Trust in AI deep dive: Connectivity

Technology introduction

AI-RAN is a communication network technology that uses AI to optimize the operation of AI services running across radio access networks, edge environments, and cloud infrastructure. The network continuously understands the latency, bandwidth, reliability, and computing requirements of AI applications such as video analytics, robotic control, and remote monitoring. Based on these requirements, it dynamically optimizes radio resources, communication routes, network slices, and edge computing resources. As a result, the network evolves beyond a platform that simply transports data. It becomes an intelligent infrastructure that autonomously adapts to the status and requirements of AI services, enabling more responsive and efficient operations.

Why it matters

This technology enables enterprises and communication service providers to secure the communication quality and computing resources required by AI services in real time. By continuously monitoring traffic conditions, radio quality, device mobility, and AI processing workloads, AI-RAN can anticipate potential service impacts and dynamically control network resources to minimize communication instability and processing delays. This allows organizations in industries such as manufacturing, logistics, infrastructure, and public services to achieve low-latency, highly reliable AI-driven control, stable remote monitoring, autonomous operations, and greater operational efficiency. In this way, the network becomes more than a connectivity service—it becomes a foundational platform for delivering AI-powered services in the AI era.

Example use case

In smart factories, large numbers of robots, autonomous guided vehicles (AGVs), cameras, and sensors work together while AI applications for video analytics, quality inspection, and robotic control operate across devices, edge environments, and cloud infrastructure. At the same time, wireless network conditions constantly change due to the movement of people and equipment, physical obstructions, and fluctuations in traffic demand, making it challenging to maintain low-latency and highly reliable control. AI-RAN continuously monitors both AI service performance and network conditions in real time. By predicting communication and computing resource requirements, it dynamically optimizes radio resources, communication paths, network slices, and edge processing locations. As a result, autonomous operations can continue safely and efficiently even as conditions on the factory floor change.

Technology introduction

APN (All Photonic Network) is a network technology that aims to provide high-speed, high-capacity, low-latency, and energy-efficient communications by connecting devices to the network using an all-optical architecture. As data volumes increase and AI processing becomes more sophisticated, networks are required to deliver higher performance than ever before, and APN is positioned as the foundation supporting this evolution. In applications that handle large volumes of data—such as video, audio, and sensor data—in real time, the quality of communication itself determines the value of the service.

Why it matters

The value this technology brings is the ability to handle the massive volumes of data required by AI, robotics, and digital twins, even across geographically distributed environments, in near real time. For example, in scenarios such as remote monitoring, equipment inspections, and on-site control, communication delays or insufficient bandwidth directly impact decision-making and safety. By leveraging APN, it becomes easier to perform AI-driven analysis and remote operations reliably while mitigating such challenges.

Example use case

Smart cities and large infrastructure environments require the real-time collection of vast volumes of sensor data to create accurate digital twins. APN enables high-capacity, ultra-low-latency transmission of sensor data from geographically distributed locations. This supports real-time AI analysis, anomaly detection, traffic optimization, and other data-driven decision-making processes.

Technology introduction

AI Network OS is a network software technology that forms the foundation of next-generation infrastructure for the AI era. The value of AI is not determined by computing performance alone. As AI systems generate and process vast amounts of data, the network that connects data, computing resources, and AI models becomes increasingly important. To support this environment, organizations need a flexible and scalable network foundation that can be deployed across diverse environments without being tied to specific hardware.

Why it matters

AI Network OS helps organizations accelerate AI adoption while optimizing infrastructure costs. As AI evolves from systems that simply learn to systems that continuously reason and collaborate, AI models are becoming increasingly distributed across cloud, data center, and edge environments. Connecting these distributed AI resources efficiently is essential to enabling them to operate as a unified intelligence. AI Network OS can be deployed anywhere from the edge to the cloud, enabling seamless connectivity across AI systems and supporting large-scale data processing with greater efficiency.

Example use case

As AI services become more advanced, multiple AI inference models are often used to respond to different types of user requests. These models are distributed across cloud, data center, and edge environments, which can lead to response delays, uneven resource utilization, and increased infrastructure and operational complexity.
AI Network OS dynamically connects distributed AI inference models and directs workloads to the most appropriate execution environment, helping organizations improve both the quality and profitability of AI services.