Robotic Assistance Devices, LLC. (RAD) - Experts & Thought Leaders

Robotic Assistance Devices, Inc. (RAD), announces the commercial availability of SARA™ Assess, developed in collaboration with Circadian Risk. As SARA’s first commercial integration with a third-party physical risk intelligence platform, SARA Assess extends RAD’s agentic AI beyond its own hardware ecosystem and creates a new software-led entry point for organisations seeking to continuously verify connected security controls, update facility risk scores, and initiate corrective action. SARA has primarily operated as the intelligence and response layer within RAD’s deployed security solutions. Deployed security solutions SARA Assess marks a significant expansion of that role, enabling SARA to work across third party systems and return actionable findings to Circadian Risk’s platform. This allows organisations to add agentic AI capabilities to existing security environments without making a RAD hardware deployment the required point of entry. “This is exactly the kind of expansion we built SARA to support,” said Steve Reinharz, CEO/CTO of AITX and RAD. “SARA is not confined to the devices we manufacture. With SARA Assess, it can work across third-party security systems, interpret operating conditions, initiate supported actions, and return meaningful intelligence to an enterprise risk platform. This first commercial integration opens SARA to organisations that may not yet deploy RAD hardware and demonstrates that our agentic AI platform can create value across a much larger security ecosystem.” Public project tracker SARA Assess is one of 15 active initiatives included in AITX’s product development roadmap and public project tracker. The Company is systematically unveiling these hardware and software projects as their development, testing and release plans are finalised, allowing investors and other stakeholders to follow the roadmap as individual initiatives are introduced. In its initial application, SARA Assess connects with an organisation’s video management system to identify cameras that are offline, obstructed, impaired, or otherwise not performing as intended. SARA communicates those findings to the Circadian Risk platform, where the site’s risk score can be updated and the appropriate task, work order, or supported corrective action initiated. Video management system “Organisations have traditionally had to separate understanding their physical risk from verifying whether their security controls are actually performing,” said Michael Martin, CEO of Circadian Risk. “SARA Assess brings those functions together. RAD’s agentic AI provides the active intelligence needed to check connected systems and drive action, while our platform translates those findings into a current view of risk. The result is a practical solution that helps security teams move faster, prioritise more effectively, and maintain greater confidence in the systems protecting their facilities.” SARA Assess builds upon a longstanding relationship between RAD and Circadian Risk. AITX invested in Circadian Risk in 2023, establishing a foundation for collaboration focused on connecting physical risk intelligence with AI-driven security operations. The commercial launch of SARA Assess advances that relationship through a jointly developed solution now available to end users. SARA Assess is available now for pilot programs and full deployments. The solution will be presented at GSX 2026 in Atlanta, September 14 through 16. Security leaders attending the event are invited to visit Circadian Risk in booth 1747 to meet with Circadian Risk and RAD representatives, see SARA Assess, and discuss its application within their existing security environments.

Artificial Intelligence Technology Solutions, Inc., a pioneer in AI-driven security and productivity solutions, along with its wholly owned subsidiary, Robotic Assistance Devices, Inc. (RAD), announces that it has secured a new direct customer agreement with a global healthcare organisation for an initial deployment of multiple RIO 360™ solar-powered autonomous security solutions. The customer is implementing RAD’s AI-driven security technologies as part of a broader initiative focused on improving security operations and reducing traditional guarding costs across portions of its facility footprint. Security coverage decisions Large organisations operating distributed facilities often face difficult security coverage decisions, particularly at remote, lightly occupied, or lower activity locations where maintaining a dedicated guard presence may not be operationally or financially practical. RAD believes its autonomous security solutions are increasingly being adopted by organisations seeking to improve coverage consistency, reduce operating costs, and create more scalable security operations across broad facility networks. As part of RAD’s platform expansion initiative announced earlier this year, all deployed RIO 360 units now include SARA™, RAD’s Speaking Autonomous Responsive Agent, as a standard integrated feature. SARA is designed to support intelligent detection, communication, escalation, and operational response workflows across RAD’s autonomous security platforms. Improving efficiency and responsiveness “Healthcare infrastructure and related facilities represent a substantial long-term opportunity for RAD,” said Steve Reinharz, CEO/CTO and founder of AITX and RAD. “Organisations operating complex facility environments are actively seeking technologies that can help modernise security operations while improving efficiency and responsiveness. Solutions like RIO can extend visibility and deterrence capabilities into areas where traditional security models may be difficult to justify or consistently maintain.” The Company expects to complete the deployment of the first RIO 360 units in mid-June as part of its initial implementation. RAD indicated that discussions regarding additional locations and broader expansion opportunities are accelerating as the organisation advances its scalable security operations initiative across its facility network. Scalable security operations Due to customer confidentiality agreements, additional details regarding the organisation and deployment scope are not being disclosed at this time. “Large enterprise organisations do not move quickly when evaluating technologies tied to safety, operations, and facility security,” said Troy McCanna, Chief Revenue Officer and Chief Security Officer at RAD. “This opportunity followed an extensive review and vetting process that spanned several months. We believe that successfully earning the confidence of organisations operating at this scale reflects the strength of RAD’s solutions and the consistency of our team’s execution.” The Company invites prospective clients, channel partners, and industry participants to connect with its team to learn how RAD’s solutions can support their security and operational objectives.

Artificial Intelligence Technology Solutions, Inc., a pioneer in AI-driven security and productivity solutions, along with its wholly owned subsidiary, Robotic Assistance Devices Group (RAD-G), now announced that RAD-G has begun invoicing monitoring company clients for SARA™, its proprietary agentic AI platform, marking a decisive shift from pilot programs and proof of concept engagements to live, revenue generating deployments across the remote video monitoring sector. Over the past several months, RAD-G has steadily advanced SARA from industry recognition to preliminary proof of concept, and now to the first phases of operational adoption. Remote video monitoring  SARA (Speaking Autonomous Responsive Agent) is the Company’s multiple award-winning agentic AI platform built for high-volume remote video monitoring. In April 2025, SARA earned top honours at the Security Industry Association New Products and Solutions Awards at ISC West, signalling early validation from industry leaders for the first industry-focused solution. By October 2025, RAD-G had announced multiple monitoring companies signing on to engage SARA proof of concepts, followed by live operational pilots in November 2025. Several completed pilots are now entering into active deployment status, and in December 2025, RAD-G crossed a critical threshold as monitoring clients moved beyond evaluation and into paid licencing. SARA revenue stream The Company believes the SARA revenue stream could represent a significant component of its financial performance for the next fiscal year. “This is the moment where theory ends and execution begins,” said Steve Reinharz, CEO/CTO and founder of AITX and all RAD subsidiaries. “SARA was built to operate at scale inside real monitoring centres. We are not asking the industry to imagine the future. We are delivering it now.” SARA Lite for single call responses SARA is offered in several forms so that clients can adopt autonomous intelligence at the pace and depth that matches their operations. Some begin with SARA Verified to remove false positives and reduce noise, while others move quickly into SARA Lite for single call responses or SARA Agent for full autonomous action. Additional modules such as SARA Assist, SARA Edge and SARA Alarm allow organisations to expand their capabilities without replacing existing systems. This modular approach lets RAD-G scale performance across thousands of feeds and supports long term migration toward fully autonomous operations. The Company invites Remote Video Monitoring, GSOC, and SOC operators interested in learning how SARA can transform their monitoring operations to connect with RAD-G.

The COVID-19 pandemic has presented an unprecedented challenge to businesses. From retail stores to office buildings to warehouses and construction sites, a big question looms: how can landlords, executives, and employers ensure their facilities don’t contribute to the spread of the virus? A low-tech solution - the face mask - has become a leading preventative measure. But, a high-tech solution is necessary to ensure that everyone is wearing them. Cameras powered by artificial intelligence can now identify whether or not people entering a facility are wearing facemasks and help enforce adherence to mask mandates. This technology is proving to be a cost effective solution that reduces risks of confrontations over masks policies and gives managers the data they need to document regulatory compliance and reduce liability. Layers of security They can also be integrated into access control systems or woven into other preventative measures that create overlapping layers of security. These cameras are an ideal solution for low-traffic, remote sites, or areas that are only accessible to employees that need to monitor mask compliance but at which hiring a manned guard is just too expensive. Cameras with mask detection capabilities are especially useful when the technology piggybacks on existing autonomous devices, such as mobile security drones. The premise is simple. When a person without a mask is detected by the autonomous robotic security device, the system can generate, depending on customer preferences, audible and visible alerts to remind people to mask up. It also feeds alerts to a cloud-based data storage system so that security executives can analyse data for trends or quickly locate video of important incidents. Why masks? One study published in the Proceedings of the Royal Society A highlights the benefits of mask usage. If just 50 percent of people use masks, the rate of COVID-19 transmission will slowly decline. If 80 percent of people use them, the rate will plummet. Bu,t people don’t love wearing them. They’re hot. They make eyeglasses foggy. It’s hard to make yourself heard when talking to others. We’re all familiar with industries that wear masks of some type or other, on a regular basis - health care, construction, and heavy industry to name a few. But for the general public, wearing a mask for long periods of time is not a regular habit. For the general public, wearing a mask for long periods of time is not a regular habit We also know that other measures site managers have used to limit the spread of coronavirus are ineffective. For example, at least three meatpacking plants rank among the top 50 locations for coronavirus clusters. One factor driving that spread: many employees, to avoid missing a day’s pay, masked their mild fevers with ibuprofen to fool the infrared temperature scanners that employers used to protect against the outbreak. The paradox of masks, however, isn’t that they protect the wearer from infection. It’s the other way around: when an infected person wearing a mask sneezes, coughs, or breathes, they don’t spread the virus as far, and thus masks slow the spread of the virus from infected people, including those that are not showing symptoms. Prove it One of the very reasons why county and state governments have instituted mask orders is simple: it’s an easily verifiable sign that an organisation is taking steps to limit the spread of coronavirus. Mask detection cameras, coupled with autonomous security systems, can provide the documentation employers need to ensure mask compliance. Imagine, for example, a warehouse full of manual laborers. The county orders everyone to wear a mask any time they leave home. A disgruntled employee, recently terminated, files an anonymous complaint to local health officials stating that the warehouse isn’t enforcing mask compliance - or worse, preventing employees from wearing masks to prevent theft. The county sends an inspector. Mask detection cameras provide site managers with the documentation they need to disprove these allegations. The autonomous systems developed by RAD will feed video footage into a cloud database, documenting not only the instances of non-compliance, but also the instances of compliance - with the mask clearly highlighted. Any inspector that arrives on a job site can see hours and hours of footage, without having to pour through hours of video. Reducing confrontation We’ve all seen the videos in which angry shoppers confront retail clerks and security guards over mask usage. In some cases, these confrontations have turned violent, resulting in injury or death. For every one of these videos, there may well be hundreds of others. While most of the videos featuring mask confrontations focus on retail settings, manned guards also face challenges in enforcement. Confrontations over mask usage have the potential to drive up workman’s compensation claims higher when guards are injured. Because autonomous security units generate alerts automatically, the chance of confrontation is minimised. It’s easy to imagine a couple of scenarios in which autonomous units can be beneficial. In health care settings, where emotions run high, autonomous devices can serve as a force multiplier for patrolling guards in parking areas. For example, roving units can identify people that are not wearing masks, and remind them to do so before they enter the building. These can also be placed in entryways that generate alerts as visitors approach doors. In many buildings, mask detection systems can be integrated into access control systems Autonomous security units can be deployed for a fraction of the cost of manned security. In healthcare, autonomous units can be used to re-allocate security spending, placing less emphasis on low intensity guards whose primary function is to observe and report - particularly those that patrol parking garages - and more emphasis on trained professionals capable of defusing confrontations inside the hospital. In other words, autonomous units outside allow facilities to hire better quality inside, where confrontations are most likely to take place. In many buildings, mask detection systems can be integrated into access control systems, which might be especially useful at entrances that are not manned by security, but accessible via key card. Changing behaviours There was a time when smoking in public was not seen as particularly anti-social. Almost everyone will stop at a stop sign, even when we can see for miles in every direction, and we know that the risk of an accident is zero. We do these things because we have been trained to. These behaviours make us safer, but we didn’t adopt them overnight. Many of us forget, but the fight over banning smoking in bars and restaurants was filled with confrontation. So, too, will it be with mask compliance. But time is short, and we all need to do everything we can to encourage good behaviour. Mask detection technology can do that, and these solutions are very cost effective. In some cases, the cost may be just 5 percent of using a manned guard. They’re effective too. Autonomous systems enforce mask policies consistently and drive accountability. That can make us all safer.

If you’ve been paying attention over the last twelve months, you will have noticed that deep learning techniques and artificial intelligence (AI) are making waves in the physical security market, with manufacturers eagerly adopting these buzzwords at the industry's biggest trade shows. With all the hype, security professionals are curious to know what these terms really mean, and how these technologies can boost real-world security system performance. The growing number of applications of deep learning technology and AI in physical security is a clear indication that these are more than a passing fad. This review of some of our most comprehensive articles on these topics shows that AI is an all-pervasive trend that the physical security industry will do well to embrace quickly. Here, we examine the opportunities that artificial intelligence presents for smart security applications, and look back at how some of the leading security companies are adapting to respond to rapidly-changing expectations: What is deep learning technology? Machine Learning involves collecting large amounts of data related to a problem, training a model using this data and employing this model to process new data. Recently, there have been huge advances in a branch of Machine Learning called Deep Learning. This describes a family of algorithms based on neural networks. These algorithms are able to learn efficiently from example, and subsequently apply this learning to new data. Here, Zvika Ashani explains how deep learning technology can boost video surveillance systems. Relationship between deep learning and artificial intelligence With deep learning, you can show a computer many different images and it will "learn" to distinguish the differences. This is the "training" phase. After the neural network learns about the data, it can then use "inference" to interpret new data based on what it has learned. For example, if it has seen enough cats before, the system will know when a new image is a cat. In effect, the system “learns” by looking at lots of data to achieve artificial intelligence (AI). Larry Anderson explores how new computer hardware - the Graphic Processing Unit (GPU) – is making artificial intelligence accessible to the security industry. Improving surveillance efficiency and accuracy with AI Larry Anderson explains how the latest technologies from Neurala and Motorola will enable the addition of AI to existing products, changing an existing solution from a passive sensor to a device that is “active in its thinking.” The technology is already being added to existing Motorola body-worn-cameras to enable police officers to more efficiently search for objects or persons of interest. In surveillance applications, AI could eliminate the need for humans to do repetitive or boring work, such as look at hours of video footage. Intelligent security systems overcome smart city surveillance challenges AI technology is expected to answer the pressing industry questions of how to use Big Data effectively and make a return on the investment in expensive storage, while maintaining (or even lowering) human capital costs. However, until recently, these expectations have been limited by factors such as a limited ability to learn, and high ongoing costs. Zvika Ashani examines how these challenges are being met and overcome, making artificial intelligence the standard in Smart City surveillance deployments. Combining AI and robotics to enhance security operations With the abilities afforded by AI, robots can navigate any designated area autonomously to keep an eye out for suspicious behaviour or alert first responders to those who may need aid. This also means that fewer law enforcement and/or security personnel will have be pulled from surrounding areas. While drones still require a human operator to chart their flight paths, the evolution of artificial intelligence (AI) is increasing the capabilities of these machines to work autonomously, says Steve Reinharz. Future of artificial intelligence in the security industry Contributors to SourceSecurity.com have been eager to embrace artificial intelligence and its ability to make video analytics more accurate and effective. Manufacturers predicted that deep learning technology could provide unprecedented insight into human behaviour, allowing video systems to more accurately monitor and predict crime. They also noted how cloud-based systems hold an advantage for deep learning video analytics. All in all, manufacturers are hoping that AI will provide scalable solutions across a range of vertical markets. 

The reviews are in, and ISC West was another hit. Brisk attendance and a comprehensive lineup of the industry’s top companies and products contributed to another successful show for Reed Exhibitions. Our Expert Panel Roundtable, who have attended many such events, added their own reflections to the industry’s post-ISC glow. We asked this week’s Expert Panel Roundtable: How successful was ISC West 2018 for security industry exhibitors and visitors?