What Does It Take To Treat Video As A First-Class Data Source?
I recently joined Mike Anzalone and Carmen Haak on On Record, the podcast from Intelligent Video Solutions (IVS). IVS builds VALT, its Video Audio Learning Tool for simulation, training, and research, and Wowza Streaming Engine delivers every live and on-demand stream inside it. Getting live video out at scale, securely and reliably, is largely a solved problem. The bigger opportunity is treating video as a first-class data source, a programmable source of operational data that triggers action on infrastructure an organization already controls.
Most organizations have enormous amounts of video that nobody watches. That gap carries real costs. Oceaneering gave us a great example during our recent webinar, where a remote subsea camera picked up a thin stream of bubbles coming from a pipe, and the system recognized it and raised an alert in real time. With IVS, we continued that discussion, focusing on how to use those video feeds to power workflows in real time, using the cameras, systems, and staff you have today.
What Should A Video Foundation Offer Before You Add AI?
A video foundation should be deployment-agnostic, extensible, and portable, so intelligence can run wherever the video already lives. For example, I used Anthropic’s Claude as a coding assistant and built a proof-of-concept module for a new delivery protocol, Media over QUIC (MoQ), in a couple of weeks. Partners like IVS use those same APIs to build Wowza Streaming Engine into applications, like VALT.
Wowza Streaming Engine, the streaming infrastructure that powers VALT, takes in video from cameras, files, satellite links, and almost any other source. It then processes, packages, and secures that video and delivers it to any device in near real time. Most customers build Wowza Streaming Engine into their own products through our APIs, VALT works the same way.
Wowza Streaming Engine takes full advantage of the hardware underneath it, including GPUs and VPUs, to optimize for high stream density. Plus, it can run on prem, at the edge, in the cloud, or across all three in a hybrid configuration. The same software runs in each environment, so a workflow stays portable as requirements change. A customer running 100 steady streams that jump to 1,000 on weekends needs an architecture that scales up and down quickly. A customer pushing 10 streams to a very large audience faces a different design problem. We have streamed live video from the ocean floor and from space, and we support mission-critical operations, like E911 services, that give dispatchers live video from callers.
What Does Activating Live Video Actually Mean?
By adding an intelligence layer on top of the streaming foundation, events caught on camera become structured signals that downstream systems can act on. All operators need to focus on is defining the rules that matter and letting the framework watch for them. Mike and Carmen work closely with nursing and medical schools that use simulation to demonstrate learner competency. If something happens, the response should follow within 10 seconds. If the response never happens, the system can send a notification or simply mark that moment in the recording. Over time, those markers become data that feeds back into IVS’ owned models that can make them more accurate.
Wowza’s Video Intelligence Framework (VIF) is for exactly that. VIF extracts frames from live streams, routes them to AI models for inference, and converts the results into real-time outputs such as webhooks, JSONL logs, overlays, custom listeners, and timed metadata. Those outputs turn passive video into structured signals that incident systems, dashboards, and other tools can act on in real-time. Like Wowza Streaming Engine, VIF is designed for flexibility, so teams can add, train, or tune models for their own environments. A clinical simulation lab and an offshore operator look for very different things, and each benefits from a model tuned to its setting.
We built VIF as a framework so new models and capabilities can be integrated as they mature. One example is the NVIDIA Synthetic Video Detector (SVD), which flags AI-generated or manipulated video. Verify for VIF goes a step further by pairing the SVD score with a VLM for a plain-language explanation of why a video was scored as synthetic.
Why Does Local Processing Matter For Regulated Industries?
Local processing matters most for VALT customers in healthcare and law enforcement, where data sovereignty requirements decide where sensitive video can go. With Wowza Streaming Engine and VIF, video stays on infrastructure the organization controls. Analysis can run on prem or at the edge, inside a hospital network, a police station, or a secure government network. A deployment can start with a single edge machine and grow to multiple servers that handle both video processing and AI. Keeping processing in approved environments means VIF can support compliance-sensitive architectures, subject to each organization’s own legal and security validation.
Why Is Time To Action The New Metric For Video?
The new metric that teams are using to measure the effectiveness of AI video workflows is “time to action.” Time to action can be thought of as the gap in time between when something happens on camera and when the response is triggered. For example, the time between a patient ending up out of position during a clinical simulation and an instructor stepping in to correct it. Video AI keeps shrinking that gap as it moves from recognizing objects to understanding what is happening and what might happen next. After that, the industry is heading toward predicting events such as a traffic jam or a crash from patterns across a scene (traffic monitoring is a natural place for that to start).
How Should A Team Start A New Video Streaming Or Video AI Project?
For teams embarking upon a video AI project, start small. Pick one clear use case, one workflow, and one problem, then get it working end to end before thinking about scale. Carmen raised the example of a nursing simulation streamed to a classroom, whether that classroom sits across the hall or across the country. Once that runs smoothly, scaling to more classrooms becomes easier than trying to launch 200 classes next week.
Many organizations already own more of the infrastructure and hardware than they realize. Streaming and video AI can often use the cameras already in place and the servers a team already runs. Wowza Streaming Engine also generates adaptive renditions that hold up across screen sizes and changing bandwidth, which makes it easier to optimize for mobile from day one.
It’s also important to get the right people involved early. That includes business owners to define the problem, video engineering and IT teams for architecture and infrastructure constraints, and security stakeholders. Partners fill the remaining gaps, including CDNs for delivery at scale, cloud storage, DRM, encoders, and cameras.
Watch The Full On Record Episode
Thanks to Mike, Carmen, and the whole IVS team for a great conversation. If your organization deals with any amount of video feeds, take a hard look at how that video gets used. There is a lot of data buried in those streams. See VIF in action or talk to a Wowza expert if you want to learn more.
Frequently Asked Questions
What does it mean to treat video as a data source?
Treating video as a data source means analyzing streams to trigger alerts, timeline markers, or workflow actions in real time. Video stops sitting unwatched in storage and becomes a programmable source of operational data for the systems an organization already uses.
What is VALT?
VALT (Video Audio Learning Tool) is the video recording and content management platform from Intelligent Video Solutions (IVS), used for simulation, training, education, and research. Wowza Streaming Engine powers live and on-demand stream delivery inside VALT.
How can video AI support competency assessment in simulation training?
Video AI can support competency assessment by watching for an expected sequence, such as an action following another within a set time, and flagging the recording when that sequence breaks. Instructors then review the moments that matter instead of the full session.
Who should be involved in planning a video streaming deployment?
A video streaming deployment works best when business owners define the problem, engineering or IT teams own the architecture, and security stakeholders join the deployment conversations earlier rather than later. Partners, such as CDN, DRM, encoder, and camera vendors, can fill the remaining gaps.
