IVInspectionCase 03

Cracks, spalling, and paint failure — spotted by a model, not a person on a ladder.

The problem

A client was inspecting structures by eye from photos — slow, inconsistent, and easy to miss a defect.

Our approach

The client sends us imagery; our computer-vision model scores each image for cracks, paint fade-off, corrosion, and other defects, then outputs a structured report with locations and severity.

User journey
Image upload
CV model
Detect cracks / paint / corrosion
Severity report
System architecture
Azure · AKS + GPU pool
Ingress
Blob uploaddrone imagery
Event Grid
new capture
Queue
Service Busbatch
KEDA autoscalerscale on depth
back-pressure
Inference
AKS GPU poolspot + on-demand
Triton · CV modelcracks / spall / rust
score every region
Post
Defect scorer
Confidence thresholdroute low-conf → human
Data
Cosmos DB
Blob · annotated
Deliver
SignalR → live UI
PDF report
Scales viaGPU batch inferenceimage queue + retriesmodel versioningconfidence thresholds
Computer visionImage modelsDefect scoring
Automatic defect detectionConsistent scoringReport in minutes

Representative build — real project type and architecture, anonymised client, illustrative figures.

Want this for your stack?

I'll map your problem to an architecture like this — and prove it with an MVP before you pay.

Start a build →

Next: Finance opsMark a row paid or unpaid