After a buyer uploads photos of damaged gloves, the system identifies the damage pattern, estimates the likely cause, recommends an after-sales action, and routes low-confidence or disputed cases to human support.
On mobile, buyers can open the camera directly. On desktop, they can choose a local image. This standalone demo uses local logic to simulate AI assessment.
Your photo preview will appear here.
The system combines image signals, order batch data, buyer descriptions, and after-sales rules into one actionable decision.
After uploading a photo and running AI assessment, this area will show the likely cause, responsibility signal, and recommended handling action.
There is not enough information yet to determine the risk tier.
Waiting for buyer upload.
Classify the damage type.
Match the handling plan.
Escalate when needed.
AI-resolvable claims generate a recommended after-sales action immediately. Compensation disputes, insufficient image evidence, or unclear responsibility are routed to the human support queue.
The system will recommend replacement, exchange, refund, usage guidance, or sample reinspection based on the likely damage cause.
Human agents receive the photo, order details, AI assessment, risk tier, and suggested after-sales action so the buyer does not need to repeat everything.
Waiting to generate an AI summary.
If confidence is low, I will confirm photo details, batch records, and the buyer's preferred resolution.
The buyer can add more photo angles or describe the usage environment.
