Visit Gyrus AI at NAB Show 2026, Las Vegas (Booth W2300K). Explore AI-powered semantic video search, in-scene ads & media management solutio
NAB Show Isn’t Just a Trade Show. It’s Where the Media Industry Comes to Find Solutions to Its Real Problems.
Every April right when spring settles over Las Vegas, the people who actually build the media industry – editors, broadcast engineers, streaming architects, ad tech leads show up at the NAB Show. Their goal isn’t vague. They’re there to spot real problems in how media gets made. The show helps them to figure out what’s actually broken and who’s fixing it.
The latest 2026 edition, running from April 18–22 at the Las Vegas Convention Center has an unmistakable theme running through it: AI isn’t experimental anymore. It’s operational. Sessions for this year feature real deployments from Microsoft, Google Cloud, and BBC Studios – not just demos, but real-world impact.
A second AI Innovation Pavilion appears at NAB Show 2026 – a sign of how quickly the conversation has shifted. Instead of asking what AI means, people are now asking where to start using it. More importantly, the focus is moving from experimental AI to scalable, production-grade deployments that deliver measurable ROI. The new space on the floor reflects that change.
We’re also here for exactly that conversation. Gyrus AI takes space at Booth W2300K inside the AI Innovation Pavilion, showing off a pair of tools built sharp for real problems today’s media teams face daily. One speeds up how quickly clips get found, while the other slips ads into view so smoothly they don’t yank attention away.
Semantic Media Search – Because “Search by Tag” Was Always a Lie:
Here’s the real situation in most media organizations today:❌ The Old Way✅ With Semantic Media SearchEditor needs a clip of “a crowd cheering at sunset”Types: “crowd cheering at sunset, outdoor stadium”Types in keywords – gets 4,000 unrelated resultsAI understands the meaning, not just the wordsSearches across 6 different folder structuresReturns contextually matched results in secondsEventually calls a colleague who “might remember where it is”Timestamps exact moments within each clip2–3 hours later, maybe finds itDone in under 5 minutes
The problem isn’t just storage. It’s retrieval. And retrieval has always been broken because traditional media asset management search systems were built around keywords and manual metadata, both of which require human effort to be accurate, and humans aren’t consistent.
Manual tagging becomes impractical and expensive at large scales. Humans make mistakes and miss relevant details.
What Makes It Actually Different?
This isn’t keyword search with better synonyms. It’s a different architecture altogether:
Text Queries | Image Queries | Audio Understanding | No Manual Tagging | No Pre-existing Metadata | Knowledge Graph Powered | Domain-Trained AICapabilityTraditional MAM SearchGyrus AI Semantic Media SearchSearch by natural languageRequires exact keywordsUnderstands meaning & contextSearch by imageNot supportedUpload an image, find similar scenesAudio content searchNot supportedSearches spoken words, music, toneRequires pre-taggingYes – ongoing manual effortNo – works on raw footageRelationship mappingFlat, keyword-basedKnowledge graph connects related contentIndustry-specific accuracyGeneric modelsDomain-trained for your vertical
Who This Is Built For:
News broadcasters with decade-long archives that are technically searchable but practically useless.
Post-production editors who waste billable hours hunting for clips they’ve seen before.
Sports networks managing thousands of match hours that need frame-level retrieval.
Streaming platforms trying to surface and reuse catalogue content efficiently.
MAM platform vendors who want to layer AI intelligence onto existing infrastructure via API.