Our advanced Metadata Extraction and Enrichment Platform leverages AI to analyze, capture, and store rich, detailed metadata from video streams and images.
Context
Why Rich Metadata Extraction is Essential
Rich metadata transforms raw video footage into a powerful knowledge base, unlocking new insights and capabilities across multiple industries—from security and enforcement to retail and infrastructure management.
Key Features
- Detailed Scene Descriptions: Capture contextual information like lighting, colours, objects, or interactions.
- Emotion and Gesture Recognition: Gain deeper insights into human behaviours and emotional states.
- Natural Language Retrieval (RAG): Query massive video repositories using everyday language—no technical queries required.
- Scalable Cloud Integration: Effortlessly handle expansive video libraries in a secure cloud environment.
Our advanced Metadata Extraction and Enrichment Platform leverages AI to analyse, capture, and store rich, detailed metadata from video streams and images. This granular metadata is instantly searchable through our integrated AI chatbot, powered by Retrieval-Augmented Generation (RAG), offering intuitive, natural-language-based queries.
Tasks
Challenge Faced
In one of our flagship projects—a large-scale film archive and licensing platform—we initially provided only an AI-powered search module. However, the project’s primary vendor, which was responsible for metadata extraction using a well-known tech giant’s AI service, produced overly simplistic results. For instance, the metadata would describe an archival film scene merely as “A bird flying in the sky.” Due to these limited descriptions, our search functionality could not identify specific species or intricate scene details, significantly limiting the archive’s usability. Filmmakers and content creators struggled to locate the exact frames they needed, and user engagement within the marketplace suffered as a result.
Results
How We Overcame It
To resolve the issue, we stepped up and built a custom metadata extraction engine that yielded far richer and more nuanced data. Rather than a broad statement like “A bird flying in the sky,” our AI enriched the scene with context: “The scene shows a hawk or eagle soaring through the sky... gliding against a backdrop of cloudy sky in monochrome.” This expanded dataset enabled much more precise search queries—for example, identifying the scene when someone typed “eagle” or “vintage bird shot.” The result was a groundbreaking level of detail that not only solved the immediate search problem but also elevated the entire marketplace by providing value-added metadata. Our solution quickly surpassed the expectations of both the client and the primary vendor, showcasing how a more tailored, context-aware AI approach can significantly enhance the discoverability and monetisation of valuable archival content.
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