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デバイス内AIソリューション

On-Device AI Solution runs AI models on edge devices for real-time insights without continuous cloud connectivity. AI Engine, AI Stack, and AI Hub support deployment and management across distributed edge sites.

Diagram illustrating on-device AI architecture components like edge devices, AI Engine, Stack, & Hub for real-time insights

Real-Time Edge Intelligence at Deployment Scale

Edge AI scalability

Real-Time Edge Inference

Run AI models directly on edge devices to enable immediate insights and decisions without relying on continuous cloud connectivity.

Data privacy shield

Data Localization and Privacy

Keep data processing local at the edge to reduce data exposure risks and support privacy-focused deployment requirements.

Fast vending operations

Reduced Cloud and Bandwidth Load

Reduce cloud transmission and bandwidth usage by processing data locally, helping control recurring infrastructure costs.

Device model monitoring

Centralized Model Lifecycle Management

Upload, store, distribute, and manage models through AI Hub to support batch deployment across distributed devices.

Enhanced user experience

Prebuilt Models and Open APIs

Use prebuilt reference models and simple APIs for faster integration, while supporting customer-optimized models by scenario.

Edge computing icon

Scalable Multi-Device Management

Scale from single-device deployment to multi-site device management with architecture designed for distributed edge operations.

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