Searchable media archives with Gemini on Vertex AI
How a broadcaster could describe, tag and search decades of video and images using Gemini’s multimodal models and Vertex AI Search.
The situation
A broadcaster holds a large archive of video, photos and transcripts with sparse, inconsistent metadata. Producers spend hours searching for footage, and valuable content goes unused.
Our approach
Prioritise the archive
Start with the most-requested collections and agree a tagging vocabulary with producers and archivists.
Generate metadata
Gemini describes scenes, identifies topics and generates searchable summaries and tags for video and images.
Index for search
Vertex AI Search indexes the generated metadata and transcripts for natural-language search.
Archivist quality control
Archivists review samples, correct tags and handle rights-sensitive material.
Scale in batches
Process the remaining archive in cost-controlled batches, monitoring quality throughout.
Typical technology
What success looks like
Metrics we would agree with you up front and track throughout:
- Time to find footage
- Archive share with usable metadata
- Search success rate
- Reuse of archive content
- Processing cost per hour of video
This is an illustrative scenario showing how Inspired Infotech approaches this type of problem. It does not describe a specific client, and actual approach and outcomes depend on each organisation’s systems, data and goals.
Services behind this scenario
Google Vertex AI & Gemini
Gemini models, Vertex AI Search and agents on Google Cloud — multimodal AI grounded in your data.
Learn moreAI & Machine Learning
Predictive models, computer vision and NLP built on your data and taken all the way to production.
Learn moreGoogle Cloud
Data, analytics, Kubernetes and Gemini-powered AI on Google Cloud Platform.
Learn moreFacing a similar challenge?
Let’s talk about your situation and what a realistic plan looks like.