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Edge Artificial Intelligence
The world is just seeing the benefits of AI. We are connecting proven benefits and focus on security and safety.
ngenious will first enable Edge Video for schools to help secure our K12 by introducing weapons detection, crowd control, video search and other safety measures.
Fast. Relevant.
Quick Summary & Solutions
Edge Artificial Intelligence at the Edge brings data processing closer to where it's generated, minimizing delays and significantly improving application responsiveness. ngenious streamlines the deployment of AI at the edge with pre-designed architectures that allow businesses to adapt and scale quickly. This approach enhances AI mode
Edge Artificial Intelligence at the Edge brings data processing closer to where it's generated, minimizing delays and significantly improving application responsiveness. ngenious streamlines the deployment of AI at the edge with pre-designed architectures that allow businesses to adapt and scale quickly. This approach enhances AI models by enabling more efficient training, where machine learning can rapidly adjust to new situations and conditions. By processing data locally, edge AI reduces latency, delivering faster inference responses and making real-time decision-making more seamless and efficient for businesses.
- Weapon Detection
- Weapon Detection Response
- Motion and Proximity
- Video search assistant
- Crowd control
- Parking and permits
Benefits, Technologies and Use-Cases
The benefits of Edge Artificial Intelligence
Edge Artificial Intelligence complements overall AI solutions by offloading the inference phase of large language models (LLMs) to the local customer site, enabling real-time decision-making with minimal latency. While the training phase of LLMs occurs centrally in cloud data centers, edge devices handle the inference process, processing real-time data directly at the source, such as in shops, schools, factories, or mines. This setup reduces bandwidth usage, enhances security by keeping sensitive data local, and allows immediate responses, such as detecting threats or optimizing operations. Meanwhile, the cloud manages model retraining and long-term analytics, creating a scalable, efficient AI architecture tailored to specific operational needs.
Technology behind Edge Artificial Intelligence
Edge Artificial Intelligent leverages a combination of storage, memory, and compute resources, including CPUs and GPUs, to enable real-time data processing, decision-making, and inference directly at the network’s edge. By deploying large language models (LLMs), machine learning algorithms, and advanced analytics at the edge, AI can process data locally, significantly reducing the need to transfer large volumes of information to centralized cloud servers for processing. This distributed approach minimizes latency and provides real-time responsiveness, allowing models to be quickly retrained and fine-tuned based on new data or changing conditions
Use-cases Edge Artificial Intelligence
Edge Artificial Intelligence is beneficial for many market situations. In manufacturing, it can monitor production lines, detect equipment issues early, and trigger predictive maintenance to prevent costly downtime. In retail, edge AI can analyze foot traffic in real-time, optimize store layouts, and enhance customer experiences. In schools or public spaces, AI-powered edge devices analyze real-time video footage to detect suspicious behavior or weapons. Processing data locally allows for immediate alerts and automated responses, reducing delays. This ensures faster decision-making, enhanced safety, and efficient usage of resources.