AI System for Checking Food Production Cleaning Records

The problem

In day-to-day operations, a missing verification record may not be identified before production begins. When the cleaning plan, completion form, line, shift, verification and signature data are scattered across sensors, documents, field records or team systems, issues do not become visible in time.

This fragmented data delays preparation of the missing cleaning records list and forces teams to search for supporting evidence again by hand. Before making a decision, the food engineer or hygiene manager must complete missing records and verify each conflict individually.

A common exception is that a different cleaning form may be used after planned maintenance. Without this context check, incomplete or incorrect automation could allow a line to open in error or keep it closed unnecessarily, so no physical or operational action can be applied directly.

What we set out to improve

• Number of verified missing fields in cleaning records
• Proportion of outputs corrected or rejected by the food engineer or hygiene 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 or hygiene manager role
2. 02 Retrieve inputs: cleaning plan, completion form, line, shift, verification and signature data
3. 03 Check source identity, date, calibration or version, and data recency
4. 04 Match the plan with the completion record
5. 05 Check verification, signature and timing
6. 06 Check for an exception: a different cleaning form may be used after planned maintenance
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 or hygiene manager approve, correct, defer or reject the output

Methods we used

• Form-schema and shift-level status checks
• Source, format, timing and required-field checks for the cleaning plan, completion form, line, shift, verification and signature data
• 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, missing cleaning records list and human decision

Where people stay involved

Data and security

Access to the cleaning plan, completion form, line, shift, verification and signature data 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 or hygiene 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