Use case type
This category helps users find, tag, and navigate content using metadata extraction and semantic search. It addresses the problem of locating relevant material within large, mixed-media collections.
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2 mo
Adoption over time
By case publish month · completed months only
2 cases documented across 1 month (Jun 26 – Jun 26), peaking at 2 in June 2026.
1 so far in July 2026 (in progress, not charted)
Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.
Company examples
3 shown from 3 use cases
TwelveLabs developed multimodal video foundation models and deployed them on AWS to support training and high-volume inference at global production scale.Its Marengo and Pegasus models power semantic video search, summarization, and structured outputs over massive archives.
Vionlabs added text as a third modality to its multimodal content intelligence pipeline so it could better understand plot and textual nuances in video libraries.The company used Llama 3.1 models on Gemini Enterprise Agent Platform, integrated with BigQuery, to speed tool adoption and training-job iteration without building its own text LLM.The result was faster deployment, new frame-level indexing plans, and revenue growth without significant cost increase.
Australian Broadcasting Corporation (ABC) modernised its 90-year-old archive using Gemini API in Vertex AI for multimodal analysis to create rich, descriptive metadata.The project processes long-form video streams, generates structured metadata, flags incidental footage, and powers natural-language search and retrieval with human-in-the-loop review.
OtherCommon questions
