INSURANCE
Catastrophe Response
Catastrophe Response. What it takes to run it.
Rapid visual triage of storm damage across wide geographic areas.
// 01 · The question
“Which of these properties needs an adjuster on the ground first?”
// 02 · Why it is hard
It is a surge event. The volume arrives in days, the imagery is whatever could be flown, and the answer is worth far less a week later than it is on day two.
// 03 · What the labels have to be
Instance segmentation
Decided before the first frame
Triage is graded by how much of a structure is affected. Percentage of roof missing separates a tarp from a total loss, and percentage is an area, so the label has to carry area.
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
Every catastrophe looks different: hail, wind, flood and fire damage share almost no visual features. A model built on one event type is being asked to generalise to a peril it has never seen.
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