Returns are a margin problem disguised as a support problem

Returns are usually treated as a customer support problem. They are also a margin problem.

Every manual review, duplicate refund, policy exception, unclear product photo, and delayed resolution adds cost to the business.

Questions a good returns workflow should answer

• What is being returned?
• Why is it being returned?
• Does the photo match the reason?
• What does policy allow?
• Should this be auto-approved, rejected, or routed to a human?

This is a strong fit for applied AI because the task is bounded, the decision criteria can be written down, and the outcomes can be measured.

What we're optimizing for

The goal is not to remove humans from every case. The goal is to reserve human time for the cases that actually need judgment.

• Faster resolution
• Lower leakage
• Cleaner customer experience