TREN

E-commerce · Scope to be defined during discovery

AI System for Reviewing Return Images with the Stated Reason

We design an AI system that reviews the return form, order and customer images together with source and freshness information. The system prepares a review category and a list of missing evidence but does not treat them as final decisions. If damage cannot be verified from the image, the suggestion is not put into effect; the returns specialist makes the final assessment.

Representative returns operations panel with a work queue, checks and an audit trail: return image evidence queue — **Return image evidence queue** Representative interface — contains no real data. Names of people and products are masked. The panel does not decide return acceptance, refunds or replacements.

The problem

In day-to-day operations, return forms, orders and customer images arrive in separate records and files, preventing the team from reviewing the same case in one place.

As a result, the link to the source can be lost while the review category and missing-evidence list are prepared, leading to repeated checks and uncertainty about the basis for the decision.

Incomplete automation may overlook cases where damage cannot be verified from the image, while poorly controlled automation creates a risk of an unfair rejection or incorrect inventory action.

What we set out to improve

Time taken for the draft review category and missing-evidence list to be ready for review
Accurate routing of records where damage cannot be verified from the image to the correct specialist queue
Recording of suggestions amended or rejected by the returns specialist

How the system works

01 Retrieve return-form, order and customer-image data from an approved source
02 Verify the source system, record identifier and freshness information
03 Transform the fields into the target process schema
04 Prepare a draft review category and missing-evidence list
05 Compare the draft with business rules and source records
06 Exception check: damage cannot be verified from the image
07 Returns specialist approval, amendment or rejection
08 Write the input, suggestion, amendment and final decision to the audit log

Methods we used

Source-traceable data extraction for the return form, order and customer images
Structured output that restricts the review-category and missing-evidence fields through business rules
An exception gate that separates cases where damage cannot be verified from the image
A queue that shows the source and suggestion together for the returns specialist
Duplicate-processing and rollback records to manage the risk of an unfair rejection or incorrect inventory action

Where people stay involved

The returns specialist is the final decision-maker. If damage cannot be verified from the image, the workflow stops and the case is presented to the specialist with the supporting records. The specialist can amend or reject the suggestion, or stop the process.

Data and security

Only the fields required for the task are processed from the return form, order and customer images. Source-system permissions are preserved, and personal and commercially sensitive data is minimised before being passed to the model. Every read, suggestion, human amendment and write to the target system is logged.

Who this suits

A good fit

Teams carrying out repetitive reviews in e-commerce
Companies with defined ownership for sources and approvals
Organisations that want exceptions to remain under human oversight

Not a good fit

When source data is not current
When no decision owner or rollback process has been defined
When the existing method is sufficient for the low volume involved

Frequently asked questions

What data is used?

The return form, order and customer images are used. The exact connections, fields and retention limits are defined during the access review.

How are exceptions handled?

If damage cannot be verified from the image, the workflow stops. The record enters the returns specialist's queue with source evidence.

How is poorly controlled automation limited?

Because an unfair rejection or incorrect inventory action could occur, no final action is taken without human approval. The source, suggestion and human decision are recorded separately.

Project led by:DijitalPi

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