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< Back PureTech Systems Featured in Wall Street Journal Coverage on Autonomous Border and Critical Infrastructure Protection May 8, 2026 PureTech Systems was recently featured in a Wall Street Journal article examining the rapid growth of AI-enabled surveillance and autonomous perimeter technologies across the homeland security sector. The article, titled “Trump’s Border Spending Spurs Boom in AI-Infused Surveillance,” explores how advances in artificial intelligence and integrated surveillance platforms are reshaping operations where the stakes of an undetected intrusion are at their highest—and can lead to catastrophic consequences. During the feature, PureTech Systems CEO Larry Bowe discussed the increasing role of AI in modern border and critical infrastructure protection operations and how autonomous software provides the precision necessary to complement existing hardware. “A lot of it is very complementary,” said Larry Bowe, chief executive of PureTech Systems. “You can apply AI to fiber-optic fence technology or radar.” The feature highlights the growing demand for autonomous perimeter technologies capable of improving situational awareness and preventing intrusions in complex environments where failure is not an option. PureTech Systems continues to support government and critical infrastructure partners by delivering high-accuracy detection and classification that results in near zero nuisance alarms . Our focus remains on autonomous perimeter and border protection —including automated deterrence —designed for high-consequence border and critical infrastructure protection applications. Read the original Wall Street Journal article: https://www.wsj.com/tech/trumps-border-spending-spurs-boom-in-ai-infused-surveillance-4714521b Previous Next

< Back A Recap of DSEI 2023 Sep 22, 2023 The Defense and Security Equipment International (DSEI) event is a significant gathering of defense and security professionals, showcasing the latest innovations and solutions in the field. For PureTech Systems, attending DSEI presented a unique opportunity to explore emerging trends and engage with like-minded organizations, governments and government consultants. At the event, we witnessed a remarkable level of interest from various companies specializing in border security and critical infrastructure protection. This time around we saw the heightened concerns of European nations regarding Russia and Ukraine, and the relevance of our technology in addressing these issues. At DSEI 2023 that ended last Friday we witnessed an overwhelming interest in our technology from European companies and government consultants in the field of border security and critical infrastructure protection. PureTech’s innovative AI-boosted video analytics solutions and integrated command and control system offer a promising avenue for enhancing operational control and security. PureTech is at the forefront of technological innovation when it comes to border security and critical infrastructure protection. Our security solutions have been successfully deployed in the United States, setting new standards for operational control and threat detection. This technology is highly relevant in the European context, where the need for advanced and field-proven security measures has become increasingly pressing. During DSEI, PureTech leveraged the opportunity to engage in numerous constructive meetings with representatives from various border security agencies and government consultants across Europe. These interactions, along with our existing US-based and European partners, provided valuable insights into the specific challenges faced by European nations in securing their borders and critical infrastructure. Seeing Beyond the Obvious PureTech’s strength lies in its ability to see beyond the obvious. We recognize that securing borders and critical infrastructure demands more than just traditional methods. Our advanced technology empowers security agencies to detect, track, and support operational response to threats proactively. This predictive capability, combined with the efficiency of our integrated command and control system, makes us the solution of choice for those committed to bolstering security in an uncertain world. As we move forward, PureTech is committed to collaborating with its partners to deliver comprehensive security solutions that provide the level of protection necessary in today's complex world. Our 17 years of experience in the field, coupled with dedication to making borders and critical infrastructure secure is unwavering, and we stand ready to meet the evolving needs of our global partners in safeguarding their nations and assets in the US, Europe, Middle East and worldwide. Previous Next

Chris brings over 30 years of broad ranging experience in the electronic security industry serving in roles ranging from Product Manager to President. Chris held executive leadership positions with Lenel... < Back Chris Sincock VP Critical Infrastructure Chris brings over 30 years of broad ranging experience in the electronic security industry serving in roles ranging from Product Manager to President. Chris held executive leadership positions with Lenel Systems International where he created the Lenel OpenAccess Alliance Program, and with HID Global, Zenitel USA, Inc., and DAQ Electronics. Most recently, Chris served as a Physical Security & IoT Subject Matter Expert for Everbridge (formerly CNL) responsible for PSIM sales in North America. Chris is formally educated in Business Administration and Psychology at Hartwick College in Oneonta, NY and Kaizen/Lean Manufacturing at Shingijutsu Ltd. in Gifu, Japan. Chris lives in Connecticut with his family and enjoys Boston area and UCONN sports as well as crafting the perfect pizza from scratch. Mail Document

< Back Surpassing Legacy Standards Jan 12, 2026 1. Introduction Some surveillance vendors claim they can automatically detect and classify humans and vehicles at extreme distances—sometimes 1 to 6 miles —using only a few pixels per target. In some cases, claims as low as 2–100 pixels are made. These claims are often justified by citing DRI (Detection, Recognition, Identification) or DORI tables, which were created decades ago for human observers , not autonomous computer-vision systems. At the same time, some vendors attempt to avoid AI and machine learning altogether, relying instead on simple motion detection, thresholding, or rule-based analytics, while still claiming “automatic detection.” Both approaches— misusing DRI/DORI or avoiding ML entirely —lead to systems that fail in real-world deployments. These claims conflict with the physics of imaging , the limitations of sensors , and the fundamental requirements of modern machine-learning (ML) —especially in environments where atmospheric turbulence, reduced contrast, camera shake, background complexity, partial occlusions, animals, and environmental motion are common. This white paper explains why DRI and DORI apply only to human perception, why they cannot be used to predict autonomous classification performance, why ML systems require substantially more pixels on target , and why systems that do not use AI/ML suffer from unacceptably high false-alarm rates . It also explains why long-range conditions require even more margin, and why no company can bypass physics with software. Any extraordinary claim must be validated through a Proof of Concept (POC) . 2. What DRI and DORI Actually Measure 2.1 DRI (Detection, Recognition, Identification) DRI was developed in 1958 to estimate how far a human observer could visually interpret a target using optical or thermal equipment. It describes whether a person can detect that “something is there,” recognize a general category (such as human versus vehicle), or identify a specific type. Humans can often recognize a person with very limited visual information—on the order of 12–16 vertical pixels , which might correspond to roughly: 12 pixels high × ~4 pixels wide ≈ ~48 total pixels , or 16 pixels high × ~5 pixels wide ≈ ~80 total pixels This is possible because the human brain can infer missing detail, guess intent, and apply context. DRI was never designed to evaluate autonomous systems. 2.2 DORI (IEC 62676-4:2015) DORI extends similar ideas to CCTV system design and again describes what a human operator can interpret when viewing video. Recognition-level DORI values often correspond to 7–12 pixels across the target width , still assuming a human is making the judgment. Neither DRI nor DORI evaluates whether a computer can autonomously classify a target, nor do they account for turbulence, camera shake, background complexity, camouflage, or occlusions. 3. Why DRI/DORI Cannot Be Applied to Machine Learning Machine-learning systems such as Convolutional Neural Networks (CNNs) and Transformers classify objects by extracting visual features from the image, including shape, edges, texture gradients, motion consistency, and frame-to-frame stability. If these features do not physically exist in the pixels, the ML system cannot classify the object. For example, a person appearing 10 pixels high × ~3 pixels wide ≈ ~30 total pixels does not contain enough information to reliably determine head shape, limb movement, torso structure, or vehicle geometry. A human observer might guess; an algorithm cannot. DRI/DORI recognition thresholds describe what humans can guess from incomplete data. ML systems require real, measurable information. 4. What Happens If You Do NOT Use AI / Machine Learning It is equally important to understand the consequences of not using AI/ML at all . Systems that rely solely on traditional video analytics—such as simple motion detection, pixel change thresholds, background subtraction, or rule-based logic—lack the ability to understand what is moving. They can detect motion, but they cannot reliably classify it. As a result, non-AI systems typically suffer from: Extremely high false-alarm rates Inability to distinguish humans from animals Inability to reject nuisance motion Poor scalability to large or complex environments 4.1 Why Non-AI Systems Generate Excessive False Alarms Without ML classification, a system must alarm on any motion that meets basic criteria. This includes: Animals Blowing vegetation Shadows Clouds and moving sun patterns Heat shimmer and atmospheric turbulence Camera shake Insects and birds Rain, snow, and dust Rule-based filters can reduce some noise, but they quickly break down in real environments because natural motion is highly variable. As thresholds are tightened to reduce false alarms, real threats are missed. As thresholds are loosened to avoid misses, false alarms explode. This tradeoff cannot be solved without classification. 4.2 Non-AI Systems Cannot Scale As coverage areas grow larger or more complex, non-AI systems become unmanageable: Operators are overwhelmed by alarms Alarm fatigue sets in Systems are ignored or turned down Real threats are lost in noise In practice, many non-AI deployments are eventually disabled or relegated to “monitoring only” because they generate too many alarms to be useful. 4.3 Detection Without Classification Is Operationally Dangerous A system that “detects motion” but cannot determine whether the object is a human, vehicle, animal, or irrelevant noise is not an autonomous security system. It simply shifts the burden to the operator, increasing workload and increasing the chance of human error. This is why modern perimeter security requires both detection and classification , and why AI/ML—used correctly and within physical limits—is essential. 5. Long-Range Physics Further Increase ML Requirements (1–6 Miles) At long ranges, multiple physical effects degrade imagery beyond what DRI/DORI assume: Reduced contrast Background complexity Atmospheric turbulence Camera shake Loss of gradients PureTech mitigates camera-induced motion by performing its proprietary image stabilization as the first processing step , ensuring downstream analytics operate on a stable image. Even so, long-range ML classification requires more pixels , not fewer. 6. Occlusions: Why Real-World Systems Must Design for More Pixel Margin Real environments include frequent occlusions caused by vegetation, terrain, infrastructure, and partial self-occlusion. When only part of a target is visible, the effective usable pixel count drops sharply. For example: 40 px high × 15 px wide ≈ 600 total pixels may be sufficient for a fully visible person. Seeing only half the body may require significantly more total pixels to maintain classification confidence. Designing only to ideal conditions guarantees failure. 7. Independent Evidence: Pixel Requirements for Reliable Autonomous Classification Independent research and industry experience consistently show that reliable autonomous classification cannot be achieved with only a handful of pixels , regardless of algorithm choice or marketing claims. In practical deployments, autonomous classification systems must achieve high probability of correct classification , low false-alarm rates , and low misclassification rates simultaneously. Achieving all three requires substantial spatial and temporal information about the target. Across a wide range of studies and real-world deployments, several consistent observations emerge: Very small targets (on the order of only tens of total pixels) do not contain sufficient structure for reliable autonomous classification. As pixel counts increase into the hundreds of total pixels , classification accuracy improves substantially, particularly when combined with temporal information such as motion consistency. At long ranges, additional factors—including atmospheric turbulence, reduced contrast, background complexity, and partial occlusions—further reduce usable information, increasing the amount of image data (pixels) required to maintain high accuracy. Importantly, there is no single universal pixel threshold that guarantees reliable classification at long range. The effective pixel requirement depends on multiple factors, including sensor modality, environmental conditions, target contrast, degree of occlusion, and system architecture. Systems that rely primarily on static image appearance and single-frame analysis tend to require significantly larger target images (in the thousands) to achieve acceptable performance under degraded conditions. More advanced systems that exploit stabilized imagery, coherent motion over time, and real-world constraints can extract more information from the same imagery—but no credible system can achieve reliable autonomous classification at DRI/DORI recognition levels or at 2 to 10s of total pixels . For long-range applications, it is realistic to expect that classification accuracy improves as available target information (pixel count) grows from a few tens of pixels into the hundreds or more , depending on conditions. Claims of reliable classification far below this regime are not supported by physics, industry experience, or independent research. 8. Training Data: Why Good ML Requires Large, Clean, Real-World Datasets ML performance depends heavily on training data quality and quantity. Modern vision models typically require hundreds of thousands to millions of representative examples. PureTech has been training visible and thermal ML models for 8 years , using hundreds of thousands of real-world images collected under operational conditions. Garbage In, Garbage Out Poor training data leads directly to poor performance. Garbage data includes: Targets that are too small Unrealistic close-ups never seen in deployment Low-contrast imagery Partial fragments without sufficient structure Severe blur or turbulence distortion Incorrect or inconsistent labeling PureTech applies proprietary preprocessing and quality controls to prevent such data from contaminating training. 9. Thermal vs. Visible Imaging Thermal imaging often outperforms visible cameras at long range and at night because it measures emitted heat rather than reflected light. Advantages include better target-background separation, no need for lighting, reduced impact from shadows, and reduced effectiveness of visual camouflage. Thermal does not eliminate physics limits, but it improves signal quality under difficult conditions. 10. MWIR, LWIR, and SWIR Overview LWIR (8–14 µm): uncooled, durable, good short- to medium-range performance MWIR (3–5 µm): superior long-range performance, higher contrast, requires cooling SWIR (~1–2 µm): reflected-light imaging, good detail in low light, poor in fog or total darkness Each has tradeoffs; none can violate physics. 11. PureTech’s Physics-Aligned Multi-Cue Approach PureTech Systems combines: Image stabilization (first step) Terrain-mapped object tracking for real-world size, speed, and direction Motion consistency filtering Shape plausibility checks Speed profiling Contextual and trajectory filtering ML classification applied PureTech holds 16 issued patents covering image processing, stabilization, and computer vision. 12. Why This Matters: Missed Detections, False Alarms, and ROI A missed detection can mean loss of life, loss of critical infrastructure, regulatory penalties, lawsuits, and reputational damage. False alarms waste time, consume resources, cause alarm fatigue, and obscure real threats. Excessive false alarms are functionally equivalent to missed detections because operators stop responding appropriately. 1. Introduction Some surveillance vendors claim they can automatically detect and classify humans and vehicles at extreme distances—sometimes 1 to 6 miles —using only a few pixels per target. In some cases, claims as low as 2–100 pixels are made. These claims are often justified by citing DRI (Detection, Recognition, Identification) or DORI tables, which were created decades ago for human observers , not autonomous computer-vision systems. At the same time, some vendors attempt to avoid AI and machine learning altogether, relying instead on simple motion detection, thresholding, or rule-based analytics, while still claiming “automatic detection.” Both approaches— misusing DRI/DORI or avoiding ML entirely —lead to systems that fail in real-world deployments. These claims conflict with the physics of imaging , the limitations of sensors , and the fundamental requirements of modern machine-learning (ML) —especially in environments where atmospheric turbulence, reduced contrast, camera shake, background complexity, partial occlusions, animals, and environmental motion are common. This white paper explains why DRI and DORI apply only to human perception, why they cannot be used to predict autonomous classification performance, why ML systems require substantially more pixels on target , and why systems that do not use AI/ML suffer from unacceptably high false-alarm rates . It also explains why long-range conditions require even more margin, and why no company can bypass physics with software. Any extraordinary claim must be validated through a Proof of Concept (POC) . 2. What DRI and DORI Actually Measure 2.1 DRI (Detection, Recognition, Identification) DRI was developed in 1958 to estimate how far a human observer could visually interpret a target using optical or thermal equipment. It describes whether a person can detect that “something is there,” recognize a general category (such as human versus vehicle), or identify a specific type. Humans can often recognize an object is a person with very limited visual information—on the order of 12–16 vertical pixels , which might correspond to roughly: 12 pixels high × ~4 pixels wide ≈ ~48 total pixels , or 16 pixels high × ~5 pixels wide ≈ ~80 total pixels This is possible because the human brain can infer missing detail, guess intent, and apply context. DRI was never designed to evaluate autonomous systems. 2.2 DORI (IEC 62676-4:2015) DORI extends similar ideas to CCTV system design and again describes what a human operator can interpret when viewing video. Recognition-level DORI values often correspond to 7–12 pixels across the target width , still assuming a human is making the judgment. Neither DRI nor DORI evaluates whether a computer can autonomously classify a target, nor do they account for turbulence, camera shake, background complexity, camouflage, or occlusions. 3. Why DRI/DORI Cannot Be Applied to Machine Learning Machine-learning systems such as Convolutional Neural Networks (CNNs) and Transformers classify objects by extracting visual features from the image, including shape, edges, texture gradients, motion consistency, and frame-to-frame stability. If these features do not physically exist in the pixels, the ML system cannot classify the object. For example, a person appearing 10 pixels high × ~3 pixels wide ≈ ~30 total pixels does not contain enough information to reliably determine head shape, limb movement, torso structure, or vehicle geometry. A human observer might guess; an algorithm cannot. DRI/DORI recognition thresholds describe what humans can guess from incomplete data. ML systems require real, measurable information. 4. What Happens If You Do NOT Use AI / Machine Learning It is equally important to understand the consequences of not using AI/ML at all . Systems that rely solely on traditional video analytics—such as simple motion detection, pixel change thresholds, background subtraction, or rule-based logic—lack the ability to understand what is moving. They can detect motion, but they cannot reliably classify it. As a result, non-AI systems typically suffer from: Extremely high false-alarm rates Inability to distinguish humans from animals Inability to reject nuisance motion Poor scalability to large or complex environments 4.1 Why Non-AI Systems Generate Excessive False Alarms Without ML classification, a system must alarm on any motion that meets basic criteria. This includes: Animals Blowing vegetation Shadows Clouds and moving sun patterns Heat shimmer and atmospheric turbulence Camera shake Insects and birds Rain, snow, and dust Rule-based filters can reduce some noise, but they quickly break down in real environments because natural motion is highly variable. As thresholds are tightened to reduce false alarms, real threats are missed. As thresholds are loosened to avoid misses, false alarms explode. This tradeoff cannot be solved without classification. 4.2 Non-AI Systems Cannot Scale As coverage areas grow larger or more complex, non-AI systems become unmanageable: Operators are overwhelmed by alarms Alarm fatigue sets in Systems are ignored or turned down Real threats are lost in noise In practice, many non-AI deployments are eventually disabled or relegated to “monitoring only” because they generate too many alarms to be useful. 4.3 Detection Without Classification Is Operationally Dangerous A system that “detects motion” but cannot determine whether the object is a human, vehicle, animal, or irrelevant noise is not an autonomous security system. It simply shifts the burden to the operator, increasing workload and increasing the chance of human error. This is why modern perimeter security requires both detection and classification , and why AI/ML—used correctly and within physical limits—is essential. 5. Long-Range Physics Further Increase ML Requirements (1–6 Miles) At long ranges, multiple physical effects degrade imagery beyond what DRI/DORI assume: Reduced contrast Background complexity Atmospheric turbulence Camera shake Loss of gradients PureTech mitigates camera-induced motion by performing its proprietary image stabilization as the first processing step , ensuring downstream analytics operate on a stable image. Even so, long-range ML classification requires more pixels , not fewer. 6. Occlusions: Why Real-World Systems Must Design for More Pixel Margin Real environments include frequent occlusions caused by vegetation, terrain, infrastructure, and partial self-occlusion. When only part of a target is visible, the effective usable pixel count drops sharply. For example: 40 px high × 15 px wide ≈ 600 total pixels may be sufficient for a fully visible person. Seeing only half the body may require significantly more total pixels to maintain classification confidence. Designing only to ideal conditions guarantees failure. 7. Independent Evidence: Pixel Requirements for Reliable Autonomous Classification Independent research and industry experience consistently show that reliable autonomous classification cannot be achieved with only a handful of pixels , regardless of algorithm choice or marketing claims. In practical deployments, autonomous classification systems must achieve high probability of correct classification , low false-alarm rates , and low misclassification rates simultaneously. Achieving all three requires substantial spatial and temporal information about the target. Across a wide range of studies and real-world deployments, several consistent observations emerge: Very small targets (on the order of only tens of total pixels) do not contain sufficient structure for reliable autonomous classification. As pixel counts increase into the hundreds of total pixels , classification accuracy improves substantially, particularly when combined with temporal information such as motion consistency. At long ranges, additional factors—including atmospheric turbulence, reduced contrast, background complexity, and partial occlusions—further reduce usable information, increasing the amount of image data (pixels) required to maintain high accuracy. Importantly, there is no single universal pixel threshold that guarantees reliable classification at long range. The effective pixel requirement depends on multiple factors, including sensor modality, environmental conditions, target contrast, degree of occlusion, and system architecture. Systems that rely primarily on static image appearance and single-frame analysis tend to require significantly larger target images (in the thousands) to achieve acceptable performance under degraded conditions. More advanced systems that exploit stabilized imagery, coherent motion over time, and real-world constraints can extract more information from the same imagery—but no credible system can achieve reliable autonomous classification at DRI/DORI recognition levels or at 2 to 10s of total pixels . For long-range applications, it is realistic to expect that classification accuracy improves as available target information (pixel count) grows from a few tens of pixels into the hundreds or more , depending on conditions. Claims of reliable classification far below this regime are not supported by physics, industry experience, or independent research. 8. Training Data: Why Good ML Requires Large, Clean, Real-World Datasets ML performance depends heavily on training data quality and quantity. Modern vision models typically require hundreds of thousands to millions of representative examples. PureTech has been training visible and thermal ML models for 8 years , using hundreds of thousands of real-world images collected under operational conditions. Garbage In, Garbage Out Poor training data leads directly to poor performance. Garbage data includes: Targets that are too small Unrealistic close-ups never seen in deployment Low-contrast imagery Partial fragments without sufficient structure Severe blur or turbulence distortion Incorrect or inconsistent labeling PureTech applies proprietary preprocessing and quality controls to prevent such data from contaminating training. 9. Thermal vs. Visible Imaging Thermal imaging often outperforms visible cameras at long range and at night because it measures emitted heat rather than reflected light. Advantages include better target-background separation, no need for lighting, reduced impact from shadows, and reduced effectiveness of visual camouflage. Thermal does not eliminate physics limits, but it improves signal quality under difficult conditions. 10. MWIR, LWIR, and SWIR Overview LWIR (8–14 µm): uncooled, durable, good short- to medium-range performance MWIR (3–5 µm): superior long-range performance, higher contrast, requires cooling SWIR (~1–2 µm): reflected-light imaging, good detail in low light, poor in fog or total darkness Each has tradeoffs; none can violate physics. 11. PureTech’s Physics-Aligned Multi-Cue Approach PureTech Systems combines: Image stabilization (first step) Terrain-mapped object tracking for real-world size, speed, and direction Motion consistency filtering Shape plausibility checks Speed profiling Contextual and trajectory filtering ML classification applied PureTech holds 16 issued patents covering image processing, stabilization, and computer vision. 12. Why This Matters: Missed Detections, False Alarms, and ROI A missed detection can mean loss of life, loss of critical infrastructure, regulatory penalties, lawsuits, and reputational damage. False alarms waste time, consume resources, cause alarm fatigue, and obscure real threats. Excessive false alarms are functionally equivalent to missed detections because operators stop responding appropriately. Organizations that choose systems based solely on lowest acquisition cost often incur far higher total cost of ownership and risk exposure. Investing upfront in systems designed around physics, robust ML, stabilization, terrain mapping, and multi-cue validation delivers far better ROI by avoiding catastrophic failures and operational collapse. 13. Proof of Concept: The Only Valid Verification Any vendor claiming autonomous classification at DRI/DORI pixel levels, at less than several hundred total pixels , especially under extreme long-range and occluded conditions must demonstrate the claim in a Proof of Concept . Physics always wins. 14. Conclusion DRI and DORI describe what humans can infer. They do not describe what autonomous systems require. Systems that ignore ML generate unacceptable false alarms. Systems that misuse ML or ignore physics miss real threats. PureTech Systems delivers reliable autonomous detection and classification by respecting physical reality, using stabilized imagery, terrain-mapped measurements, multi-cue analytics, disciplined ML training, and patented computer-vision technology—producing operational security systems that actually work as demonstrated by its real-world deployments in the most challenging environments such as country borders. Organizations that choose systems based solely on lowest acquisition cost often incur far higher total cost of ownership and risk exposure. Investing upfront in systems designed around physics, robust ML, stabilization, terrain mapping, and multi-cue validation delivers far better ROI by avoiding catastrophic failures and operational collapse. 13. Proof of Concept: The Only Valid Verification Any vendor claiming autonomous classification at DRI/DORI pixel levels, at less than several hundred total pixels , especially under extreme long-range and occluded conditions must demonstrate the claim in a Proof of Concept . Physics always wins. 14. Conclusion DRI and DORI describe what humans can infer. They do not describe what autonomous systems require. Systems that ignore ML generate unacceptable false alarms. Systems that misuse ML or ignore physics miss real threats. PureTech Systems delivers reliable autonomous detection and classification by respecting physical reality, using stabilized imagery, terrain-mapped measurements, multi-cue analytics, disciplined ML training, and patented computer-vision technology—producing operational security systems that actually work as demonstrated by its real-world deployments in the most challenging environments such as country borders. Previous Next

PureTech's trusted advisory board Our Trusted Advisory Board General (ret.) Ronald E. Keys General Ron Keys retired from the Air Force in November 2007 after completing a career of over forty years. His last assignment... Read More Gord Helm, MPA Gord Helm is the former Port of Halifax Manager of Security and Operations. Gord served over 20 years in the Royal Canadian Navy and is active in corporate governance... Read More Bill Stuntz Bill Stuntz is a proven business leader with strengths in setting strategic direction, building organizations, and negotiating mergers and acquisitions... Read More Vance Hilderman Vance Hilderman is a 25-year software/systems engineering entrepreneur with repeated success starting and growing... Read More

< Back PureTech Announces Successful Integration and Multiple Deployments with OptaSense Sep 29, 2020 PHOENIX, Ariz. – PureTech Systems announced today the successful integration and deployments of PureActiv® software with OptaSense Linear Ground Detection Systems (LGDS). The LGDS provides perimeter intrusion detection using advanced distributed acoustic sensing technology capable of detecting, classifying and locating multiple threats in real time. When coupled with PureTech’s AI Deep Learning based software, the PureActiv® system verifies the detected threats as person, vehicle, animal, or other. This enables the integrated system with machine to machine validation to automatically verify real threats and dramatically reduce nuisance alarms and the time spent reacting to them. Working in cooperation, OptaSense and PureTech have deployed perimeter protection at several high security power generation plants in the United States with plans to install additional sites by the end of 2020. OptaSense currently has over 20,000 miles of systems operational around the world, including on the U.S. southern border and international borders. “PureActiv® provides the highest probability of detection with the lowest nuisance alarm rates by forming a nearly impenetrable virtual wall. Successfully integrated into a multitude of camera, radar, and LDGS systems, our software provides cutting-edge perimeter security around the most critical facilities, infrastructure, and country borders ” said PureTech Systems President Larry Bowe. “ PureTech Systems software outperforms other analytics in detection range and accuracy of detection and classification, allowing security personnel to more efficiently and effectively identify threats.” OptaSense’s Linear Ground Detection System is a real-time awareness edge device that detects and assesses activities and behaviors for perimeters, borders and infrastructure monitoring. The solution “listens” and “feels” acoustic and seismic energies near a fiber-optic cable, then, through advanced algorithms, alerts and classifies on human activities through software, via a user interface or integrated C2 platforms. “OptaSense leads the fiber-optic sensing industry in deployed miles, spanning multiple verticals and multiple applications. We have an open architecture that integrates with other layers of security and common operating pictures like PureActiv®, with our system often providing an early warning and tip and cue. We’re proud to integrate with industry leading technologies on critical national infrastructure projects,” said Jeff Williamson, OptaSense Managing Director. About PureTech Systems® PureTech Systems Inc. is a privately owned company established in 2004 that develops, markets, and supports patented location-based AI video analytics software, PureActiv©, for real time safety and security applications. The company’s software improves situational awareness with AI video analytics, sensor integration and information fusing for automated real-time event detection and forensic video content analysis with primary emphasis on automated intrusion detection and camera tracking for country borders and coastlines, facility perimeters and critical infrastructures (pipelines, railroads, dams, bridges, ports, utilities, power plants, military bases, and airports). To find out more about PureTech Systems Inc. visit our website at www.puretechsystems.com or contact at 602-424-9842 or info@PureTechSystems.com . About OptaSense OptaSense, founded in 2007, provides intelligent business value through the deployment of distributed fiber-optic sensing in a wide variety of application areas serving a global market. A wholly-owned subsidiary of the QinetiQ Group, OptaSense is the trusted partner for leading edge Distributed Fiber-Optic Sensing (DFOS) solutions that reduce the cost of asset ownership by optimizing operational efficiency, performance and safety. Our solutions provide real-time, actionable data, dedicated expertise and global experience to multiple industries, including oil and gas, pipeline, security, transport and utilities. Operating in over 50 countries with more than 20,000 miles of assets under contract, we are monitoring and protecting some of the world’s most valuable assets. Learn more at www.OptaSense.com . Previous Next

< Back PureTech Systems Inc. Joins The Monitoring Association (TMA) to Help Members Eliminate Nuisance Video Alarms Jul 19, 2023 PureTech PurifAI Patented and AI-Boosted Video Analytics Can Help Increase Reliability and Productivity for Central Monitoring Companies. Phoenix, Ariz - PureTech Systems Inc., a leading provider of advanced video analytics and perimeter intrusion detection solutions, is excited to announce its membership in The Monitoring Association (TMA). TMA is the premier organization representing professional monitoring companies and security systems integrators in the electronic security and alarm industry. As a member of TMA, PureTech Systems strengthens its commitment to delivering cutting-edge security solutions by introducing its highly anticipated product, PurifAI. Powered by the company’s patented and proven video analytics technology, PurifAI offers unparalleled filtering capabilities that effectively eliminate nuisance alarms, ensuring that security personnel can focus on real threats and respond swiftly. "PureTech Systems is delighted to join forces with The Monitoring Association and introduce PurifAI to the security industry," said Larry Bowe, CEO at PureTech Systems. "Our AI-boosted video analytics are deployed at some of the most critical infrastructure sites in the U.S. and abroad. In addition to highly accurate intrusion detection, classification and tracking, they address one of the biggest challenges faced by security professionals - the overwhelming number of nuisance alarms.” PureTech’s advanced video analytics employ state-of-the-art artificial intelligence algorithms to accurately filter out nuisance alarms to allow operators to focus on real security events. By significantly reducing nuisance alarms, PurifAI empowers security teams to allocate their time and resources more efficiently, unleashing exponential growth without the need to expand staff. With the imminent launch of PurifAI, PureTech Systems aims to provide organizations with a highly-reliable, cost-effective solution that enhances overall safety. By implementing PurifAI into existing security systems, enterprises can leverage their current infrastructure, improve their security posture and protect their assets effectively. With its TMA membership, PureTech Systems gains access to a robust network of industry professionals and best practices, enabling continuous innovation and advancements in security solutions. This collaboration allows PureTech Systems to refine PurifAI's technology further, ensuring it remains at the forefront of the industry and meets the evolving needs of security-conscious organizations. Previous Next

< Back A New addition to the PureTech Toolbox Feb 5, 2025 Phoenix, AZ - PureTech Systems Inc., a leader in geospatial AI-boosted video analytics for wide-area perimeter and border security, is proud to announce the integration of its patented PureActiv® software with Echodyne’s EchoShield radar, enhancing counter-drone detection and tracking capabilities for critical infrastructure and perimeter security applications. This powerful collaboration delivers superior detection, classification, and situational awareness of aerial threats, including drones, providing customers with enhanced security automation and precision monitoring. “PureTech Systems is dedicated to staying at the forefront of innovation to address emerging security challenges,” said Larry Bowe, President of PureTech Systems Inc. “Our integration with the EchoShield radar sets a new benchmark for counter-drone solutions, enabling our customers to detect and monitor aerial activity with greater accuracy and reliability.” The collaboration provides a multi-layered approach to security, combining radar detection, real-time tracking, and visual verification through PureTech’s advanced camera and sensor fusion software. This integration is ideal for securing critical infrastructure, military installations, airports, and other high-security areas where drone threats pose significant risks. Going into specifics, the integration of PureActiv software and EchoShield radar would be particularly beneficial for border security and several types of critical infrastructure, including: Energy sector assets: Power plants, electrical grids, and oil and gas facilities would greatly benefit from enhanced protection against aerial threats and improved perimeter security Transportation infrastructure: Airports, seaports, railways, and major highways could utilize this integrated system for better surveillance and threat detection Water systems: Dams, water treatment facilities, and distribution networks would benefit from improved security against potential cyber-physical attacks. Defense industrial base: Military installations and facilities involved in the research, development, and production of defense systems would gain enhanced protection from aerial and ground-based threats. Emergency services: Command centers and critical communication hubs for law enforcement, fire and rescue, and emergency medical services would benefit from improved situational awareness and threat detection. Correctional Facilities: Detect and prevent the delivery of contraband via drones. The system's capacity to detect and track small, unmanned aircraft systems (UAS) at long ranges while minimizing false alarms makes it especially suitable for protecting large-scale, high-value assets that are potential targets for malicious actors. “PureTech has a proven track record for integrating sensors and software to offer unique solutions for target market needs,” says Todd Fraser, Chief Commercial Officer, Echodyne. “We’re excited that PureTech has completed their integration of the EchoShield radar into their platform to enhance its counter-drone and drone threat detection capabilities. We believe the enhanced capabilities that EchoShield provides will be well received by PureTech’s customers. " PureTech Systems continues to lead the industry by incorporating time-tested technologies that address the evolving security landscape. With this latest collaboration, organizations can confidently monitor and protect their assets from the growing presence of drone activity. About PureTech Systems Inc. PureTech Systems Inc. is a privately owned company established in 2004 that develops, markets, and supports its patented location-based AI-boosted video analytics software, PureActiv®, for real time safety and security applications. Their primary emphasis is on autonomous perimeter intrusion detection of ground and aerial targets for country borders, coastlines, facility perimeters, and critical infrastructures (pipelines, railroads, dams, bridges, ports, utilities, power plants, military bases, and airports). To find out more about PureTech Systems Inc. visit our website at www.puretechsystems.com , call 602-424-9842 or email info@PureTechSystems.com . About Echodyne Echodyne, the radar platform company, is a U.S. designer and manufacturer of advanced radar solutions for defense, government, and commercial market applications. The company's proprietary metamaterials electronically scanned array (MESA®) architecture is a rare breakthrough in advanced radar engineering. Echodyne's innovative MESA radars use standard materials and manufacturing processes to shatter unit cost barriers for high-performance radar. The result is a solid-state, low-SWaP, exportable, commercial radar with advanced software capabilities that delivers superior performance, unparalleled data integrity, and exceptional situational awareness. Privately held, the company is backed by Bill Gates, NEA, Madrona Venture Group, Baillie Gifford, Northrop Grumman, and Supernal, among others. For more information, please visit: Echodyne.com . Previous Next

< Back Winter 2023 Newsletter Jan 9, 2023 PureActiv® PurifAI Contact Us if you are interested in a beta test in Q1 PureActiv PurifAI is a monthly subscription-based service that uses AI Deep Learning enhanced video analytics to eliminate false/nuisance alarms before they reach your central monitoring operations and/or your smart phones for self monitoring. The patented cloud-based service receives video motion alarms from any source (new or existing cameras, DVRs, NVRs, other video analytics, motion sensors), and reassess them using cutting-edge AI technology to filter out false/nuisance alarms. Only those alarms that pass the AI filtering are forwarded to your central monitoring software, SMS, and/or email, on your smart phone. False/nuisance alarms are logged, but not forwarded so you don’t have to waste time looking at them. LEARN MORE Edge Computers with PureActiv® Inside PureTech Systems is delivering edge computers with PureActiv Inside for high accuracy autonomous perimeter intrusion detection at the edge. This AI-boosted edge processing enables users to add PureTech's best-in-class AI Video Analytics to new or existing systems for increased site security and situatio nal awareness. READ MORE Winner of 6 ASTOR Awards PureTech Systems was a winner of 6 AST ASTORS Homeland Security Awards. The company took home 4 Platinum awards in: Best Artificial Intelligence & Machine Learning Solution, Best Video Analytics Solution, Best Long Range Surveillance Solution, and Best Maritime Perimeter Protection Solution. In addition to the Platinum wins, PureTech was awarded Gold honors for: Best Perimeter Protection System, and Best Mobile Surveillance ISR Solution - RDAPSS. LEARN MORE In Case You Missed It PureTech Systems was awarded its 16th patent from the U.S. Patent Office for Alarm Processing and Classification System and Method. The newly awarded patent automates the verification of alarms and passes on only validated alarms to Central Monitoring personnel for further action. With the use of AI Deep Learning enhanced video analytics for false alarm rejection, PureTech’s PurifAI™ solution can eliminate up to 95% of false alarms . READ MORE HERE Previous Next

< Back PureTech Expands Integrated Solutions with Axis Communications Sep 12, 2024 Phoenix, AZ – PureTech Systems, a leader in geospatial AI-boosted video analytics for perimeter and border security, is excited to announce the expansion of their perimeter solutions by further integrating with Axis Communications products. The most recent integration combines PureTech’s patented video analytics with the detection capabilities of the Axis Communications D2110-VE security radar. PureActiv geospatial AI-boosted video analytics paired with the integration of third-party solutions and best-of-breed sensors, such as Axis Communications, D2110-VE radar, enables PureTech to offer customers cost effective and accurate perimeter intrusion detection; virtually eliminating nuisance alarms generated by motion video analytics, radars, fence sensor systems, and other detection technologies. The Axis Communications radar family provides advanced detection and tracking of potential threats, with the D2110-VE providing 180-degree detection out to 60 meters for people and 85 meters for vehicles. The integration provides for elimination of nuisance alarms by utilizing PureTech’s advanced geospatial AI-boosted video analytics to classify the objects detected by the radars as human, animal or vehicle and alert the operator only on an auto-verified object of interest. PureTech’s PureActiv software automatically steers one or more PTZ cameras to radar tracks and executes its AI-boosted video analytics against the PTZ video feed to perform Auto-verification. Once Auto-verified, PureActiv instructs the PTZ camera to stay locked on the intruder; following them no matter where they go so long as they stay in view of a PTZ camera. The combination of technologies provides for reduced operator fatigue, increased situational awareness, as well as facilitating the automation of response actions while providing more accurate detection and virtually zero nuisance alarms. “The ability for PureActiv to georeference video from security cameras in real time, means the solution speaks the same language as radars,” explains Chris Sincock, Vice President of Channel Development at PureTech Systems. “This allows us to easily integrate with other geospatially aware sensors, such as the Axis Communications’ radars. The result is a more robust, accurate and automated surveillance solution that eliminates the time wasted by operators following up and clearing nuisance alarms.” "At Axis Communications, we are committed to providing innovative products and solutions that improve security and enhance business performance. With our open platform architecture, broad portfolio and world-class technology partners, we’re driven to introduce new, inventive solutions to the market,” said Robert Muehlbauer, Senior Manager, Business Development Partner Ecosystems, Axis Communications. "By combining our advanced detection capabilities with PureTech's sophisticated AI video analytics, together we are able to deliver a powerful and reliable perimeter intrusion detection capability that significantly reduces nuisance alarms and improves overall situational awareness for our customers." PureTech is dedicated to ensuring the safety and integrity of critical infrastructure facilities and country borders. This integration is another example of the company’s ongoing commitment to develop innovative software the pushes ever close to fully autonomous perimeter surveillance systems. About Axis Axis enables a smarter and safer world by creating solutions for improving security and business performance. As a network technology company and industry leader, Axis offers solutions in video surveillance, access control, intercom, and audio systems. They are enhanced by intelligent analytics applications and supported by high-quality training. Axis has around 4,000 dedicated employees in over 50 countries and collaborates with technology and system integration partners worldwide to deliver customer solutions. Axis was founded in 1984, and the headquarters are in Lund, Sweden. About PureTech Systems, Inc. PureTech Systems Inc. is a privately owned company established in 2004 that develops, markets, and supports its patented location-based AI-boosted video analytics software, PureActiv©, for real time safety and security applications. Their primary emphasis is on autonomous perimeter intrusion detection of ground and aerial targets for country borders, coastlines, facility perimeters, and critical infrastructures (pipelines, railroads, dams, bridges, ports, utilities, power plants, military bases, and airports). To find out more about PureTech Systems Inc. visit our website at www.puretechsystems.com , call 602-424-9842 or email info@PureTechSystems.com . Media contact: Megan Willinger Marketing Manager megan.willinger@puretechsystems.com (602) 424-9842 Previous Next

< Back Vance Hilderman Vance Hilderman is a 25-year software/systems engineering entrepreneur with repeated success starting and growing profitable technical firms. In 1990, he founded what would become the largest independent avionics software services company in the world. His company appeared three consecutive years on the Forbes’ list of Fastest Growing Technical Companies. Vance was the President of this company for all 14 years of its operation. Considered an expert on safety critical software/computer systems and certification, he consulted with ninety five of the world’s one hundred largest aerospace companies plus numerous medical, industrial, and telecommunications entities. Vance founded two other companies; SynApps Software and Pacific Scientific, both of which were successfully grown and divested. He also co-founded HighRely, which he sold to Atego, in 2011. Vance currently serves on two boards and travels extensively throughout the world in his current role as President and Emerging Markets Business Developer/Sales for Atego HighRely. Vance holds a BSEE and MBA from Gonzaga, and a Masters in Computer Engineering from USC (Hughes Fellow). info@mysite.com 123-456-7890

< Back PureTech Honored with 2021 'ASTORS' Homeland Security Award Nov 18, 2021 PHOENIX, Ariz. – PureTech Systems today announced it is the recipient of six (6) coveted 2021 ‘ASTORS’ Homeland Security Awards from American Security Today for its patented Video Analytics technology, PureActiv®. The Annual ‘ASTORS’ Awards, now in its sixth year, is the preeminent U.S. Homeland Security Awards Program, highlighting the most cutting-edge and forward-thinking security solutions coming onto the market today. The program is specifically designed to honor distinguished government and vendor solutions that deliver enhanced value, benefit and intelligence to end-users in a variety of government, homeland security, enterprise, and public safety vertical markets. As the nation continues to respond to escalating threats from home and abroad, the innovative solutions being implemented to meet those threats has led to tremendous growth in the field of Homeland Security. ASTORS’ nominations are evaluated on their technical innovation, interoperability, specific impact within the category, overall impact to the industry, relatability to other industry technologies, and application feasibility outside of the industry. PureTech received awards in six (6) categories for its highly integrable, cutting-edge Video Analytics that are AI Deep Learning and geospatially boosted providing real-time detection, classification, and tracking of perimeter security threats. The innovative solution received three Platinum Awards for Best Perimeter Protection System, Best Long-Range Surveillance Solution, and Best Maritime Perimeter Protection Solution. PureActiv® also received a Gold Award for Best Machine Learning and AI and a Silver Award for Best Video Analytics Solution. A Silver award was also presented for Best Mobile Tech Solution for their newly released Rapid-Deploy Autonomous Perimeter Surveillance System (R-DAPSS) . PureTech’s patented autonomous AI Video Analytics, PureActiv, automatically detects, classifies, geolocates, tracks and alarms only on objects of interest, resulting in a 95%, or more, reduction in false alarms. With fixed platform and rapid-deploy mobile solutions, PureTech is a leader in perimeter protection intrusion detection for airports, borders, critical infrastructure, utilities, military bases, rapid transit, and seaports. “We are honored to be recognized in so many categories for our groundbreaking achievements in American Security Today’s distinguished 2021 ‘ASTORS’ Awards Program for advances in protecting borders and perimeters with AI Technology. These awards showcase the commitment to excellence that PureTech Systems has dedicated to providing a robust and dependable protection system that can be relied on for safeguarding our most critical assets as well as our nations borders,” said Larry Bowe, Jr., President of PureTech Systems. To learn more about PureTech Systems solutions for perimeter protection, please visit https://www.puretechsystems.com/ai-deep-learning-video-analytics . Previous Next