AI for returns and claims: from evidence to a reviewable recommendation

Start with a case file that shows what is known, what is missing and what should happen next. Customers should not have to explain the same situation every time the case changes hands. Staff need the evidence and reasoning together, not another summary disconnected from the order.

Consider an illustrative customer who reports that a portable speaker will not turn on and sends a photo plus an order number. The representative needs the correct item, model, purchase record, delivery status and troubleshooting already attempted before choosing the next step.

An agent can organise the customer's wording into fields such as reported symptoms, attachments and unanswered questions. Preserve the original message. Turning a statement that the speaker was charged but did not start into a diagnosis of a failed battery goes beyond the evidence.

Check for an existing case using the order identifier and verified customer identity before creating another. If the customer contacts both chat and email, let staff confirm whether the records belong together. Similar names or wording are insufficient: one customer may have different problems with different items.

A useful case file contains four distinct sections:

• Verified purchase information: items, quantities and delivery history read from the order system, with a retrieval time.
• Customer-reported information: symptoms, images, videos and attempted steps, without converting them into an unsupported technical diagnosis.
• Missing or conflicting evidence: an unreadable model label, an unmatched order number or documents containing different dates.
• A recommendation for review: request specific evidence, involve a specialist or follow an applicable policy, with reasons and required approvals.

Keep returns, exchanges and refunds distinct. Shopify's returns and exchanges documentation treats them as different actions. Accepting a case into the process should not imply that repayment has already been approved.

Customer-facing language must reflect the actual state, such as receiving the information and checking the purchase record. Staff can see more detailed internal states. This avoids making a commitment before the authorised person decides.

Image-capable AI may help identify whether a photo shows the whole item, whether a label is legible and which additional view would be useful. A single picture may not establish the cause or timing of damage, or how an internal component behaves.

Anthropic's vision documentation describes interpretation errors with poor-quality images and warns against relying on the model to detect AI-generated images. Use the feature to check evidence completeness rather than accuse a customer of submitting a fake photograph.

Make requests concrete: the whole product, its model label or the reported problem area, where necessary for that case. Collect information relevant to the assessment. Ask customers to keep personal documents and payment-card details outside the image.

When the evidence is insufficient, explain what is missing. Repeatedly requesting another photo without a reason is poor service. Where physical inspection is necessary, preserve that pending state instead of presenting a model's interpretation as proof that the customer breached a condition.

Policies can vary by item, channel, promotion and purchase period. Before an agent proposes an outcome, a responsible business owner must identify the applicable policy sources and situations that require further review.

Retain the policy version and supporting passage in the case. General web search should not replace the business's approved rules, and a policy-looking document supplied by the customer should not automatically become an internal authority. Market-specific requirements and ambiguous rules need review by the responsible team before they become automated decisions.

A valid recommendation might state that the purchase record is complete but the item needs inspection before a remedy can be selected. A pending case is not necessarily an AI failure. Producing a final answer in every situation is the wrong objective.

Recommending a refund and executing one are separate steps. Show the amount, item, reason and supporting evidence to the approver. The destination system must enforce permissions, applicable limits and the current order state, even when the action originates from an AI-prepared screen.

OWASP's Excessive Agency guidance addresses unnecessary tools and authority. The initial agent might therefore read order information and draft responses, while status changes and refunds remain separately controlled actions.

If a response is lost after an action was sent, check the destination before repeating it. Use an operation identifier to prevent duplicates. Distinguish checking an uncertain result from an action that has not started, so a representative does not retry simply because the interface looks stuck.

The audit trail should record both the recommendation and the human decision. If the representative chooses a different outcome, retain the reason instead of rewriting the original suggestion. That record helps identify whether the system lacked information, selected the wrong policy or interpreted the case poorly.

Use authorised examples covering complete evidence, unreadable photos, multiple order items, reopened cases and conflicting policy information. Check whether the system matches the right item, asks only necessary questions and stops when it lacks permission or sufficient evidence.

Measure the time from intake to a case that a representative can decide, including repeated questions and incorrectly classified cases. A high number of AI replies does not establish useful work. If staff must reconstruct every case afterwards, the complete process may not have improved.

Begin with a product group and policy the team understands well. Expand after the review screen and handover work in practice. The guides to after-sales service tracking and agent permissions and approvals explain related parts of this workflow.

Talvyn Labs builds claims-intake tools that connect orders, organise evidence and prepare recommendations for review. Customer-facing states and traceable approvals belong in the same product scope. Bring sample cases and the policies your team uses to define a system that reduces information chasing while keeping decisions accountable.