Unlocking faster Edge AI deployments
Edge AI is rapidly moving from a level of abstraction into real applications. In this process, computing power and AI functions are shifting toward the point of origin – away from distributed cloud infrastructures into on-site edge devices. However, this places high demands on developers of Edge AI systems, since they have to integrate operating systems, runtime environments, containers, AI models, and security layers directly into the application, which often leads to problems and significantly increases the effort. Edge AI projects often fail not because of the concept, but because of the integration effort.
SECO’s Clea platform tackles complexity by providing developers with a framework for scalable edge AI systems. SECO’s Application Hub plays a central role, enabling fast access to usable components through deployment-ready containers, sample applications, and deployment guides. This quickly turns an idea into a testable and validated Edge AI application.
Clea OS as the runtime foundation for Edge AI
The technical foundation for Edge AI applications is SECO’s Clea OS software platform. As a secure embedded Linux distro based on the Yocto LTS project, Clea OS is predestined for embedded and edge applications and supports both Arm and x86 architecture platforms. In this context, it provides a controlled, production-oriented runtime environment. Unlike generic embedded Linux, Clea OS is designed for repeatable deployments, secure updating, high maintainability, and consistent execution of applications.
For developers, this means less effort in building the system foundation and more focus on application logic, models, and use cases. In combination with the Clea Software Framework, Clea OS supports device connectivity, data orchestration, fleet management, and AI services at the edge. The Application Hub complements this foundation with available applications, containers, and software components. This makes Clea OS the bridge between application-ready building blocks from the Hub and their practical execution on real edge hardware.
Application Hub: Activate use cases faster
The SECO Application Hub builds on the runtime foundation of Clea OS and accelerates the concrete implementation of Edge AI deployments. While Clea OS provides the controlled execution environment on the edge hardware, the Application Hub delivers the appropriate software building blocks for the application layer.
Instead of having to search extensively for suitable AI models and package containers, prepare inference runtimes, and check individual hardware compatibility, developers can access a curated library of ready-to-use software containers, sample applications, prebuilt AI models, deployment guides, and Edge AI components for a wide range of use cases through the SECO Application Hub.
Developers can select suitable components directly from the Application Hub, test them on the intended target hardware, and then integrate them into a Clea-based edge environment. This creates a consistent process from the runtime foundation through available software building blocks to the executable application.
This noticeably reduces the effort required for setup, packaging, and initial integration, which reduces effort and ensures shorter development time. Technical decision-makers can also assess more quickly whether hardware, performance, and business value are sufficient for near-production implementation.
Use Case: Intelligent People Counting
An example use case of “Intelligent People Counting” shows how an Edge AI application can be implemented in practice more quickly with SECO’s Application Hub and Clea OS. Developing a people-counting application from scratch would normally require several integration steps: connecting the camera, configuring the edge device, setting up the inference runtime, and passing results on to downstream systems.
The Application Hub reduces this effort by providing a pre-made people-counter application, becoming the starting point of a guided flow that moves developers from application discovery to workflow creation and edge deployment. Developers can indeed select the component, adapt the camera input, and target device parameters, and test it locally on the edge hardware.
In the next step, one can use Clea Studio AI to connect the model output to downstream workflows such as dashboards, alerts, occupancy analytics, or IoT systems using visual input, model, and output blocks. Developers simply select the appropriate component in the Hub, adjust parameters such as camera input and target device, and configure the output flow in Clea Studio AI.
Many tasks related to packaging, runtime execution, and workflow integration are covered by the platform. Developers experience the benefit of swiftly creating a use case, then proceed to validation without having to build the application completely from scratch. Tedious and repetitive manual setup is a thing of the past.
The Application Hub in Process
In a pilot project, the Application Hub becomes the central tool for evaluating technical feasibility quickly and easily. Teams can assess at an early stage whether a use case runs reliably on the intended edge hardware, if available computing power is sufficient, and whether the results justify further investment. For this purpose, the Hub provides validation-worthy components such as containers, AI models, sample applications, and deployment guides that can be tested directly on the target device.
Rather than starting with basic integration tasks such as rebuilding packaging, runtime setup, or deployment logic, developers can move directly into functional and technical evaluation. Developers can adapt parameters to the specific use case, configure input sources such as camera or sensor data, test the application on the intended target hardware, and measure relevant performance metrics such as latency, throughput, or resource utilization.
At the same time, they can check how the generated results can be integrated into existing systems – for example dashboards, IoT platforms, alerts, or analytics workflows. This shifts the focus from building the technical foundation to evaluating whether the use case works under realistic conditions.
Clea Studio AI: From Model to Workflow
After selecting a suitable component in the Application Hub, Clea Studio AI guides implementation into an executable workflow. After all, an AI model alone does not yet make a complete application; data sources must be connected, model execution must be configured, and results must be passed on to target systems.
Clea Studio AI supports developers with a visual environment in which to connect input, model, and output blocks. In the people-counting case, for example, the input block can represent the camera stream, the model block uses a selected component from the Application Hub, and the output block can pass the counting results on to dashboards, alerts, or IoT workflows.
This turns a model into a concrete application flow without having to build every integration manually, in code. Regardless, developers retain control over the model, data source, and output path.
Conclusion
The people-counting example illustrates how SECO’s Clea OS, Application Hub, and Clea Studio AI work together. A prepared Edge AI component hastily becomes a test-worthy workflow on real Edge AI hardware.
For developers, “near-production” means that they do not have to start over again with basic integration but can continue working in a targeted way. For example, through parameter tuning, performance validation, security review, and preparation of the rollout across multiple compatible edge devices.
In addition, technical decision-makers receive a reliable foundation for assessing the feasibility, scalability, and business value of a use case more quickly. This turns a validated prototype into a real, scalable Edge AI application faster.
Contact SECO for further information.