Returns management for e-commerce: read the reason, work the exception, stop the next one.

Everything that happens between “I’m sending it back” and the decision that follows — the label and the refund, yes, but also the reason it came back, the follow-up the client gets, and what you change so the next one doesn’t happen. Most tooling covers the first part. Our AI covers the rest: reading the reasons at scale, applying your disposition rules, and feeding the signal upstream.

By moving the decision upstream. Most ecommerce returns at a fashion retailer trace to size and fit — so the AI reads kept-size history, the most consistently purchased and kept size per client, and the size-scale quirks per designer, then puts that guidance in front of the buyer before the box ships. Prevention beats a smoother refund.

It sorts the queue: RMAs, return labels, refund routing, status updates. It stops at the judgment — which client earns a personal follow-up inside 48 hours, whether to offer the next size down or a similar piece in her palette, which carts over $10,000 get worked first. We encode that judgment as rules the AI applies; the software stays the plumbing.

A fit finder asks the shopper questions and guesses a size; ours doesn’t guess — it reads what the client already kept. When your designers run a UK 10, a French 34, and an Italian 38 for the same fit, the AI maps each client’s kept-size history onto each designer’s real sizing, so the recommendation is evidence, not a quiz. The return never happens because the wrong size never ships.

The ones where the relationship has the most at stake. The team’s rule: prioritization goes to carts over $10,000, with average unit retail thresholds and fine-jewelry priority behind it. The AI scores the queue against those rules every morning, so a high-value return never waits in line behind a routine refund.

Wherever they already live — your returns platform’s reason codes and notes, order and kept-item history, support threads, and stylist feedback. Nothing moves to a new system of record; the AI reads across what you have and writes its output where your team already works.