Today’s modern retail and e-commerce companies produce huge amounts of visual content – product photos, promotional videos, user-generated clips, audio voiceovers, influencer reels, etc.
When teams look for files using visual similarity, spoken content, or contextual semantics, old-style search tools fall short because they rely on manual tags or simple keyword indexing, which fail to understand the meaning of this content. Instead of just scanning filenames or descriptions, semantic and multimodal search systems turn text, images, video, and audio into a shared semantic space that enables retrieval based on meaning rather than exact metadata matches.
The Challenge:
A leading retail/e-commerce company is drowning in digital files – countless product images, raw video clips, unfinished ads, audio tracks piling up daily. This flood of data refused to slow down. Managing it became nearly impossible. Files piled higher every week. The search took forever.
Hours slipped away as video editors dug through folders, not timelines. Marketing teams lost momentum searching for past campaign assets rather than planning new launches. Old visuals got rebuilt again and again – just because nobody could track them down fast enough. Time meant for real tasks bled into endless searches across cluttered drives.
The media library contained an estimated 25–30% duplicate assets. Multiple outdated or unapproved versions mixed with new ones. Team members guessed where things might be. Some assets vanished entirely. Others got reused by accident. Time slipped away on busywork instead of real tasks. Mistakes crept into live campaigns. Frustration grew behind closed doors. The core issues were:
- Search by meaning wasn’t possible
- Duplicate content and low discovery
- Slow workflows and high operational costs
- Lack of multimodal search support
Put simply, the team wanted a smarter method for organizing digital files – one that understood meaning in images, audio, and text instead of relying only on labels – so finding and using old material became faster during projects.
The Wish List:
So the company set clear goals meant to make a real difference both technically and commercially.
- Contextual search without manual tagging.
- Ability to search media using text, image, or audio inputs.
- Faster indexing of large volumes of video and image data.
- A cost-efficient alternative to metadata-heavy or LLM-centric solutions.
- Seamless integration with the existing MAM/DAM platform.
- Measurable ROI, driving faster discovery, lower content creation costs.
The Solution:
One step ahead, the team brought Gyrus AI Semantic Media Search and integrated it into their media/digital asset management setup. Mostly behind the scenes, it works by understanding content deeply before delivering results.
- Contextual search, no tagging needed – Editors could now just type simple queries like “product unboxing close-up” or “model wearing blue jacket” 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 aligning with enterprise requirements.
The Results:
After integrating Gyrus AI Semantic Media Search into its existing Media Asset Management platform, the retail/e-commerce company observed the following measurable outcomes:
| Area | Impact |
| Editor Productivity | Editors saved 2–3 hours per day by finding clips in minutes, not hours – more time spent editing, not searching. |
| Marketing Output | Teams created 30-40% more assets (reels, promos, explainers, intro videos, brochures) by reusing existing content. |
| Content Operations | Faster discovery of approved product visuals reduced duplicate creation and content rework. |
| Search & Indexing | Asset discovery became ~80% faster; 1 hour of video indexed in ~5 minutes. |
| Cost Efficiency | Achieved up to 10× lower operational cost compared to metadata-heavy or LLM-based solutions. |
| Workflow Fit | Seamlessly integrated with the existing MAM and supported on-prem deployment. |
Now operations run faster because the media library has become simpler, cost-effective, and one piece feeds many tasks. Savings add up when files get reused instead of remade each time. Workflows feel smoother since assets load more quickly across online stores. The whole setup adapts easily as needs shift.
