Graphiant: The AI Strategy
Vinay Prabhu, Chief Product Officer at Graphiant, outlined a two-part strategy encompassing network for AI and AI for network. The network for AI pillar addresses the challenge of AI being a massive distributed publisher and subscriber problem, where data is generated in one location and inference happens in another, often across different business boundaries. To manage this, Graphiant provides a platform for secure data exchange, analogous to financial or healthcare workloads. Prabhu emphasized that simplifying exchange is insufficient without trust, using a ride-sharing app analogy: just as a passenger needs to see the driver and the path, enterprises need real-time observability, auditability, and centralized control to program governance policies directly onto the global fabric.
The second pillar, AI for the network, is embodied by GINA (Graphiant Intelligent Network Assistant). GINA is designed to act as a virtual member of the operations team, automating complex, time-consuming tasks. Prabhu gave the example of a CSO requesting a monthly compliance report, a task that might take an hour to manually collate data from various dashboards and databases. GINA can generate this report, along with threat intelligence and infrastructure insights, almost instantly. Prabhu summarized GINA’s value as running a 60-minute stand-up in 60 seconds, buying back valuable time for practitioners to focus on innovation rather than manual data gathering.
Presented by Vinay Prabhu, Chief Product Officer. Recorded live at Networking Field Day 39 in Silicon Valley on November 5, 2025. Watch the entire presentation at https://techfieldday.com/appearance/graphiant-presents-at-networking-field-day-39/ or visit https://techfieldday.com/event/nfd39/ or https://Graphiant.com for more information.
Transcript
Hey folks. Uh, I'm Reay Pabu, chief Product Officer at Graphite. And for the past few weeks and months, everyone's been asking, be it prospects, customers, or practitioners.
What's an AI strategy really look like for a network infrastructure company? And unlike any other strategy based question, it can't be built in isolation. So what we did was, Hey, let's divide and conquer this.
Look at the fundamental parts that you need for ai, for network strategy and a network for AI strategy. So the first piece of the puzzle Is the data exchange. What does an AI network really look like?
What does it have to support? And what we really realized was AI is a massive distributed publisher and subscriber problem. You may be the one who's generating the data, but the inference might be happening at some other business location, and it may not be your business that's running those GPUs or those inference engines.
Essentially, it's a scale problem of moving your data from one business to another business, but extending your data jurisdiction beyond your boundaries. We see a lot of analogous workloads in the financial and the healthcare sectors where you have data that is exchanged between satellite offices or banks consuming financial services. So what we did there was we consulted the practitioners who are living and breathing this data exchange problem on a daily basis.
And hence, you see the tagline of designed by practitioners built by graph. Essentially, to enable data exchange rapidly, we need to simplify two things of the puzzle in the data exchange problem itself. The first one is the logistical and communication problem.
Essentially being able to get the information out to your partners to enable that service and bring it up and online, essentially giving you a marketplace where you can really consume applications on demand. And the second, the security problem, really being able to govern, secure and observe what's happening on that data itself as it moves in its lifecycle of the data in motion. With that in mind, I'll jump to the second pillar, but before I jump to the second pillar, I'll tell you why we think that pillar is really important.
And just a quick show of hands, folks who have used Uber or Lyft or any ride sharing app, either to come here to this event or any other location. Okay, so a bunch of y'all have, and let me pose a second order question A, and assuming you all have you'all trust the application, correct? Well, would you be comfortable riding on that cab if Uber didn't give you visibility into the type of driver, the type of vehicle, or give it, give you visibility into your path, form your source to your destination and just ask you to trust it?
I guess not, right? So it's not enough for me to simplify the data exchange. I need to be give, uh, be giving you the observability, the auditability of how that exchange is happening in real time.
And I have to not only give you the visibility, I need to give you the control back to really decide what is compliant to you. What means, uh, means, uh, a secure communication platform for your business needs. So the second pillar that you really need to focus on for AI strategy is observability.
Auditability and trust. So once we have simplified the data exchange, what we want to provide you is realtime visibility of who is producing the data, who's consuming it, when are they consuming it, give you full auditability back in time to see how that production and consumption took place and the ability to control that production and consumption of data in real time through a simplified centralized policy construct. Essentially giving you the ability to program our backbone network.
So for the first time, you don't just program your edge, we give you the ability to program the global fabric. And the last piece of the puzzle, the AI for the network itself. So we are giving you the ability to really exchange data.
We're giving you the ability to see your data in action. We are giving you the ability to control it, but it's a lot of information that you need to munch. So what we have introduced here is Gina.
She's gonna be part of your workforce sitting and giving you access and insight into what's running on the network itself. She's beyond someone who is asking you questions. This comes from my real life experience of building, managing, and operating the network at graph itself.
She's here to guide you into what looks to be an important question that needs to be answered in order to get this network operational and compliant. It takes me close to 60 minutes to compile this data. Typically, my chief security officer always wants the compliance report at the end of every month.
It, the sources of the report are varied. It's different people, different dashboards, different databases. It's hard to really collate all that information and really present it in a form that makes sense and makes it actionable.
For me, what Gina helps me do in this, uh, in this construct is essentially give me the ability and a guided outlook of how I can run these reports, how I can really get her to be a part of my workforce. So it unloads the burden on my practitioners who are really building and operating it. So what she does in turn is gives me observability of how the infrastructure is performing.
She gives me compliance reports of where I need to improve on. She gives me threat intelligence that I need to act on immediately to protect this infrastructure that's on the move. So essentially Gina is running a 60 minute standup within 60 seconds for me.
So she saved me, she's bought me back time, 59 minutes of my time on a weekly basis just through a standup meeting that Gina is running. So sorry. So Gina is, is ai is is The AI Is AI and AI program?
Yeah. And okay. Dumb question, but just 'cause I'm a**l.
Yeah. Does Gina stand for anything? Yes, it's the G is grapht graph Intelligent network assistant.
Okay. Is what Gina is. So essentially these are the three pillars that we really want to showcase as part of a demo today.
The data exchange, the observability that's enabled by the data assurance capabilities of graphene. And finally, Gina, who's going to essentially buy you back time for your workforce to really help them get time to innovate and get business back in on the network infrastructure.
