- Checks the order against return policy
- Requests missing photos or order details
- Prepares approval or rejection notes
- Creates return labels when approved
- Updates the ticket and order record
Work we can take on
Turn return requests into fast policy checks without losing control of refunds.
CapacityBench helps commerce and operations teams sort return requests, collect evidence, prepare decisions, and keep expensive exceptions with the right person.
Workflow pilots start from $750/month. Prove one recurring job, then move into a managed AI employee from $1,500/month.
What changes
The routine work gets handled. Your team gets the clean handoff.
This page is the simple version: what comes off the team's plate, what they get back, and where people still make the calls that matter.
- RMA label
- Refund instruction
- Replacement order
- Warehouse note
- High-value refunds
- Fraud flags
- Policy exceptions
- Chargebacks
- Angry customer cases
- The return policy is not written down
- Most refunds require relationship judgment
- Order and ticket data cannot be accessed
- No owner can approve exceptions
Why it is safe to start small
Start with the repeatable work. Keep judgment with people.
The first version is deliberately narrow. It clears routine follow-up, prepares clean summaries, and brings the judgment calls back to the right person.
Return decision timePhotos or evidence collectedException rateTickets waiting on policy review
First 30 days
A practical start, not a transformation project.
We start with the smallest useful version, prove whether it moves the work, and only expand when the first role is earning its keep.
Define the return lanes
Map policy rules, evidence requirements, refund thresholds, warehouse handoffs and who approves exceptions.
Start with reviewed decisions
Classify return requests, gather missing evidence and draft policy-based outcomes for approval.
Move routine returns faster
Let low-risk eligible returns progress while exceptions route to the right owner with context.
Measure queue relief
Review decision time, unresolved return tickets, exception quality and avoided manual checks.
Example result
Return request checked: Order #W88214, size mismatch, 8 days after delivery, tag attached. Eligible under policy. Label prepared. Refund waits for warehouse scan-in. Exception: none.
Faster return decisions and fewer tickets waiting on policy checks.
The pilot is judged against movement in this metric, not against whether the AI looks impressive.
People still approve the calls that matter.
- High-value refunds
- Fraud flags
- Policy exceptions
- Chargebacks
- Decision time per return
- Tickets cleared from policy queue
- Evidence collected before review
- Exception handoffs arrive with clean context
- Support inbox triage
- Order status updates
- Review and reputation
- Weekly support themes
Check this workflow
Send the rough version. We will tell you if this is the right first AI employee.
Use this when the work shows up every week, has a clear owner, and is costing the team time.