Where every return becomes insight.
A return is not just a cost. It is data. High return volumes, slow processing and wrong deliveries drain margin, and the answer is to read every claim properly. Agents analyse the patterns, catch the anomalies and score the risk. Named returns specialists decide the cases above the line. What comes back teaches product, logistics and experience teams what to fix next.
Every return tells a story. Not every story is true.
We verify the product, the reason and the eligibility through automated workflows before anyone touches a refund. Suspicious behaviour is caught early, so revenue and reputation are protected. Then we learn from what came back, closing a feedback loop that turns pain points into insight for product, logistics and experience teams.
- Verify before you refundProduct, stated reason and policy eligibility are checked against the order record automatically, so clean returns move fast and doubtful ones do not move at all until a person looks.
- Catch the pattern earlySerial returners, wardrobing and swapped items show up as behaviour, not as one claim. The signal reaches a specialist while it is still cheap to act on.
- Learn from every returnReasons, images and outcomes feed back to the teams who can remove the cause: a size chart, a packaging spec, a carrier, a product page.
Six capabilities, one returns record.
Each capability reads on its own and writes only through the approval gate. The thresholds that separate an automatic approval from a manual review are set by your people, not by the model.
Image-based validation
Return photos are compared with the product record and the original listing images to confirm the item is the one that was bought, in the condition claimed, for the reason stated.
Reason mapping
Every return is categorised by product type, damage type and customer reason on one taxonomy, so the patterns that hurt sales and satisfaction stop hiding inside free-text fields.
Risk-scoring engine
Each claim receives a fraud probability score. Cases inside a band your team has set are approved automatically. Cases above it are queued for manual review by a named specialist, highest risk first.
Pattern and behaviour analysis
Unusual customer behaviour and the products that attract return abuse are surfaced with the evidence behind them, giving you the grounds to redesign a policy rather than guess at one.
Customer feedback capture
Customer explanations are organised and grouped so recurring issues such as sizing, quality or delivery delays are visible as a trend, with the orders behind each one.
Smart restock and insight reporting
Validated returns are fast-tracked back to inventory through your existing systems, and the trends are reported to the people who improve product pages, packaging and return policies.
Let’s build smarter, faster and more scalable e-commerce experiences together.
Explore how AI + creativity can accelerate your next big move. Bring one month of returns. Leave with a risk-scored view of every claim, the patterns behind them and a record of who decided what.