INSURANCE
Fleet Condition Monitoring
Fleet Condition Monitoring. What it takes to run it.
Automated inspection for rental and commercial vehicle fleets.
// 01 · The question
“What is new since this vehicle went out?”
// 02 · Why it is hard
It has to be defensible to a customer who disputes it. The comparison is only as good as the consistency of the two captures, which is why this is the use case where capture standardisation matters most.
// 03 · What the labels have to be
Instance segmentation
Decided before the first frame
The output is a difference between two inspections, and a difference between outlines is meaningful where a difference between boxes is not. Chargeable damage is decided by size against a documented threshold.
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
Re-equip the inspection lanes and every before-and-after pair straddles a change in optics and resolution. Damage that was invisible at the old resolution appears as new damage at the new one.
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