AI System for Comparing Food Labels with Approved Product Data

The problem

In day-to-day operations, an outdated label version may be sent to production. When the label draft, approved product specification, allergen matrix and version record are scattered across sensors, documents, field records or team systems, issues do not become visible in time.

This fragmented data delays preparation of the label consistency report and forces teams to search for supporting evidence again by hand. Before making a decision, the food engineer and regulatory affairs manager must complete missing records and verify each conflict individually.

A common exception is that the market or pack size may require a different label version. Without this context check, incomplete or incorrect automation could cause the wrong label to be printed or reject a valid local version, so no physical or operational action can be applied directly.

What we set out to improve

• Number of label-to-source discrepancies verified by the specialist
• Proportion of outputs corrected or rejected by the food engineer and regulatory affairs manager
• Time between the source event and the inspection output

How the system works

1. 01 Define the review scope and the authorised food engineer and regulatory affairs manager roles
2. 02 Retrieve inputs: label draft, approved product specification, allergen matrix and version record
3. 03 Check source identity, date, calibration or version, and data recency
4. 04 Extract the label fields
5. 05 Compare the fields with the specification and version
6. 06 Check for an exception: the market or pack size may require a different label version
7. 07 Route low-confidence or conflicting records to a separate specialist queue
8. 08 Write the source identity, rule, output and timestamp to the audit trail
9. 09 Have the food engineer and regulatory affairs manager approve, correct, defer or reject the output

Methods we used

• Source-field and version matching without interpreting regulations
• Source, format, timing and required-field checks for the label draft, approved product specification, allergen matrix and version record
• A missing-data warning instead of an estimate when inputs are incomplete or conflicting
• Separate evaluation of exceptions and the main rule
• Combined retention of the source, rule result, label consistency report and human decision

Where people stay involved

Data and security

Access to the label draft, approved product specification, allergen matrix and version record is limited to the minimum permissions required for the task. Commercial, location and employee information is masked where possible, and only the necessary subset of the raw dataset is passed to the model. Access, outputs and the food engineer's and regulatory affairs manager's decisions are logged; the system is not released to production until the organisation approves the retention period and data location.

Who this suits

Frequently asked questions