{"slug":"returns-triage","name":"Returns Triage Console","category":"Operations","customer":"Operations leads at mid-sized online retailers","problem":"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.","value":"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. Address the recurring problem: exceptions sit for days because only a supervisor knows the warranty or restock rules. The value hypothesis is faster triage with less manual opening and retyping; the pilot must establish whether the prediction is accurate enough to save time.","format":"Client intake portal and staff exception queue","screens":"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.","functionality":"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.","workflow":"Customer submits a return request with photos and order number, automation matches the order, pulls the return window and item condition, classifies the outcome, writes a draft reply, staff approves or edits, and the reply and any label send. Start with return requests, photos, order history and customer notes and finish with approved outcome and customer reply.","ai":"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.","inputs":"Return requests, photos, order history and customer notes","deliverables":"Approved outcome with reason and customer reply","admin":"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.","mvp":"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.","expansion":"Automate label generation, escalate edge cases to supervisors, and feed approved decisions back into the classifier to reduce manual edits over time.","usp":"The automation reads photos and order history together and drafts a policy-based reply, so staff approve rather than investigate, and the system learns from every approved edit.","defensibility":"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":"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.","revenue":"Test a fixed fee of USD 1,500 per month per client, with a setup fee of USD 3,000, as a hypothesis.","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.","integrations":"Order management system, returns portal, email or Slack inbox, and refund or label generation service.","dependencies":"Clean order data, clear return rules, and access to return request photos and history.","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.","plan30":"Week 1: Map the returns inbox and policy rules with the client. Week 2: Build the intake queue and order matching. Week 3: Train the classifier on sample photos and draft replies. Week 4: Run a manual review pilot with five staff and measure time saved.","metrics":"Average time from return request to approved decision, and percentage of refunds issued outside the return window.","channels":"Ecommerce operations forums, logistics and fulfillment newsletters, and direct outreach to retailers with public return policies.","leadMagnet":"A free return triage demo using ten anonymised return requests from your own inbox.","message":"Stop opening boxes to confirm what the photo already shows; approve the outcome instead.","retention":"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.","controls":"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.","crossSector":"Customer Support; Insurance","fn":["Match order and pull return window and purchase history","Classify outcome as refund, repair or reject with reason","Draft a customer reply using policy and tone","Queue draft for staff approval or edit","Generate refund or repair label on approval","Log every decision for audit and policy refinement"],"sc":{"opp":7,"pain":8,"feas":9,"now":8},"phases":[{"name":"MVP","scope":"One buyer segment, one recurring use case; first modules: intake queue and decision preview. Manual review in the loop.","time":{"days":2,"label":"2 days"},"usd":5500},{"name":"Paid pilot","scope":"Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.","time":{"days":3,"label":"3 days"},"usd":4500},{"name":"Full product","scope":"Self-serve onboarding, billing, monitoring and the wider integration set.","time":{"days":5,"label":"5 days"},"usd":6500}],"running":[{"stage":"MVP and paid pilot","note":"about 3 customers","hosting":[30,60],"ai":[40,90],"total":[70,150]},{"stage":"Full product","note":"about 50 customers","hosting":[110,210],"ai":[280,560],"total":[390,770]}],"total":16500,"complexity":0.04,"days":10,"shot":true,"demo":true}