AI System for Structuring Vehicle Service Intake Images

The problem

Authorised vehicle photographs, service forms and vehicle records arrive from different systems and event logs, preventing the team from viewing each case on a single timeline.

As a result, source links may be lost while the image-supported intake draft is prepared, the case may be referred to the wrong team and the basis for a technical decision may remain unclear.

An incomplete workflow may overlook unreadable chassis information or unclear damage, while an unsuitable workflow could record existing damage incorrectly.

What we set out to improve

• Time taken for the image-supported intake draft to be ready for specialist review
• Correct referral of records with unreadable chassis information or unclear damage to the appropriate specialist queue
• Record of suggestions corrected or rejected by the service adviser

How the system works

1. 01 Retrieve authorised vehicle photograph, service form and vehicle record data from authorised sources
2. 02 Verify the source, timestamp and record identifier
3. 03 Add the context of the relevant asset, user, store, vehicle or content
4. 04 Prepare an image-supported intake draft
5. 05 Compare the draft with technical rules and authority limits
6. 06 Check for exceptions: unreadable chassis information or unclear damage
7. 07 Obtain the service adviser's approval, correction or rejection
8. 08 Write the input, suggestion, change and final decision to the audit log

Methods we used

• Source- and time-traceable data extraction for authorised vehicle photographs, service forms and vehicle records
• Structured output that constrains image-supported intake fields according to technical rules
• An exception gate that separates unreadable chassis information or unclear damage from workflow processing
• An authorised queue that presents evidence and suggestions together for the service adviser
• Duplicate transaction checks and a rollback record to address the risk of recording existing damage incorrectly

Where people stay involved

Data and security

Only fields required for the task are processed from authorised vehicle photographs, service forms and vehicle records. Source-system permissions are preserved, and personal, technical and security-sensitive data is minimised before being provided to the model. Every read, suggestion, human change and target-system action is written to the audit log.

Who this suits

Frequently asked questions