TREN

Automotive · Scope and volume to be determined during discovery

AI System for Preparing Vehicle Inspection Documents for Review

We design an AI system that reviews vehicle images, registration document fields and inspection notes with their source and time information. The system prepares a draft of missing documents and findings but does not carry out technical, security or access decisions. If an image is inadequate or a technical finding conflicts with another record, the system does not finalise the outcome; the authorised vehicle inspection specialist evaluates the evidence.

Representative vehicle assessments panel with a work queue, checks and an audit trail: assessment document preparation queue — **Assessment document preparation queue** Representative interface — contains no real data. Owner and registration details are masked. The panel does not decide technical suitability or value.

The problem

Vehicle images, registration document fields and inspection notes 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 draft of missing documents and findings 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 an inadequate image or conflicting technical finding, while an unsuitable workflow could cause an AI summary to be treated as a technical vehicle inspection.

What we set out to improve

Time taken for the draft of missing documents and findings to be ready for specialist review
Correct referral of records involving an inadequate image or conflicting technical finding to the appropriate specialist queue
Record of suggestions corrected or rejected by the authorised vehicle inspection specialist

How the system works

01 Retrieve vehicle image, registration document field and inspection note 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 a draft of missing documents and findings
05 Compare the draft with technical rules and authority limits
06 Check for exceptions: an inadequate image or conflicting technical finding
07 Obtain the authorised vehicle inspection specialist's approval, correction or rejection
08 Write the input, suggestion, change and final decision to the audit log

Methods we used

Source- and time-traceable data extraction for vehicle images, registration document fields and inspection notes
Structured output that constrains missing-document and finding fields according to technical rules
An exception gate that separates an inadequate image or conflicting technical finding from workflow processing
An authorised queue that presents evidence and suggestions together for the authorised vehicle inspection specialist
Duplicate transaction checks and a rollback record to address the risk of treating an AI summary as a technical vehicle inspection

Where people stay involved

The authorised vehicle inspection specialist is the final decision-maker. The workflow stops when an image is inadequate or a technical finding conflicts with another record, and the evidence is moved to the specialist queue. The system does not independently decide to block an action, change access, carry out a technical intervention or take a physical action.

Data and security

Only fields required for the task are processed from vehicle images, registration document fields and inspection notes. 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

A good fit

Automotive teams that carry out recurring reviews
Companies with defined responsibility for sources and approvals
Organisations that want exceptions to remain under human oversight

Not a good fit

Organisations whose source data is not current
Operations without a designated decision-maker and rollback process
Operations where the existing method is sufficient for the low volume

Frequently asked questions

What data is used?

Vehicle images, registration document fields and inspection notes are used. The exact connections, fields and retention limits are determined during the access review.

How are exceptions managed?

The workflow stops when an image is inadequate or a technical finding conflicts with another record. The record and its source evidence are referred to the authorised vehicle inspection specialist's queue.

Does the system act on its own?

No. Because an AI summary could be treated as a technical vehicle inspection, the final technical, security or access decision remains with a person; the suggestion and decision are recorded separately.

Project led by:DijitalPi

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