Published on: April 21, 2025 Updated 2 times since publishing
SafetyDetectives recently sat down with Marty Sprinzen, the visionary Co-Founder and CEO behind Vantiq, a company that’s reshaping the future of enterprise systems through real-time, AI-native platforms. In a world where seconds matter—from emergency response to critical infrastructure—Sprinzen believes the old software paradigms simply can’t keep up. While much of the tech industry is distracted by avatars and novelty GenAI features, Vantiq is building systems that sense, decide, and act in real time. In this interview, Sprinzen explains why traditional SaaS platforms are falling short, how generative AI can actually tackle systemic challenges like climate change, and what it really takes for enterprises to move from experimentation to transformation.
What sparked the creation of Vantiq and how does it address a gap other AI platforms miss
Most enterprise systems were built for an orderly, predictable world—but that world is gone. Every second counts when severe weather hits, patients crash or networks go down. A slow response can mean lost revenue or even lost lives. Legacy systems simply can’t keep up with these high-stakes moments.
We built Vantiq to change that. Traditional platforms are fine at analyzing what happened yesterday, but they can’t react to what’s happening now. Vantiq fills this gap by sensing real-world events as they occur, understanding the context, and taking action immediately. No waiting for someone to run a report or file a request—Vantiq responds on the spot. That’s the shift from slow, reactive operations to true real-time responsiveness.
Legacy SaaS platforms like Salesforce and Workday were game-changers in their time. Where are they now falling short, and what does the next generation of enterprise tech need to do differently?
Salesforce and Workday helped organize business workflows in a simpler time. Processes were linear, people made the calls, and decisions could wait. That world is over.
Now, companies are managing global supply chains, responding to emergencies, and running hospitals and public safety systems where every second counts. These environments are complex, distributed, and constantly shifting. Legacy platforms—designed for static, cloud-centric operations—weren’t built for this.
Modern systems must automate and operate in real time and at the edge. They need to sense, analyze and act instantly, without waiting for human input or a round-trip to the cloud. And they must run wherever the action is—on-site, in the field, across geographies—not just in data centers or headquarters.
This is the new world of edge-native, real-time intelligence. Systems can’t just track decisions anymore. They have to make them—proactively, autonomously and at the speed of now.
Many companies are still fixated on avatars and assistants when it comes to GenAI. Why is that a dead-end for serious enterprise applications?
Most of what’s being built with generative AI today is focused on assistants that write emails or avatars that mimic people. That’s Interesting, maybe even entertaining–but it doesn’t solve meaningful problems at scale.
In critical environments—like emergency response, industrial operations, or infrastructure management—you don’t need a chatbot. You need a system that can perceive what’s happening, interpret complex signals and take decisive action in real time.
You’re not going to re-route ambulances, track down a public safety threat or respond to a national weather disaster with an avatar. The real value of generative AI isn’t cosmetic—it’s operational. It’s about enhancing the intelligence and responsiveness of real-world systems.
At Vantiq, we’re not experimenting with novelty—we’re operationalizing generative AI for mission-critical use cases where downtime, delay, or error isn’t an option. It’s about safety, continuity, speed, and trust. Generative AI makes a real difference not by mimicking people, but by augmenting how systems think, react, and adapt—automatically prompting GenAI and applying its output for the right intelligence at exactly the right moment, with the right context.
In what ways can generative AI be applied to help address climate change or other large-scale systemic challenges?
Climate change isn’t a single issue–it’s a chain reaction across energy, infrastructure, food systems, and public health. Solving it requires more than isolated innovations. It demands systems that can operate in real time, collaborate across sectors, and respond before disruptions become disasters.
Generative AI can help make sense of complex, high-velocity data and anticipate what’s coming. But insight alone isn’t enough. The value lies in action. Think: a smart grid that dynamically shifts energy to prevent blackouts. A flood monitoring system that mobilizes response before streets are under water. A wildfire detection network that not only senses danger, but coordinates first responders, reroutes traffic, and initiates evacuations—automatically.
These are the kinds of intelligent systems that can change outcomes. But they won’t come from layering AI onto outdated infrastructure. They require platforms built for real-time orchestration—systems that can sense, decide, and act without human lag.
We already have the data—live, streaming from sensors, systems, and devices. We have the compute to process it instantly. What’s missing is the connective tissue: a real-time intelligence layer that integrates with existing systems and turns insight into action.
The climate crisis doesn’t wait for reports or dashboards. It demands systems that can sense, decide, and act without delay—before streets flood, before wildfires spread, before the grid fails.
This isn’t about replacing infrastructure—it’s about making it smarter, faster, and more adaptive. That’s the true role of generative AI in the enterprise: not just content creation, but orchestrated, real-world response at scale.
And that’s exactly where Vantiq is focused—bridging the gap between awareness and action, and enabling intelligent systems to respond when it matters most.
Enterprise adoption of AI often comes with big promises but slow progress. What hard truths do organizations need to face if they want to move beyond experiments and into true transformation?
The hard truth is this: most enterprises aren’t failing because of AI—they’re failing because of architecture. You can’t bolt AI onto legacy systems built for static workflows and expect real transformation. It doesn’t scale, it doesn’t adapt, and it certainly doesn’t move fast enough.
We work with global organizations that have hit the ceiling of what traditional systems can do. They understand that AI isn’t a feature—it’s a new operational model. That means rethinking how decisions are made, removing handoffs, and designing systems to think, react, and adapt in real-time.
It also means confronting questions of control. As AI takes on more responsibility, leaders need clear oversight—who owns the outcomes, how decisions are audited, and how governance keeps pace without becoming a bottleneck.
If you’re serious about transformation, you have to rebuild the foundation. Automating yesterday’s processes won’t get you where you need to go—and it won’t protect you from the risks of moving fast without visibility.
How could AI-native systems have changed the outcome of the California wildfires, and what needs to happen to make that kind of response a reality?
Wildfires and floods don’t wait. By the time an alert goes out, it’s often too late. That’s the failure of systems designed to react after the fact—or worse, ones that rely on humans to coordinate everything in the heat of the moment.
Agentic AI-native systems flip that model. They can monitor live data, detect early threats, alert responders, and mobilize resources—automatically, within seconds. They fuse inputs from sensors, satellites, and weather models to act before the situation escalates.
The technology exists. What’s missing is the mindset. Until we treat real-time software as critical infrastructure for public safety and healthcare emergencies and the likes, we’ll keep responding too late—and paying the price.