Returns Triage Console

For

Operations leads at mid-sized online retailers

Solves

A returns inbox fills with blurry photos, missing labels and long threads, so staff open every box just to confirm what the photo already shows, then retype the same reply.

Delivers

Approved outcome with reason and customer reply

Built in

10 days of creation time, MVP in 2 days

Investment

$5,500 for the MVP, $16,500 for the full product

Run it

Inside your business, or as part of your offer to clients

For operations leads at mid-sized online retailers, turn return requests, photos, order history and customer notes into a predicted outcome with a draft reply queued for staff approval.

1. Match order and pull return window and purchase history.
2. Classify outcome as refund, repair or reject with reason.
3. Draft a customer reply using policy and tone.
4. Queue draft for staff approval or edit.
5. Generate refund or repair label on approval.
6. Log every decision for audit and policy refinement.

What goes in, what comes out

The workflow

1. In
   Start with

   Return requests, photos, order history and customer notes

2. 1

   Customer submits a return request with photos and order number

3. 2

   Automation matches the order

4. 3

   Pulls the return window and item condition

5. 4

   Classifies the outcome

6. 5

   Writes a draft reply

7. 6

   Staff approves or edits

8. 7

   The reply and any label send

9. Out
   Finish with

   Approved outcome with reason and customer reply

AI does the heavy lifting, people stay in charge

Use vision models to read photos and language models to draft replies and classify outcomes. Keep policy rules in structured fields. Validate order matches and return windows through deterministic checks. A staff member approves or edits before anything is sent to the customer.

What your team sees

Key screens: Intake queue, decision preview, approval panel. Use a list of return requests needing a decision, each showing the predicted outcome, the reason and a draft reply. Let staff open a detail view with photos, order history and policy notes. Display approve, edit or escalate actions. Show a status filter for pending, approved and rejected. In this product, the first view is intake queue, followed by decision preview and approval panel.

Accounts and administration

Staff roles, approval permissions, decision versions, audit trail of every classification and edit, policy version history and a log of what was sent to the customer.

Integrations and data access

Order management system, returns portal, email or Slack inbox, and refund or label generation service.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   2 days

   One buyer segment, one recurring use case; first modules: intake queue and decision preview. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   3 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   5 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will operations leads at mid-sized online retailers use it to solve "A returns inbox fills with blurry photos, missing labels and long threads, so staff open every box just to confirm what the photo already shows, then retype the same reply"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. A paid pilot with one client proves it by measuring average time to decision and refund leakage before and after, with a decision to continue if time per decision drops by at least 30 percent.
4. Measure, then decide. Track average time from return request to approved decision and and percentage of refunds issued outside the return window. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One buyer: operations leads at mid-sized online retailers. One use case: simple returns with clear photos and order history. First two modules: intake queue and decision preview. Manual review of every draft before send.

After the MVP. Automate label generation, escalate edge cases to supervisors, and feed approved decisions back into the classifier to reduce manual edits over time.

What the build depends on. Clean order data, clear return rules, and access to return request photos and history.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a fixed fee of USD 1,500 per month per client, with a setup fee of USD 3,000, as a hypothesis.

Message to test

Stop opening boxes to confirm what the photo already shows; approve the outcome instead.

Where to find buyers

Ecommerce operations forums, logistics and fulfillment newsletters, and direct outreach to retailers with public return policies.

Lead magnet

A free return triage demo using ten anonymised return requests from your own inbox.

The first 30 days

1. Week 1: Map the returns inbox and policy rules with the client.
2. Week 2: Build the intake queue and order matching.
3. Week 3: Train the classifier on sample photos and draft replies.
4. Week 4: Run a manual review pilot with five staff and measure time saved.

Paid pilot

A paid pilot with one client proves it by measuring average time to decision and refund leakage before and after, with a decision to continue if time per decision drops by at least 30 percent.

Success metrics

Average time from return request to approved decision, and percentage of refunds issued outside the return window.

Retention and expansion

It keeps earning by reducing manual review effort and refund leakage, and the classifier improves with each approved decision, making the service more valuable over time.

Why clients would pick it

The more decisions staff approve or edit, the better the classifier matches your specific policy and tone, and the harder it becomes to switch to a generic tool.

Alternatives and positioning

People use shared inboxes, spreadsheets and manual policy checks. This differs by predicting the outcome before a human opens the box and drafting the reply in the same step.

Main delivery costs

Main delivery costs are integration with order and returns systems, vision model usage, and staff time for policy setup and review during the pilot.

Staff must approve every reply before send. The system must not auto-approve refunds or rejections. It logs all decisions and edits, and flags out-of-policy cases for supervisor review.