Building for the Next Phase of Intelligence at the Edge
By Benny Retnamony, founder & CEO, Quvia
Everyone’s talking about AI as the next great disruptor. But in network-constrained environments, not enough attention is paid to the data infrastructure required to make that possible.
Intelligence doesn’t exist in a vacuum. It sits on top of data. If data can’t move, AI doesn’t work. And whether data can move depends on the network.
In industries like aviation and maritime, data is generated and stored at the edge. That’s where goods are produced, services are delivered and decisions are made. These ecosystems were built to run on limited or intermittent connectivity. Now, they’re sitting on enormous unrealized value.
The value created above the network will be orders of magnitude greater than the network itself.
Whether that value gets fully realized comes down to two fundamental shifts.
Edge and cloud working together at scale
Data is being created at the edge faster than organizations can use it.
Aircraft, ships and offshore assets generate large volumes of data every day. Much of it stays at the edge because moving it is too expensive and unreliable. That constraint ultimately shaped how the edge ecosystem was built. But operational requirements are changing.
In most environments today, continuous data movement between edge and cloud is assumed. Analytics run continuously. Models are trained and updated without friction.
In network-constrained environments, that’s not the case. Moving data off the edge is limited.
For a long time, that limitation has been about bandwidth. But as bandwidth expands, that limitation is shifting to intelligence. Too much data movement still operates without context, treating all data the same regardless of value or impact. That doesn’t scale.
What’s required now is intelligent data movement between edge and cloud. Systems need to decide what moves, when it moves and under what conditions.
That will make new digital workflows and experiences possible in remote environments, with impact extending well beyond AI and unlocking capabilities that have long been blocked.
That means airlines can serve personalized ads or dynamic content by syncing large media libraries and tailoring experiences by route. Cruise media teams can upload thousands of high-resolution photos and videos during each trip, enabling real-time digital sales and same-day experiences. Seismic survey companies collecting terabytes of data daily can sync datasets back to shore in near real time, reducing reliance on physical media transfers.
This is where our focus is heading. Building the digital fabric between edge and cloud that allows value to be created above the network rather than constrained by it.
AI agents built on network intelligence
Everyone will have AI agents. That won’t be the differentiator. What matters most is the data those agents can access and the context they can act on.
In these environments, network behavior isn’t a background metric. It’s a primary driver of application performance and user experience. When a system flags an issue, it shouldn’t simply report a failure. It should understand the network conditions that caused it.
That context turns reactive alerts into proactive resolution.
In practice, teams still troubleshoot across multiple tools with limited end-to-end context.
This is where AI creates real value. Agents built on network intelligence can continuously monitor systems, identify anomalies early and surface recommendations with full context. The result is less noise and faster time to resolution.
In aviation, this can mean identifying onboard IT and IFEC issues before they affect passengers. In cruise, diagnosing connectivity or application failures in real time and guiding guest-experience teams to resolution. In energy, helping offshore teams troubleshoot critical systems remotely and escalating issues with full context when needed.
Today, much of this work remains manual and reactive. AI agents move that intelligence into the system itself, allowing issues to be identified and understood earlier.
AI agents will become standard. Differentiation will come from the data and context behind them.
Looking ahead
AI will not be defined by models alone. It will be defined by whether data can move and intelligence can act. Edge and cloud must work as one.
Making that possible requires deep collaboration across the ecosystem.
That’s what we’re focused on in 2026: continuing to build the foundation and working with partners to help customers realize the full value above the network.
If you’re interested in partnering, I’d welcome the conversation.
