TREN

Fashion e-commerce · Scope to be defined during discovery

AI System That Presents Virtual Try-On Outputs with Clear Limits

We design an AI system that reviews an authorised user image, product image and size chart together with source and freshness information. The system prepares an illustrative appearance preview but does not treat it as a final decision. If the image or product geometry is unsupported, the suggestion is not put into effect; the product experience lead makes the final assessment.

Representative fashion product experience panel with a work queue, checks and an audit trail: virtual try-on eligibility queue — **Virtual try-on eligibility queue** Representative interface — contains no real data. User image is authorised and masked. The panel does not show actual fit, choose a size or produce a purchase outcome.

The problem

In day-to-day operations, authorised user images, product images and size charts 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 an illustrative appearance preview is prepared, leading to repeated checks and uncertainty about the basis for the decision.

Incomplete automation may overlook unsupported images or product geometry, while poorly controlled automation creates a risk that the preview will be mistaken for conclusive evidence of fit.

What we set out to improve

Time taken for the illustrative appearance preview draft to be ready for review
Accurate routing of records with unsupported images or product geometry to the correct specialist queue
Recording of suggestions amended or rejected by the product experience lead

How the system works

01 Retrieve the authorised user image, product image and size chart 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 illustrative appearance preview
05 Compare the draft with business rules and source records
06 Exception check: the image or product geometry is unsupported
07 Product experience lead 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 authorised user image, product image and size chart
Structured output that restricts the illustrative appearance preview fields through business rules
An exception gate that separates cases with unsupported images or product geometry
A queue that shows the source and suggestion together for the product experience lead
Duplicate-processing and rollback records to manage the risk of a preview being mistaken for conclusive evidence of fit

Where people stay involved

The product experience lead is the final decision-maker. If the image or product geometry is unsupported, 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 authorised user image, product image and size chart. 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 fashion 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?

An authorised user image, product image and size chart are used. The exact connections, fields and retention limits are defined during the access review.

How are exceptions handled?

If the image or product geometry is unsupported, the workflow stops. The record enters the product experience lead's queue with source evidence.

How is poorly controlled automation limited?

Because the preview could be mistaken for conclusive evidence of fit, 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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