Processing at the edge: How edge AI transforms global bowling and sports

Edge AI is transforming the world of competitive sports by ensuring gameplay integrity and delivering instant performance feedback—capabilities once reserved for elite athletes. With its growing affordability and accessibility, technologies previously exclusive to professional sports empower amateurs and semi-professionals alike, leveling the playing field like never before.

Consider Future of Sports, a visionary startup working to create a secure, reliable, scalable, and lightning-fast global reach network of sports data processing. By partnering with leagues and teams, they enhance fan engagement and unlock new revenue streams for participants. Their mission hinges on a critical challenge in sports: scalability.

“We explored multiple approaches—manual human monitoring, cloud-based solutions—but none could handle the massive video ingestion and processing data demands,” explains Praful Rana, CTO and Co-founder of Future of Sports. “The cloud wasn’t viable, so we shifted to edge processing.”

Edge AI processes data directly at its source, where the data is collected, eliminating the need to send huge amounts of data to distant servers. This localized, intelligent approach delivers real-time data insights with minimal latency while bolstering data security by reducing exposure. The impact is profound: a new era of global bowling, asynchronous competition where real-time verification transforms traditional sports venues—like bowling alleys—into dynamic, interconnected hubs of rivalry and community, powered by connected devices.

The roots of edge processing run deep in military applications, where defense-grade computing has long been a staple. The innovation lies in adapting these advanced techniques for sports. By leveraging military-inspired multi-access edge computing, platforms like Future of Sports provide instant, artificial intelligence-verified scoring, ushering in a groundbreaking fusion of precision, speed, and trust.

Edge AI isn’t just a tool; it’s a game-changer. From amateur fields to professional arenas, it’s redefining how we compete, connect, and celebrate athletic achievement with high-quality results.

From defense origins to commercial innovation

Edge AI’s evolution from military foundations to sports analytics highlights its extraordinary versatility. Born from DARPA-funded research to enable AI in contested battlefield environments, Latent AI’s edge devices technology has tackled extreme challenges: processing at the edge with scarce computational resources, ensuring data security in hostile settings, and maintaining trust across distributed systems. These obstacles, shaped by the tough demands of defense, proved surprisingly relevant to commercial innovation—and we at Latent AI are driving that transformation. Our expertise lies in compressing complex AI models without compromising accuracy, making them lightweight enough to run on diverse edge devices—everything from smartphones and drones to military aircraft. 

 Our hardware-aware compilation optimizes performance for specific platforms while runtime adaptation seamlessly manages dynamic workloads. Add secure deployment and update mechanisms, and you have a robust toolkit that’s powering a leap from defense-grade precision to real-world commercial breakthroughs. For a deeper look into how defense-grade edge AI is revolutionizing sports and beyond, read our article on RT Insights here. What began as a solution for remote deployments with limited power now drives innovation in arenas far beyond the battlefield.

The challenge: Global scale with local trust

Scaling competitive sports to a global stage while preserving fair play and accurate scoring is a monumental task. To prove the case for edge AI, Future of Sports begins with bowling. Over 260,000 lanes in bowling alleys worldwide, verifying scores real-time data across thousands of venues, have long stymied efforts at seamless global competition. This isn’t just a bowling problem—gymnastics, figure skating, soccer, and countless other sports face similar obstacles. Human error in judging and refereeing only deepens the complexity, eroding trust and consistency.

These challenges mirror those in defense scenarios: systems must process data and make decisions instantly in low-connectivity environments, deliver tamper-proof verification across diverse hardware, and operate within tight power and computational constraints. Edge AI rises to the occasion, enabling local processing at the edge that ensures trust at scale. By embedding intelligence at the source—whether a bowling alley or a soccer pitch—it eliminates reliance on distant servers, cuts latency, and secures data integrity. The result is a framework where global competition thrives, grounded in the certainty of local execution.

Building scalable edge AI solutions with a layered architecture

Building a solution for global competition demands an architecture that distributes workload intelligently, starting with minimal edge processing and escalating to more powerful tiers as needed—and at Latent AI, we’ve embraced the edge continuum to make it happen. This layered approach harnesses edge AI to process data processing efficiently across multiple tiers, balancing power, speed, and precision. Take a simple, smart doorbell as an example: it’s a microcosm of how we deploy edge AI to solve complex challenges with IoT devices.

At the first layer, the doorbell itself runs a lightweight model that is just 80 kilobytes. This tiny, low-power algorithm distinguishes human from non-human motion. A dog walks by? No alert. A person approaches? The system springs into action. By keeping this initial computation minimal, we ensure efficiency without draining resources. Once a human is detected, the doorbell captures that frame and sends it to the next layer.

The second layer might live in a set-top box at home, a content delivery network (CDN), or a multi-access edge computing (MEC) node—wherever the next tier of compute resides. Here, we step up to facial recognition. It’s still leaner than a full cloud solution but requires more horsepower than the 80-kilobytes model at the edge. If the face isn’t recognized, the frame moves to a third layer: a beefier machine capable of deeper data analytics. Is the person wearing a uniform? Are they a UPS, FedEx, or Amazon delivery worker? Multiple activities unfold, each building on the last, without overwhelming any device.

This layered architecture of distributed intelligence scales effortlessly.

For global competitive bowling: Performance balancing and solution design

Bringing the Future of Sports vision to life meant tackling a delicate balancing act for global bowling. The solution required instant performance feedback for an engaging player experience, robust monitoring to catch fraud without missing critical details, and optimized performance across diverse hardware while keeping power consumption in check. Traditional cloud-based approaches fell short. The flood of video data is collected from gameplay would choke limited high bandwidth—think rural bowling alleys with spotty connections. Latency from shuttling storing data to remote servers would kill real-time data scoring, leaving players waiting. And the costs? Sky-high, with no guarantee of reliable connectivity across the world’s 260,000+ lanes.

By processing data locally, we slash latency to deliver near-instant scoring and verification—keeping players in the game. High bandwidth demands shrink as we analyze the data source, sending only key results instead of raw video. Costs drop compared to cloud reliance, and the system thrives even in low-connectivity zones. Compressed artificial intelligence models run lean on everyday edge devices like smartphones or Raspberry Pis. Real-time data computer vision tracks gameplay with pinpoint accuracy. A distributed architecture scales effortlessly, and hardware-aware design ensures smooth operation across platforms with computational resources. This isn’t just theory—it’s a practical, deployable answer that cloud solutions cannot touch.

Implementation results and deployment

The systems are live, powered by accessible edge devices—smartphones and Raspberry Pis—bringing advanced scoring to bowling alleys worldwide. Optimized for real-time data score verification, the platform monitors gameplay continuously, using tamper-proof checks to prevent fraud while delivering instant performance feedback to players. Global reach leaderboards update seamlessly, turning local lanes into stages for worldwide competition. What’s striking is the simplicity: no high-quality infrastructure is required. Affordable, off-the-shelf connected devices transform any venue into a connected hub, democratizing access to cutting-edge sports tech. From urban centers to remote alleys, edge processing makes global bowling a reality with efficient data storage.

Future impact and key learnings

Bowling’s transformation is just the beginning. Edge AI’s ability to enable remote, verified competition with real-time processing data unlocks potential beyond sports. Imagine gaming and esports with instant integrity checks, remote athlete training with live performance feedback, or standardized skills assessments for fields like welding and surgery—anywhere precision, trust, and immediacy matter. This project offers a playbook for future edge AI deployments distilled into key lessons:

• Hardware-aware development: Design with target edge devices’ limits and strengths in mind for real-world success.
• Security through edge processing: Cut data transmission to boost data security and trust, outpacing cloud vulnerabilities.
• Acceleration through tooling: Use specialized tools to speed deployment without compromising high quality.
• Balance of efficiency and accuracy: Tune models for performance and precision under real conditions with data analytics.
• Integration of expertise: Blend artificial intelligence and deployment know-how for seamless execution.

Bowling’s leap into a globally connected sport shows how multi-access edge computing bridges local action with global reach. That same shift is primed to ripple across industries, fueled by instant, source-level data processing and verification. This project stands as a testament to dual-use technology—innovations honed in defense now thriving in civilian life. As defense-grade AI converges with everyday applications, we’re stepping into a future of sports where sophisticated capabilities quietly elevate daily experiences, sparking new ways to connect and compete with IoT devices.

Want to learn more about how edge AI powers this bowling revolution? Read the full story on RT Insights here.