TREN

Retail · Scope and volume to be defined during discovery

System for Matching Shelf Price Labels with Product Records

We design an AI system that reviews label images, barcodes and current price records together with source and time information. The system prepares a price discrepancy review record but does not implement technical, security or access decisions. If a label is unreadable or a promotion condition is unclear, the system does not finalise the result; the store manager assesses the evidence.

Representative price control ops panel with a work queue, checks and an audit trail: shelf price label variance queue — **Shelf price label variance queue** Representative interface — contains no real data. Names are anonymised. The panel does not make price decisions, print labels or process sales.

The problem

In day-to-day operations, label images, barcodes and current price records arrive through different systems and event logs, preventing the team from viewing the case on a single timeline.

This can break the link to the source while the price discrepancy review record is prepared, route the case to the wrong team and leave the basis for the technical decision unclear.

Incomplete automation may overlook an unreadable label or unclear promotion condition, while incorrect automation risks displaying the wrong price to a customer.

What we set out to improve

Time taken for the draft price discrepancy review record to become ready for specialist review
Referral of records with an unreadable label or unclear promotion condition to the correct specialist queue
Record of suggestions corrected or rejected by the store manager

How the system works

01 Retrieve label image, barcode and current price record data from authorised sources
02 Verify the source, timestamp and record identifier
03 Add the context of the relevant asset, user, store, vehicle or content
04 Prepare the draft price discrepancy review record
05 Compare the draft with technical rules and authority boundaries
06 Exception check: the label is unreadable or a promotion condition is unclear
07 Have the store manager approve, correct or reject the suggestion
08 Write the input, suggestion, change and final decision to the audit trail

Methods we used

Source- and time-referenced data extraction for label images, barcodes and current price records
Structured output that constrains the price discrepancy review record fields using technical rules
An exception gate that removes unreadable labels or unclear promotion conditions from machine-led processing
An authorised queue showing the evidence and suggestion together for the store manager
Duplicate-processing controls and a rollback record to mitigate the risk of displaying the wrong price to a customer

Where people stay involved

The store manager makes the final decision. If a label is unreadable or a promotion condition is unclear, the workflow stops and the evidence is referred to the specialist queue. The system does not make blocking, access-change, technical intervention or physical action decisions by itself.

Data and security

Only fields from label images, barcodes and current price records that are required for the task are processed. Source-system permissions are retained, and sensitive personal, technical and security data are minimised before being sent to the model. Every retrieval, suggestion, human change and target-system action is written to the audit trail.

Who this suits

A good fit

Teams performing repeat reviews in retail
Companies with defined source and approval responsibilities
Organisations that want exceptions to remain under human oversight

Not a good fit

When source data are not current
When there is no decision owner or rollback process
When the existing method can handle the low volume

Frequently asked questions

What data are used?

Label images, barcodes and current price records are used. The exact connections, fields and retention limits are defined during the access review.

How are exceptions handled?

If a label is unreadable or a promotion condition is unclear, the machine-led workflow stops. The record is referred to the store manager's queue with its source evidence.

Does the system take action by itself?

No. Because of the risk of displaying the wrong price to a customer, final technical, security and access decisions remain with a person; the suggestion and decision are recorded separately.

Project led by:DijitalPi

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