800 Returns a Week.
Handled in 6 Hours, Not 4 Days.
Returns, catalogue work, and customer queries grow with your revenue. Scale them with headcount and growth eats your margin. We automate the volume — with consistent decisions your policy actually intended.
as share of total sales
(NRF 2024 data)
on our e-commerce project
returns processing project
Growth That Makes Operations Worse, Not Better
E-commerce volume amplifies. When operations are manual, more orders means more returns, more queries, and more inconsistencies — at scale.
At a 15% return rate, a €10M business handles 1,500+ returns per €1M in sales. Assessing condition, applying policy, and issuing refunds by hand is a department that grows with revenue.
The policy says 30 days, full refund on unused items — but the edge cases (worn once, missing tag) get decided by whoever's on the queue that day. Same situation, different answers.
67% of shoppers check the returns policy before buying (NRF). A slow resolution erodes trust, even when it's correct. The customer who waited 4 days for a refund doesn't come back.
Manual review can't spot patterns across thousands of transactions. The customer with 7 returns in 60 days doesn't stand out in a queue. Automated detection flags it before the refund goes out.
How the Returns System Works
800 Returns a Week — Without 4 Full-Time Staff
An online fashion retailer handling 800+ returns a week with four full-time staff, a 4-day resolution time, inconsistent policy calls, and no view of fraud. We built a returns system that checks item condition from photos, applies policy automatically, flags fraud, and sends only the genuine edge cases to a person. One person now does what four used to.
3 FTEs reallocated (€38K × 3 = €114K) + estimated fraud/chargeback reduction (€41K). Payback: ~10 weeks.
See this and 8 more case studies →Consistent Decisions, Faster Resolutions, No Extra Headcount
The goal isn't removing humans from hard decisions. It's removing them from the 90% of decisions that aren't actually hard.
It reads the customer's photos and judges condition — unused, worn, damaged, missing tags — against your policy. Every decision is logged with the image and the rule applied.
Your policy becomes rules you can change yourself — no developer needed. Refund, exchange, store credit, or reject, applied the same way on every return.
It flags the patterns a queue hides — frequent returners, inconsistent claims, mismatched details — before the refund runs, then sends them to a person with full context.
Genuine edge cases — unusual damage, disputes, policy gaps — go to one reviewer with everything attached: images, history, and policy reasoning. People handle only what the system couldn't.
What Runs the Returns System
How many returns are you processing a week?
Share your returns volume, your team, and your main SLA challenge. We'll map what's automatable, what needs a human, and what payback looks like.
Estimate my returns savings