Processing and Scaling AI at the Edge with Pathr.ai

By George Shaw, Founder and CEO at Pathr.ai 

With the tremendous growth of deploying artificial intelligence (AI) and Internet of Things (IoT) applications across billions of devices, huge amounts of data are being connected and generated. Edge computing has become increasingly popular and beneficial for companies focused on delivering real-time analytics, protecting data privacy, and reducing latency. According to the IDC, the worldwide edge computing market will reach $250.6 billion in 2024, helping companies unlock new business opportunities, while maintaining data security and privacy.

At Pathr.ai, we use edge computing to deliver real-time spatial intelligence insights into human behavior in physical spaces through anonymous location data. We do this by integrating our software into existing infrastructure, such as security cameras and other sensors, and delivering real-time analytics back to our customers, enabling them to drive the business results that matter most to them. We work closely with IT teams to access their location’s cameras, video management systems (VMS), and other types of sensors such as access control, and deploy our AI-powered spatial intelligence solution. Here, I explore three main advantages of edge computing and how Pathr.ai deploys its technology at the edge in a scalable and cost efficient approach, securely, and through real-time processing.

Scalability and Cost Efficiency 

The rising need for edge computing is accelerated by the deployment of AI and IoT applications across billions of devices. When data is locally processed at the edge, data transfer doesn’t become a bottleneck, as less congestion and strain are placed on the network. This leads to accelerated solutions that IT teams can scale on their existing infrastructure, rather than purchasing new servers in locations they wish to expand processing power. Further, processing at the edge can deliver significant cost reductions to companies who require large amounts of data to be processed and analyzed. When scaling a solution across 1000 locations, cloud computing can prove costly when purchasing large capacities from a cloud server. Even though edge computing requires local computing power, it is a much more cost-effective solution to scale AI.

With Pathr.ai, our software integrates directly with a physical space’s existing infrastructure and is capable of scaling across multiple locations, nationwide and globally. IT teams don’t need to purchase or install additional hardware inside their locations. They can simply use the existing assets they have in place to acquire rich insights into human behavior and interactions, which may lead to additional revenue generation opportunities for their company. With the power of edge computing, we’re able to process large amounts of data in real-time and scale our solution to other locations that IT teams operate from.

Real-time Processing

Time is money and for many companies, having real-time analytics delivered to them can boost decision-making. Edge AI computing allows for fast processing and storing of data, making it possible to achieve low latency levels and improve real-time performance. This functionality can improve business outcomes around fraud prevention, customer experiences, and staff optimizations. As many sensors and IoT devices are located at the edge, edge AI computing delivers high-performance compute power in an IoT-enabled network.

Pathr.ai’s spatial intelligence software utilizes machine learning and computer vision models to connect human movement relative to business drivers of a client. These tasks run more efficiently at the edge, as they are closer to the data source and reduce latency. Edge processing begins at the data ingestion stage, where our proprietary software, Sensor Layer v2.0, detects, tracks, and projects flows in single and multi-camera settings. The anonymous data then enters our Behavior Engine, which creates quantifiable analytic insights for our clients. By processing locally at the edge, we deliver real-time analytics that prompt immediate decision making from our clients.

Maintaining Data Privacy

Data privacy has increasingly become a critical topic in today’s world. Any time sensitive data is transferred over networks or through cloud computing, there is vulnerability in theft and online attacks. Therefore, processing at the edge enables companies to keep their data private and secure on their own protected networks. For IT teams who are constantly monitoring data security and privacy on their infrastructure, edge AI computing reduces the risk of mishandled data that would otherwise be sent to the cloud.

At Pathr.ai, our stack operates locally at the edge, ensuring that any video or data captured by existing cameras and devices never leaves the client’s site and preserves the data sensitivity. As captured video or data passes through our system, our software removes all personally identifiable information (PII) of an individual and displays human movement simply as a “dot” on a floor plan, never retaining any video data without explicit client approval. By functioning in real-time at the edge, Pathr.ai ensures that no PII leaves the client’s site without their permissions, maintaining personal privacy and complying with GDPR and CCPA standards. GDPR compliance has accelerated the need to adopt edge computing, as data and compute solutions remain close to the source of information and never leave the country where it’s generated. Not only does this approach preserve data security, it also reduces the risk of data breaches that have become a major threat from accessing data stored in the cloud.  

Embracing a Future at the Edge

The increasing need for scalable AI solutions, real-time analytics, and data security have propelled companies to process data at the edge. Edge computing allows data produced by IoT devices to be analyzed quickly, as they are processed locally at the site rather than being sent up to the cloud. Additionally, edge AI can help companies unlock new growth opportunities, while maintaining the sensitivity of their data. Pathr.ai’s spatial intelligence technology runs at the edge in real-time, ensuring that data collected stays at the client’s site and enabling fast analysis. By adopting an edge computing approach, we’ve scaled our AI solution to multiple worldwide locations, while placing data privacy and security at the forefront of our technical development. 

For more information about Pathr.ai, please visit https://pathr.ai/ or reach out to info@pathr.ai. 

George Shaw is the Founder and CEO of Pathr.ai™. An accomplished industry veteran working at the intersection of data and engineering, Shaw is a true innovator in the fields of spatial intelligence, machine learning, artificial intelligence, and related technology solutions.

Prior to founding Pathr.ai™, Shaw served as Senior Director of Technical R&D at AltSchool. He additionally held senior technical positions at Intel and Target, working respectively as a Principal Engineer and Principal Data Strategist in Consumer IoT.

Preceding Intel and Target, Shaw was a Technical Fellow & Scientific Advisor and Vice President of R&D at RetailNext, revolutionizing the way retailers generate and utilize data about how customers move through their stores. He also served as a consultant at Second Spectrum, implementing spatiotemporal pattern recognition and machine learning technology to provide real-time insights based on NBA player movements.

He has served as an advisor to multiple startups and emerging technology entities and holds multiple U.S. patents for his inventive work in the areas of retail analytics methodology, customer movement and path analysis.

Shaw earned his B.A. from Boston University and M.S. from MIT.