Solutions / Returns automation

Returns & refund automation

A return request is checked against your policy, approved or declined with a reason, given a label and tracked to the refund — with only the genuine edge cases reaching an operator.

Support Workflow automation E-commerce
10–20 sec
to check a return request
No operator
on the standard path
Edge cases only
escalated to a person
Who it is for

E-commerce support and ops teams with high return volume, where returns quietly consume the same team that is supposed to be answering pre-sales questions.

Short answer

A return request is checked against your policy — window, condition, category, order history — in 10–20 seconds, approved or declined with the reason stated, issued a shipping label, and tracked through to the refund. Only genuine edge cases reach an operator.

The problem

Returns are the least profitable conversation you have, repeated four hundred times a month.

01

Every return is the same four questions

Order number, reason, condition, has the window passed. An operator asks them, waits, checks the system, then types a policy answer that has not changed in two years.

02

Silence turns a return into a complaint

A customer who does not hear back within a day escalates — to a chargeback, a public review, or both. The refund was going to be approved anyway; only the goodwill was lost.

03

Policy is applied differently by different people

One operator makes an exception, another does not, and the customer who was declined finds the one who was not on a forum. Inconsistency costs more than the policy ever would.

How it works

From trigger to result, step by step.

01

Take the request wherever it starts

The returns form, email, chat, marketplace message or a phone follow-up. The order is identified from whatever the customer supplies, including a photographed receipt or a half-remembered order number.

02

Check eligibility against your policy

Return window, product category, condition described, whether it was on sale, prior return history on the account. The rules are yours; the system applies them the same way at 3am as at 3pm.

03

Answer with the reason, not just the verdict

Approved gets a label and next steps. Declined gets the specific rule and the date it applied — which is what stops the follow-up email and the review that comes after it.

04

Run the logistics

Label generated, pickup or drop-off arranged with your carrier, the customer updated at each movement, and the warehouse notified of what is coming back and why.

05

Close the loop to the refund

Once receipt is confirmed, the refund is triggered through your payment provider and the customer is told. Reasons are aggregated by product, because a rising return rate on one SKU is information your merchandising team needs this week.

Before / after

What changes on the ground.

Today, by hand
×Operators ask the same four questions all day
×Customers wait a day and escalate to a chargeback
×Policy applied differently by different people
×Return reasons are never aggregated by product
With the automation running
›A request checked in 10–20 seconds, at any hour
›Approved returns get a label immediately
›Declines quote the specific rule and date
›Return reasons aggregated per SKU for merchandising
What you get

Delivered, not demoed.

›Intake from your form, email, chat and marketplace channels
›Your returns policy as executable rules with consistent application
›Carrier label generation and status updates to the customer
›Refund triggering and per-product return-reason reporting
›Documentation and a handover session — the system is yours
Built with

We build in your stack rather than moving you onto ours. The list below is what this solution most often connects to — other systems are a scoping question, not a blocker.

LLM (replaceable) Shopify WooCommerce Marketplace APIs Carrier APIs Stripe Zendesk PostgreSQL n8n
Time to production4–6 weeks
Build priceFixed quote
First stepFree mini-audit
Honest limits

When this is not the right solution.

·If your returns policy is really a series of case-by-case decisions, there is nothing to automate until it is written down. Writing it is part of the build, and it is usually the harder part.
·Under a few dozen returns a month, a person handles this fine. We will say so rather than build around it.
·High-value or fraud-sensitive categories should keep a human in the loop, and we will design them that way even where you could automate — automated approval is exactly what return fraud looks for.

Questions we get about this one

They reach a person, with the full history and the rule that was applied attached. The automation handles the standard path so your team has time for the conversations that actually need judgement — and an operator can override any decision, with the override logged.

Bring us the process that hurts.

The mini-audit is free: we take your version of this process apart and tell you plainly whether automating it pays. If it does, you get a scope and a fixed price.