AI System for Checking Delivery Evidence

The problem

In day-to-day operations, missing evidence may go unnoticed on a delivery marked as complete. When delivery orders, times, locations, photographs, signatures and recipient fields are scattered across sensors, documents, field records or team files, issues may not become visible in time.

This fragmented data delays preparation of the delivery-evidence discrepancy report and forces teams to search manually for supporting evidence again. The delivery operations lead must complete missing records and verify each conflict before making a decision.

A common exception is that contactless delivery or delivery to a secure location may use different evidence. Incomplete or incorrect automation that fails to check this context could reject a valid delivery or close a delivery with insufficient evidence, so no physical or operational action can be carried out directly.

What we set out to improve

• Number of verified cases of missing evidence
• Proportion of outputs corrected or rejected by the delivery operations lead
• Time between the source event and the review output

How the system works

1. 01 Define the review scope and the role of the authorised delivery operations lead
2. 02 Retrieve the inputs: delivery orders, times, locations, photographs, signatures and recipient fields
3. 03 Check source identity, date, calibration or version, and data recency
4. 04 Match each delivery order to its evidence
5. 05 Check times, locations and required fields
6. 06 Check for exceptions: contactless delivery or delivery to a secure location may use different evidence
7. 07 Place low-confidence or conflicting records in a separate specialist queue
8. 08 Write the source identity, rule, output and timestamp to the audit trail
9. 09 Have the delivery operations lead approve, correct, defer or reject the output

Methods we used

• Document and location consistency checks
• Source, format, timing and required-field checks for delivery orders, times, locations, photographs, signatures and recipient fields
• A missing-data warning instead of an estimate when inputs are incomplete or conflicting
• A separate exception check outside the main rule
• Combined retention of the source, rule result, delivery-evidence discrepancy report and human decision

Where people stay involved

Data and security

Access to delivery orders, times, locations, photographs, signatures and recipient fields is restricted to the minimum permissions required for the task. Commercial, location and employee information is masked where possible, and only the necessary portion of the dataset is sent to the model. Access, outputs and the delivery operations lead's decision are logged; production use does not begin until the organisation has approved the retention period and data location.

Who this suits

Frequently asked questions