AI System for Tagging Media Archives by Scene and Subject

The problem

In day-to-day operations, authorised video, audio, transcripts and the archive glossary arrive through different systems and incident records, preventing the team from viewing the case on a single timeline.

As a result, source links may be lost while time-coded content tags are prepared, the case may be referred to the wrong team, and the basis for a technical decision may be unclear.

An incomplete workflow may overlook an unclear identity or sensitive scene; poorly designed automation creates a risk of content being used inappropriately because of an incorrect tag.

What we set out to improve

• Time taken for the draft time-coded content tags to be ready for specialist review
• Correct referral of records involving an unclear identity or sensitive scene to the appropriate specialist queue
• Recording of suggestions corrected or rejected by the archive editor

How the system works

1. 01 Retrieve authorised video, audio, transcript and archive glossary data from authorised sources
2. 02 Verify the source, timestamp and record identifier
3. 03 Add the relevant asset, user, location, vehicle or content context
4. 04 Prepare the draft time-coded content tags
5. 05 Compare the draft with technical rules and authority limits
6. 06 Check for exceptions: an unclear identity or sensitive scene
7. 07 Have the archive editor approve, correct or reject the draft
8. 08 Write the input, suggestion, changes and final decision to the audit log

Methods we used

• Data extraction with source and timestamp traceability from authorised video, audio, transcripts and the archive glossary
• Structured output that restricts time-coded content tags to fields defined by technical rules
• An exception gate that separates an unclear identity or sensitive scene from system processing
• An authorised queue that shows the archive editor the evidence alongside the suggestion
• Duplicate-processing controls and a reversal record to mitigate the risk of content being used inappropriately because of an incorrect tag

Where people stay involved

Data and security

Only fields required for the task are processed from authorised video, audio, transcripts and the archive glossary. Source-system permissions are preserved, and unnecessary personal, technical and security-sensitive data is minimised before being provided to the model. Every retrieval, suggestion, human change and target-system action is written to the audit log.

Who this suits

Frequently asked questions