Here’s a question I think we’ll be hearing a lot more in the industry, and soon: what if the monitoring system at your facility and the monitoring system at your rights holder were able to talk to each other?
Not able to share a dashboard. Not a phone call between NOC teams. Actually exchanging data, in real time, about the same piece of content as it moves through both organizations.
That’s not science fiction, it’s closer than most people realize. And I think it changes everything about how broadcast ops teams find problems, prove SLAs, and yes, use AI.
The Multi-Organization Problem Broadcast Monitoring Hasn't Solved
Broadcast engineers have gotten very good at monitoring what happens inside their walls. Alarm hierarchies, MTTR: the industry has invested a decade in getting faster at diagnosing problems once they show up in a facility.
The problem is that content doesn’t stay inside your facility.
A network picks up a sports game. That content originates at a production truck, gets encoded and sent over WAN, lands at the network’s facility, gets processed for linear playout and streaming, hits a CDN, and ends up on someone’s TV or phone. That’s eight or nine handoffs between multiple organizations. And for most of that journey, each organization’s monitoring system is completely clueless about what happened upstream.
When something goes wrong at the viewer end, like a dropout or a quality artifact, the finger-pointing starts. Was it the truck? The WAN? The encoder? The CDN? Everyone has their own data. Nobody has the whole picture.
What Cross-Organization Visibility Looks Like in Broadcasting Today
Even though these organizations are separate agencies, a lot of them are already running TAG. The sports associations, major networks, large cable operators: they’re all monitoring their infrastructure with the same platform. Those deployments have been siloed islands. They don’t have to stay that way.
We’re opening the TAG SDK so that third-party vendors can write data directly into the TAG platform. Witbe, Techex, others, systems that have visibility into parts of the chain where TAG isn’t natively deployed can now feed their data into the same pipeline. And all of that data, from TAG-native deployments and third-party integrations alike, flows into Elastic, where it’s available via API to whatever analytics or AI tooling a customer wants to build on top.
What that creates, for the first time, is a real distributed quality chain.
Let’s take a routine example. A broacdcaster takes a PGA feed. PGA’s TAG deployment is already monitoring that feed at the truck output. When the broadcaster picks it up, they can see quality metrics at the source, before it hits their facility. If there’s a degradation on the WAN leg, they’re not debugging in the dark. They have a reference point. They can see where the problem started.
That’s a fundamentally different way of operating.
Why AI in Broadcast Ops Is Only as Good as the Data Underneath It
veryone is talking about AI in broadcast right now. Automated root cause analysis, anomaly detection, predictive failure alerts, you name it. The potential is real. But AI ops is only as good as the data you feed it.
The accuracy problem with AI is the data. You need a lot of it, and it has to be accurate. Bad data at scale doesn’t produce better insights, it just produces wrong answers faster.
TAG’s platform is already generating enormous amounts of signal data. A large broadcaster running 58 MCMs, monitoring around 2,800 simultaneous decodes, with hundreds of thresholds active per feed: That’s a data pipeline. Jitter on every SRT input. PCR clock accuracy on every flow. Closed caption content on every channel, decoded and logged. Nielsen watermark presence, per stream, continuously.
Most of that data is being generated and discarded. Operations teams look at what caused an alarm. Everything else scrolls by.
Connect that data to an AI engine and the picture changes. Jitter creeping up on a WAN link, bandwidth dropping slightly below threshold, a spine switch showing elevated utilization; individually, none of those things triggers an alarm. Together, they predict a failure before it happens. An AI agent can draw that connection. But only if it has the data.
That means making sure every point in the chain is generating data, making sure that data is normalized and accessible, and making sure the platform is open enough that customers can build whatever they want on top of it.
The bottom line
I want to be clear about where we are versus where we’re going.
Content fingerprinting, watermark detection, stream quality monitoring: TAG has been doing that for years. That’s not the story here. What’s new is the architecture that makes cross-organizational visibility possible, and the SDK openness that brings third-party systems into the same data layer.
The multi-tenant, cross-org piece is on our roadmap. It requires TAG deployments at multiple points in the chain and a willingness from operators to share data across organizational boundaries. That’s a commercial and trust question as much as a technical one, and it won’t happen overnight.
But the data infrastructure it needs, such as MCS pushing everything to Elastic, an open API, an SDK that third parties are already writing against, that exists today. The foundation is there. What gets built on top of it is the interesting question.
Broadcast has always been an industry where the signal matters more than anything else. What we’re working toward is a future where you can see the signal everywhere it travels, not just where it lives in your facility.
That’s the visibility the industry needs. And we’re building it.
Michael Demb | VP of Product Strategy | TAG Video Systems
Michael Demb is Vice President of Product Strategy at TAG Video Systems, turning broadcast challenges into practical products. With 20+ years in media technology, he brings a hands-on approach to helping customers adopt IP and software-defined workflows. Based in Toronto with his wife and three kids, he’s usually tackling DIY projects or skiing when he’s not working.
