Physical environments are becoming increasingly data-rich, yet they remain surprisingly difficult to interpret in real time. Large venues like stadiums, airports, and convention centers generate constant movement and crowd fluctuations, but operators often lack immediate visibility into how people are distributed across spaces or how congestion is forming. The result is a gap between what is happening on the ground and what decision-makers can actually see and act on.
Following recent developments in AI-driven spatial intelligence and computer vision, a growing number of companies are pushing beyond traditional surveillance and static analytics toward real-time understanding of human movement.
WaitTime is part of this transition.
The Detroit-based company turns cameras and AI into real-time insights on how people move through physical spaces. Its technology has been deployed across sports and entertainment venues, including collaborations tied to major organizations such as Manchester City and Cisco at Etihad Stadium, where crowd intelligence tools were introduced to help improve fan movement, venue visibility, and operational efficiency through real-time analytics. The same AI-driven framework is being expanded into convention centers through collaborations with Intel, TD SYNNEX, HPE, and Cox, helping operators monitor occupancy, congestion, and movement patterns in real time.
Conversations with the CEO
We spoke with WaitTime’s founder and CEO, Zachary Klima, about the origins of the company, the core problem it set out to solve, and how its real-time crowd intelligence platform is shaping the future of physical space analytics across venues worldwide.
Q: What originally inspired the creation of WaitTime, and what problem were you aiming to solve?
A: WaitTime was born from a very simple observation: large venues, airports, convention centers, and retailers were making massive operational decisions without truly understanding what was happening with people movement in real time.
Ironically, one of the moments that really sparked the idea happened while I was attending a hockey game. I stepped away to grab a beer, got stuck in an incredibly long concession line, and ended up missing the game-winning goal. That moment stuck with me because it highlighted how something as simple as a line could completely impact the fan experience.
At the time, most operators had very little visibility into what was actually happening operationally across their environments in real time. Cameras were everywhere, but they were mostly being used as passive recording devices rather than intelligent operational tools. Back in 2014, we saw an opportunity to change that — to transform existing camera infrastructure into a live intelligence layer capable of measuring crowd flow, queue dynamics, bottlenecks, wait times, and guest experience in real time.
The original problem we aimed to solve was friction. Nobody likes waiting in long lines, entering overcrowded spaces, or operating blindly during high-volume moments. We believed AI computer vision could fundamentally improve both operational efficiency and customer experience at scale, while remaining completely anonymous and privacy-first.
Q: What do you believe differentiates WaitTime’s technology from more traditional traffic or occupancy monitoring solutions?
A: The biggest differentiator is that WaitTime was architected from day one as a real-time operational intelligence platform — not just a counting system. Traditional occupancy or traffic solutions often focus on basic people counts or historical reporting. WaitTime goes much deeper by understanding live crowd behavior dynamically as environments change second by second.
Our patented AI measures queue length, wait times, crowd density, flow patterns, entry speed, directional movement, and operational bottlenecks in real time across highly complex environments. Another major differentiator is our edge-first architecture. Our platform runs entirely on Intel CPU infrastructure at the edge without requiring GPUs, which dramatically improves scalability, deployment simplicity, cost efficiency, and global adoption potential.
And importantly, we built the company around anonymous AI. No facial recognition. No biometrics. No personally identifiable information. That philosophy became increasingly important as the industry evolved.
Q: Are there any particular innovations or patented technologies you believe are especially novel or important to highlight?
A: Absolutely. One of the areas we are particularly proud of is the depth of our patented algorithmic intelligence developed over more than a decade.
Our queue management algorithms are designed to operate in some of the most challenging high-density environments in the world — stadium gates, convention centers, airports, transportation hubs, and massive public venues where crowd dynamics are constantly changing. We also developed patented massing and density intelligence capable of measuring live occupancy and crowd conditions across extremely large physical environments using advanced computer vision techniques and Z-angle camera perspectives.
Another innovation that’s very important is our ability to generate highly accurate operational intelligence entirely on CPUs at the edge. The industry often assumes advanced AI requires large GPU infrastructure, but we’ve proven that scalable edge AI can operate efficiently on existing enterprise compute environments powered by Intel architecture. That changes the economics of deployment dramatically and makes AI far more accessible globally.
Q: How important has innovation and intellectual property strategy been in supporting WaitTime’s growth?
A: It has been foundational. From the beginning, we understood that building truly differentiated AI technology would require long-term thinking, deep R&D investment, and defensible intellectual property.
We spent years refining our algorithms, training models, and solving highly complex operational challenges before AI became the mainstream topic it is today. That early investment created significant technological advantages that continue to differentiate WaitTime globally.
Our intellectual property strategy has also helped validate our credibility with major enterprise partners, global technology companies, distributors, and integrators. When companies like Intel spend years evaluating AI technologies and ultimately align strategically with your platform, it reinforces the importance of building real innovation rather than short-term hype.
Patents matter because they represent years of problem solving, iteration, and category creation.
WaitTime’s key patent
Turning camera data Into live queue estimates
In our previous article, we already featured WaitTime’s patent titled, “Techniques for automatic real-time calculation of user wait times”, which uses overhead images to calculate real-time wait times for users in a line at a venue, allowing users to make informed decisions about whether to join a line or wait for a shorter queue.U.S. Patent No. 10,902,441 describes a computer-based method for estimating real-time wait times in queues using overhead cameras and image processing. A camera mounted above a venue captures a top-down view of people standing in line, such as at stadiums, amusement parks, restrooms, or concession areas. The images are sent to a computing system that analyzes the movement and position of individuals in the line.
The system identifies a target person, typically the second person in line, and tracks their progress as they move to the front and then exit the line. It records the time taken for this transition and uses it as a measure of how fast the line is moving. To estimate overall wait time, the system multiplies this measured time by the number of people remaining in line after the target person leaves.
To improve accuracy, the system filters and processes the camera images to distinguish actual queue participants from nearby pedestrians or background objects. People moving too quickly are excluded on the assumption they are not waiting in line. The system may also combine multiple camera views into a single image and correct for lens distortion, especially when wide-angle or fisheye cameras are used. In addition to tracking wait times, it can monitor crowd density, spacing between individuals, and overall congestion within the area.
The calculated wait times and related metrics can then be shared with mobile apps, digital signage, or analytics platforms, helping both users and venue operators make better real-time decisions about crowd flow and service efficiency.
Other inventors include Thomas Sterling, John Mars, JR., and Doyle Mosher.
WaitTime’s vision for real-time intelligence
We wrapped our conversation with Zack by asking him how he sees AI-driven real-time analytics evolving over the next few years.
He described it as “still the very early innings of physical-world AI intelligence,” and pointed to a coming shift where cameras, sensors, networks, and infrastructure move from passive tools into fully intelligent operational systems.
According to Zack, organizations are only now beginning to recognize the scale of data already embedded in their environments. The next step, he explained, is not collection but activation, turning that infrastructure into real-time intelligence that can drive decisions automatically. He added that AI-driven analytics will increasingly move beyond reporting into prediction and autonomy. Instead of simply showing what has already happened, systems will begin to anticipate congestion, dynamically adjust staffing, reroute crowds in real time, and improve safety and customer experience proactively.
A major enabler of this shift, he noted, is edge AI. “Enterprises want lower latency, lower cost, stronger privacy controls, and scalable infrastructure,” Zack said, emphasizing that this is pushing intelligence closer to where data is generated rather than relying solely on centralized cloud processing.
We then asked what most excites him about WaitTime and the broader industry going forward.
Zack highlighted what he sees as a fundamental underappreciated reality, that most physical venues already have the infrastructure in place. Airports, stadiums, convention centers, retail environments, and transportation hubs are already equipped with cameras, but the opportunity lies in extracting real-time operational intelligence from them.
“The question is no longer whether AI can analyze these environments,” he said, “it’s how much operational intelligence can be unlocked in real time.”
Looking ahead, he believes the industry is moving toward a world where software-driven AI becomes a core layer for physical environments globally. For WaitTime specifically, Zack described the intersection of several massive shifts including edge AI, operational intelligence, smart infrastructure, recurring software models, and a rapidly evolving global channel ecosystem.
He closed on a clear note, saying the opportunity ahead is not just about understanding crowds better, but about redefining how physical spaces operate in the AI era.
OUR FEATURED GUEST
ZACK KLIMA
Founder & CEO, WaitTime
Zack Klima is the Founder & CEO of WaitTime, one of the original pioneers in AI computer vision for crowd and queue management. Founded in 2014, long before AI became mainstream, WaitTime has grown into a global leader in patented, ethical, fully anonymous crowd intelligence—powering real-time operational insights for stadiums, airports, convention centers, retail, and large public venues worldwide.
A core part of Zack’s strategy has been building a channel-first global sales motion alongside partners like Intel, enabling scalable, CPU-only edge AI deployments through resellers, integrators, and distributors across the world. With a background in architecture and systems thinking, Zack focuses on building durable technology, empowering channel partners, and transforming cameras into real-time operational intelligence platforms.