{"id":2190,"date":"2025-06-18T13:33:15","date_gmt":"2025-06-18T13:33:15","guid":{"rendered":"https:\/\/gyrus.ai\/blog\/?p=2190"},"modified":"2026-01-23T11:55:32","modified_gmt":"2026-01-23T11:55:32","slug":"how-gyrus-helped-news-broadcaster-save-10x-media-processing-costs","status":"publish","type":"post","link":"https:\/\/gyrus.ai\/blog\/how-gyrus-helped-news-broadcaster-save-10x-media-processing-costs\/","title":{"rendered":"How Gyrus Helped a News Broadcaster Save 10x on Media Processing Costs?"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">The broadcaster with its niche news and sports channel was battling with inefficiencies in their <a href=\"https:\/\/gyrus.ai\/Solutions\/media-asset-management-search.html\" target=\"_blank\" rel=\"noopener\">MAM<\/a> (media asset management) and search workflows. With an inherently small content library, the company found itself expending a lot of resources on manual data entry and tagging, the search, and retrieval of media, especially from the production environments.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The system did not have any automation; it was metadata-based and very resource-intensive. This increase in operational turnarounds, editors having lots of overtime, and increase in the operational costs.<\/span><\/p>\n<h3><strong>The Challenge:\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 <\/strong><\/h3>\n<ol>\n<li><span style=\"font-weight: 400;\">High cost of media processing due to the complete reliance on manual tagging and outdated search mechanisms.\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Delayed turnarounds &#8211; the searching of content by moments, keywords, or events demanded manual sifting through the footage. <\/span><\/li>\n<li><span style=\"font-weight: 400;\">Low-budget infrastructure prevented them from opting for high-end servers or cloud-native solutions.<\/span><\/li>\n<\/ol>\n<h2><strong>The broadcaster wanted a media management solution that was:<\/strong><\/h2>\n<ul>\n<li><span style=\"font-weight: 400;\">Fast and accurate in terms of content discovery.\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 <\/span><\/li>\n<li><span style=\"font-weight: 400;\">\u00a0Very low on infrastructure requirements.\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 <\/span><\/li>\n<li><span style=\"font-weight: 400;\">Economically viable for their scale\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 <\/span><\/li>\n<li><span style=\"font-weight: 400;\">Easy to integrate into their on-premise environment.<\/span><\/li>\n<\/ul>\n<h2><strong>Our Solution: Gyrus On-Premise Intelligent Media Search<\/strong><\/h2>\n<p><span style=\"font-weight: 400;\">We deployed Gyrus&#8217; on-premise <a href=\"https:\/\/gyrus.ai\/blog\/why-on-premise-ai-media-search-making-a-comeback\/\" target=\"_blank\" rel=\"noopener\">media search engine<\/a> tailored to suit the client\u2019s requirement. Gyrus&#8217; AI-powered platform offers a lightweight media search solution and digital asset management that plugs and plays for fast and cost-effective results without tagging or metadata.<\/span><\/p>\n<h3><strong>Key Features Deployed:\u00a0 \u00a0 \u00a0<\/strong><\/h3>\n<ol>\n<li><span style=\"font-weight: 500;\"><strong>On-Premise Deployment:<\/strong> <\/span><span style=\"font-weight: 400;\">Deployed on the customer&#8217;s premises for guaranteed data privacy and zero dependence on the cloud.<\/span><\/li>\n<li><strong>Lightweight &amp; Efficient:<\/strong><span style=\"font-weight: 400;\"> It smoothly runs on an affordable Nvidia 4070 GPU that can index 1-hour video within 15 minutes, with minimal compute resources.<\/span><\/li>\n<li><span style=\"font-weight: 500;\"><strong>Zero Tagging Required:<\/strong> <\/span><span style=\"font-weight: 400;\">Editors were able to search for events, people, or scenes without adding tags or metadata-the human effort was reduced drastically.<\/span><\/li>\n<li><span style=\"font-weight: 500;\"><strong>Custom Multi-Modal Embedding Model:<\/strong> <\/span><span style=\"font-weight: 400;\">Translates the video and audio content into searchable vectors based on the analysis of scene context, actions, sentiments and spoken words<\/span><\/li>\n<li><span style=\"font-weight: 500;\">\u00a0<strong>Supports contextual search<\/strong><\/span><span style=\"font-weight: 400;\"> using vision-language search techniques, allowing editors to query in natural language or visuals\/audio, and get precise result.\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 <\/span><\/li>\n<li><span style=\"font-weight: 500;\"><strong>Domain-Specific AI:<\/strong> <\/span><span style=\"font-weight: 400;\">Tuned for broadcasting-articulated in the language of sports events, scores, and live interaction on screen.<\/span><\/li>\n<\/ol>\n<h3><strong>How It Works \u2013 Semantic Search Workflow:<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">With a custom embedding framework, this specifies the semantic search workflow of <a href=\"https:\/\/gyrus.ai\/Solutions\/media-asset-management-search.html\">Gyrus&#8217; solution<\/a>:<\/span><\/p>\n<h3><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone wp-image-2192\" src=\"https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Gyrus-AI-Solutions.pptx.jpg\" alt=\"Semantic Search Workflow\" width=\"702\" height=\"395\" srcset=\"https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Gyrus-AI-Solutions.pptx.jpg 960w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Gyrus-AI-Solutions.pptx-300x169.jpg 300w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Gyrus-AI-Solutions.pptx-768x432.jpg 768w\" sizes=\"(max-width: 702px) 100vw, 702px\" \/><\/h3>\n<p><span style=\"font-weight: 400;\">This multi-modal mapping ensures the system understands everything from visual cues (scoreboards, player reactions) to commentary speech or textual overlays. It\u2019s powered by vision-language search, enabling the AI to semantically interpret both video and audio together.<\/span><\/p>\n<h3><strong>Demonstrated Results:<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">In the course of showcasing the actual implantation, we had a demo where one typed in the query \u201cArsenal win against Chelsea\u201d without using any tags or predefined metadata.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Gyrus immediately located the exact clip showing the match result:\u00a0 <\/span><span style=\"font-weight: 400;\">Arsenal 1 \u2013 Chelsea 0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">\u27a1\ufe0f <\/span><strong><a href=\"https:\/\/youtu.be\/uvZl6MnTS-A\" target=\"_blank\" rel=\"noopener\">[Watch demo video here]\u00a0\u00a0<\/a><\/strong><\/p>\n<p><img decoding=\"async\" class=\"alignnone wp-image-2194\" src=\"https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230626.png\" alt=\"Broadcasting - Media Asset Management \" width=\"478\" height=\"269\" srcset=\"https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230626.png 1919w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230626-300x169.png 300w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230626-1024x576.png 1024w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230626-768x432.png 768w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230626-1536x864.png 1536w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230626-1300x731.png 1300w\" sizes=\"(max-width: 478px) 100vw, 478px\" \/>\u00a0 \u00a0 <img decoding=\"async\" class=\"alignnone wp-image-2193\" src=\"https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230752.png\" alt=\"AI Broadcasting - Media Asset Management \" width=\"481\" height=\"270\" srcset=\"https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230752.png 1919w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230752-300x169.png 300w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230752-1024x576.png 1024w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230752-768x432.png 768w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230752-1536x864.png 1536w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/Screenshot-2025-06-09-230752-1300x731.png 1300w\" sizes=\"(max-width: 481px) 100vw, 481px\" \/><\/p>\n<h4><strong>Whether Searching by:<\/strong><\/h4>\n<ul>\n<li><span style=\"font-weight: 400;\">typing in words such as &#8220;Arsenal beat Chelsea,&#8221;<\/span><\/li>\n<li><span style=\"font-weight: 400;\">uploading an image of the scoreline,<\/span><\/li>\n<li>\u00a0or using it as a commentary audio snippet,<span style=\"font-weight: 400;\">Gyrus understands the contextual search intent and retrieves the exact moment instantly &#8211; without the need for manual tagging or metadata.<\/span><span style=\"font-weight: 400;\">It used to take an hour and more of manually scrubbing the video.<\/span><br \/>\n<h4><strong>Tangible Impact.<\/strong><\/h4>\n<table style=\"height: 381px;\" width=\"808\">\n<tbody>\n<tr>\n<td>\n<p style=\"text-align: left;\"><strong>Metric<\/strong><\/p>\n<\/td>\n<td style=\"text-align: left;\"><strong>Before Gyrus<\/strong><\/td>\n<td>\n<p style=\"text-align: left;\"><strong>After Gyrus<\/strong><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Video indexing speed<\/span><\/td>\n<td><span data-teams=\"true\">No automated indexing<\/span><\/td>\n<td><span data-teams=\"true\"> Automated indexing: 1 hr video = 10\u201315 mins compute time<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Tagging effort<\/span><\/td>\n<td><span style=\"font-weight: 400;\">4\u20136 hours\/day<\/span><\/td>\n<td><span style=\"font-weight: 400;\">0 hours\/day<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">GPU cost<\/span><\/td>\n<td><span style=\"font-weight: 400;\">High-end cloud GPU<\/span><\/td>\n<td><span style=\"font-weight: 400;\">RTX 3090\/4070 GPU<\/span><span style=\"font-weight: 400;\"> (Low-cost)<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Search turnaround<\/span><\/td>\n<td><span style=\"font-weight: 400;\">3-5 mins<\/span><\/td>\n<td><span style=\"font-weight: 400;\">&lt;10 seconds<\/span><\/td>\n<\/tr>\n<tr>\n<td><span style=\"font-weight: 400;\">Media processing cost<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Baseline<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Reduced by ~90%<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The broadcaster was spending much more annually on manual media processing.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">It has undergone a marked 10X decrease with our solution &#8211; a very economical switch.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">The solution has even gotten the editors the accuracy of finding the contextually relevant content &#8211; whether by way of keywords, images, or audio input.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">It brought automation to workflow, really freeing up the editorial team to finally concentrate on storytelling instead of doing backend grunt work. grunt work.<\/span><\/li>\n<\/ul>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone wp-image-2196\" src=\"https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/SM-Post-3-1-scaled.jpg\" alt=\"\" width=\"603\" height=\"603\" srcset=\"https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/SM-Post-3-1-scaled.jpg 2560w, https:\/\/gyrus.ai\/blog\/wp-content\/uploads\/2025\/06\/SM-Post-3-1-300x300.jpg 300w\" sizes=\"(max-width: 603px) 100vw, 603px\" \/><\/p>\n<h3><strong>Conclusion:<\/strong><\/h3>\n<p><span style=\"font-weight: 400;\">The use case above exemplifies ways and means that small broadcasters could exploit AI-powered contextual search to leapfrog traditional work processes. Gyrus came up with a scalable, cheap, and explainable implementation for the broadcaster to trim the cost, enhance work speed, and get active control of their media assets using their own infrastructure. <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The broadcaster with its niche news and sports channel was battling with inefficiencies in their MAM &hellip; <a title=\"How Gyrus Helped a News Broadcaster Save 10x on Media Processing Costs?\" class=\"hm-read-more\" href=\"https:\/\/gyrus.ai\/blog\/how-gyrus-helped-news-broadcaster-save-10x-media-processing-costs\/\"><span class=\"screen-reader-text\">How Gyrus Helped a News Broadcaster Save 10x on Media Processing Costs?<\/span>Read more<\/a><\/p>\n","protected":false},"author":11,"featured_media":2197,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[6],"tags":[148,123,142,121,152,147],"class_list":["post-2190","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-technology","tag-ai-media-search","tag-broadcasting-stream","tag-intelligent-media-search","tag-media-asset-management","tag-media-processing","tag-semantic-video-search"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>How Gyrus Cut Media Processing Costs 10x for News Broadcaster<\/title>\n<meta name=\"description\" content=\"See how Gyrus AI\u2019s on-premise intelligent media search reduced a news broadcaster\u2019s media processing costs by 10\u00d7 without manual tagging.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/gyrus.ai\/blog\/how-gyrus-helped-news-broadcaster-save-10x-media-processing-costs\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"How Gyrus Cut Media Processing Costs 10x for News Broadcaster\" \/>\n<meta property=\"og:description\" content=\"See how Gyrus AI\u2019s on-premise intelligent media search reduced a news broadcaster\u2019s media processing costs by 10\u00d7 without manual tagging.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/gyrus.ai\/blog\/how-gyrus-helped-news-broadcaster-save-10x-media-processing-costs\/\" \/>\n<meta property=\"og:site_name\" content=\"Gyrus AI | Blog | Insights on AI &amp; 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