Media Asset Management (MAM) and Digital Asset Management (DAM) platforms are considered to be the backbone for broadcasters doing numerous operations like storing and organizing massive libraries of video contents – news, shows, sports, archives, etc and making them accessible for reuse.

But broadcasters these days are not satisfied with just storage anymore. They are demanding speed, intelligence, and cost efficiency. They have to find events of interest in video files based on context and not just titles or manual tags. This is the point where traditional metadata search falls short and contextual AI search proves its value.

The Challenge: 

A European broadcaster was evaluating new MAM platforms. Their biggest frustration they faced was in the search part:

• Manual tagging was slow and inconsistent, and the editors wasted hours tagging the footage or searching for moments based on incomplete metadata.
• LLM searches were not as affordable as they thought – Attempts were made to implement actually working solutions based on large language models, but the cost was too high to scale.
• Workflow delays – To find the right clip, their team often had to scrub through the entire footage, relying mostly on luck and sometimes spending hours just to locate a single scene.

In short, this broadcaster wanted a system/solution that:

• Not only organizes the media library but also makes finding relevant clips fast and hassle-free.
• Would search for scenes contextually, without any tags or metadata.
• Fast, affordable, and flexible (either on the cloud or on-prem).

The Solution:

A Media/Digital Asset Manager bidding for this customer integrated Gyrus AI Semantic Media Search into their Media Asset Management platform, delivering advanced search capabilities. Here’s what stood out:

• Contextual search, no tagging needed – Editors could now just type simple queries like “goal celebration” or “sunset cityscape” and instantly find the scene they were looking for.
• 80% faster processing speed – An hour of video gets indexed in ~ 5 minutes by an RTX 3090/4060.

• Up to 10× more cost-effective – Our solution was able to deliver the most cost savings when compared to metadata-heavy or LLM-based solutions.
• Compact multimodal model – It is optimized to process video, audio, and images while staying lightweight and efficient.
• Flexible deployment – Able to run on-prem or in the cloud, depending on broadcaster needs.

Key Technologies Behind It.

Our Semantic Media Search features foundation multimodal models, similar in lineage with CLIP (Contrastive Language-Image Pre-training), CLAP (Contrastive Language-Audio Pretraining), and advanced video-language encoders:

• Extract the features from video, audio, and text.
• Convert them into semantic embeddings (digital fingerprints of meaning).
• Store embeddings in a vector database for really fast retrieval.

This way, the queries like “black car entering the scene” return the clips very relevant to such a scene, even if there is no actual metadata describing those clips.

 

The Benefits for the Broadcaster

After testing the Gyrus AI’s Semantic Media Search enabled MAM, the broadcaster immediately saw the difference and the impact was clear:

Metric	Impact
High speed	80% faster scene retrieval than manual or metadata search.
Minimal Compute	1 hour long long video processed in ~5 minutes.
Resource Optimized	Optimized to run on RTX 3090/4060/4070.
Cost	Most cost-effective AI search solution compared to LLM or metadata-based alternatives.
Deployment	Works both on-prem, cloud or hybrid.
Usability	Search using text, image or audio.

Result:

The broadcaster, after testing the solution, decided to migrate to a new MAM platform that had integrated Gyrus AI’s Semantic Media Search feature. Therefore, two big challenges were met at once:

On one hand, the broadcaster gained a cost-effective, AI-powered search solution; on the other, the MAM provider differentiated its platform with intelligence that competitors lacked.

On the broadcaster’s side, it meant faster turnarounds, lower operational costs, and a reliable system that scaled without adding technical complexity.  For the MAM player, it meant adding the large enterprise customer that is always looking for differentiated value – a MAM that really has intelligent contextual search and media management that is future-ready.

Future Outlook.

As multimodal AI continues to evolve, semantic search will also expand to deliver:

• Personalized Search: Results tailored to the project context or user history.
• Deeper Insights: Automated clustering, thematic mapping, and trend analysis of archives.
• Predictive Recommendations: Suggestions of content based on cultural context and storytelling patterns.

Conclusion

The use of MAM + Semantic Media Search made search and retrieval operations nearly 8× faster, while delivering a solution that was 10× more cost-effective than regular LLM-based or manual metadata approaches. The system was able to deliver real-time speed and scale without sacrificing accuracy.

This case highlights that the future of Media Asset Management is AI-empowered contextual intelligence-solutions that can flexibly be deployed on-prem or on the cloud, accordingly adapted to broadcaster needs, and capable of technically accommodating an exponential growth in content demands.