
The Challenge
Damage detection models struggled as vehicle types, lighting, and edge cases increased. Inconsistent visuals caused false positives.
Our Execution
Created production-ready visual datasets. Applied human-in-the-loop validation to reduce blind spots.
The Result
Stabilized accuracy across varied real-world conditions. Reduced manual claim reviews.
6,000+
Cases resolved per month