INSURANCE
Workers' Compensation
Workers' Compensation. What it takes to run it.
Visual verification of workplace incidents and recovery status.
// 01 · The question
“What does the footage show about how this happened?”
// 02 · Why it is hard
It is retrospective and adversarial. The footage was never captured for this purpose, the relevant seconds have to be found in hours of it, and the conclusion has to hold up to challenge.
// 03 · What the labels have to be
Bounding box + keypoints
Decided before the first frame
The question is about a body doing something: a lift, a fall, a posture held too long. Keypoints turn that into joint positions over time, which is the only representation a box cannot approximate.
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 workplace is its own domain. A model built on warehouse camera angles meets a workshop with different heights, lighting and layout, and the posture baseline it learned does not carry over.
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