If two metadata tagging tools both claim “high accuracy,” you still do not know which one will save editors time, reduce missed moments, and keep search trustworthy. What matters in real workflows is whether results are correct, usable, and consistent. A tool that occasionally tags the wrong person, suggests irrelevant topics, or drops timestamps will …
If you have ever searched for a player name and got 20 near-misses, or searched a breaking story and missed the one clip you needed, the problem usually is not the search bar. It is the metadata tagging underneath it. News and sports libraries move fast. Names change, teams rebrand, tournaments shift, and newsroom language …
Most MAM migrations do not fail because files do not move. They fail because teams lose speed after the move. The first place you feel it is search. Editors cannot jump to the exact moment they need, producers stop trusting results, and timecode-based discovery quietly disappears. If your media asset management environment relies on time-based …
Your team needs one clip. Search should take seconds. Instead, you get multiple versions with slightly different titles, missing previews, and conflicting dates. Someone downloads three files, opens two broken proxies, then messages a coworker, “Which one is the real one?” That daily friction is not just annoying. It is operational drag that compounds. The …
If you work with video or audio at scale, you already know this pain. You have a transcript, you know the quote exists, yet you still end up scrubbing through a two-hour recording to find the exact moment. The issue is not that you lack transcripts. It is that you lack timestamped transcripts. Timestamped or …
Why Timestamped Transcripts Beat Plain Text For Compliance, QC, And Discovery Read More »
The final whistle blows, the stadium empties, and your live broadcast ends. For most organizations, that is also the moment when the value of the content starts to decline. Yet every pass, goal, replay, and reaction that just went to air can keep working for you if you capture and connect it with the right …
From Live Sports Data To Evergreen Highlights: Using Metadata To Monetize Sports Content Read More »
When your channel is live, every second that goes on air either becomes an asset you can find and reuse, or it disappears into the noise. The difference often comes down to one thing. Whether your live broadcast metadata is rich, accurate, and generated in real time, or whether it is scattered across spreadsheets, log …
Live Broadcast Metadata: Real-Time Tagging That Powers Search, Alerts, And Reuse Read More »
Introduction Modern newsrooms are asked to do more with less: more platforms, more formats, more live hits, more on-demand content, and more pressure to publish first without sacrificing accuracy. The bottleneck is rarely the journalists or the gear; it is the friction between finding the right material and getting it to air or online in …
Newsroom Automation with AI Metadata: Faster Search, Smarter Storytelling Read More »
Introduction If your teams are constantly saying, “I know we shot that somewhere” while scrubbing through timelines, you do not have a storage problem. You have a metadata issue in your production workflow. Modern PAM and MAM platforms are more capable than ever. They can orchestrate cloud and on-prem workflows, manage terabytes of media, and …
Production Workflow Metadata 101: Making Every Clip Discoverable in Your PAM/MAM Read More »
Introduction Most media teams are sitting on a growing mountain of raw footage. Games, live events, news feeds, interviews, explainer videos, webinars, internal town halls, and more all end up in storage. The problem is not collecting content; it is finding the exact moment you need when a deadline or opportunity hits. That is where …
Metatagging Digital Assets at Scale: Turning Raw Footage into Searchable Gold Read More »
Digital Nirvana delivers knowledge management solutions, business process automation, and AI-based workflows that empower organizations to create, share, and mine actionable insights from electronic media.
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The intelligence layer for your Avid, Grass Valley, or custom MAM systems
Collection of AI microservices that watches your video and tells you what’s inside
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