ENERGY & INFRASTRUCTURE
Predictive Maintenance
Predictive Maintenance. What it takes to run it.
Track rust progression and wear over time to predict failure.
// 01 · The question
“Is this asset degrading faster than the one next to it?”
// 02 · Why it is hard
Comparing this quarter's frame with last quarter's requires the two to be comparable, which imagery from two separate flights rarely is until it has been registered to the asset.
// 03 · What the labels have to be
Instance segmentation
Decided before the first frame
The prediction is a rate, and a rate is the same quantity measured twice. Corroded area is that quantity. A box cannot supply it: the box grows when the aircraft flies closer, and the rust does not.
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
Pixel-Perfect Data
Annotate power line components, insulators, vegetation encroachment, corrosion, and structural defects with pixel-level precision. Expert-verified for safety-critical accuracy.
LexInsight
Asset Intelligence
Transform isolated defect labels into a queryable asset intelligence graph. Link visual anomalies to asset IDs, maintenance schedules, and SCADA data. Track defect progression over time - not just point-in-time snapshots.
LexAlert
Drift Alerts
Monitor deployed models against seasonal drift, weather changes, and equipment aging. Auto-retrain when accuracy drops, so detection holds as field conditions change.
// 05 · What breaks it after launch
Swap the payload and every historical measurement is in different units. Progression tracking is the one job where a sharper camera can make the trend line worse, because the step change reads as degradation.
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 energy & utilities
Grid Safety & Defect Identification
The Challenge
Severe risk of model degradation caused by rapidly shifting weather patterns, dynamic equipment placement, and evolving site layouts across remote grids.
Our Execution
Engineered safety-critical visual datasets and architected a continuous data ingestion pipeline to instantly process new field imagery as configurations evolved.
The Result
Achieved 21,000+ hazard detections per month, drastically improving frontline worker safety and unlocking frictionless scalability across remote sites.
21,000+
Hazard detections per month