Edge AI
We design, optimize, and deploy AI models on edge devices – from data preparation to embedded system integration, and on-device model inference.
INTELLIGENCE WHERE DATA IS CREATED
Sii runs artificial intelligence models directly on edge devices, enabling your systems to operate in real time, offline, and without sending sensitive data to the cloud. By optimizing AI models for resource-constrained hardware, we help you bring intelligence closer to your products and users. As a result, your systems respond faster, depend less on the cloud, and give you greater flexibility to use AI where data is created every day.
ACHIEVE MORE WITH SII X EDGE AI
Edge AI in practice
Edge AI runs trained AI models directly on embedded devices instead of performing inference in the cloud. In practice, this means analyzing data locally, directly on the device. Sii experts deploy this approach to reduce latency, lower network bandwidth usage, enhance data privacy, and ensure that systems can operate even without continuous internet access.
When to deploy Edge AI
Sii offers Edge AI deployment for projects where:
- real-time response is critical
- devices operate in environments with limited or unstable connectivity
- sensitive data cannot leave the device
- the system runs on hardware with constrained compute power, memory, or energy consumption
Benefits of using Edge AI
We optimize AI models for edge devices to deliver:
- ultra-low latency – data analysis runs directly on the device
- enhanced privacy – data stays on-device
- offline operation – no dependency on continuous cloud access
- optimization for embedded hardware – tailored to constrained resources
- scalability – the ability to deploy AI across large fleets of devices
Edge AI use cases
Sii specialists select and deploy Edge AI based on the type of device, data, and operating conditions of the final product. Edge AI solutions are used in:
- Smart cameras – real-time object detection and tracking support industrial use cases, safety and security systems, and vision systems
- Industrial automation – local perception and decision-making take place at the device and production line level
- Predictive maintenance – early anomaly detection helps reduce downtime and service costs
- Autonomous and semi-autonomous systems – perception and response are handled locally, without cloud dependency
- Wearable devices – data analysis and user health monitoring take place directly on the device
WHY PARTNER WITH SII FOR EDGE AI
End-to-end support
Sii supports the entire Edge AI solution lifecycle, combining the expertise of embedded engineers and AI specialists. Our scope includes:
- data collection, preparation, and normalization
- hardware and software architecture design
- AI model creation, training, and compression
- model optimization for the target hardware platform
- integration with embedded systems
- deployment of on-device model inference
- monitoring and tuning in the production environment
As a result, Sii delivers stable, ready-to-use artificial intelligence solutions for edge devices – from hardware and software design, through model creation and optimization, to integration, deployment, and ongoing solution tuning.
550+ Edge AI experts
Sii develops Edge AI projects with the support of over 550 specialists, including embedded systems experts, data science specialists, and ML engineers. This talent base allows us to build teams capable of delivering production-grade solutions, not just PoCs, or prototypes.
Advanced technology expertise
Sii delivers Edge AI projects across a wide range of platforms, from bare-metal systems and low-power microcontrollers to high-performance Linux-based solutions. Our specialists work with technologies such as:
- C, C++, Python, and Rust
- TensorFlow and ONNX
- STM32Cube.AI, Edge Impulse, TensorFlow Lite, CMSIS-NN, eIQ, and e-AI Translator
- MATLAB & Simulink
- as well as Embedded Linux, RTOS, and bare-metal environments
This allows us to select the right tools and architecture for a specific device, hardware constraints, and production requirements.
EDGE AI case studies
Meet our EDGE AI TEAM
EDGE AI News & projects
Edge AI: when should you run AI directly on a device?
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TPM 2.0 in Automotive
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Can AI support electronics design? Testing GitHub Copilot and KiCad
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Building an RTK Rover: Web-controlled precision navigation
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Sii becomes Vector Certified Embedded Engineering Partner
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Threat modeling for cybersecurity in embedded systems
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DevSecOps in practice: Integrating security throughout the development lifecycle
Read moreWHAT YOU NEED TO KNOW
Read our FAQ
Edge AI is an approach in which artificial intelligence models run directly on edge devices instead of processing data in the cloud. This means that data analysis, inference, and decision-making happen locally, where data is created. Sii designs and deploys these Edge AI systems for embedded devices, IoT devices, cameras, sensors, and industrial platforms.
Edge AI is worth deploying when a system needs to operate in real time, devices work with limited connectivity, and sensitive data should not leave the device. It is also a strong choice when privacy, low power consumption, quality control, or low-latency monitoring are critical. Sii helps assess whether AI at the edge, a cloud-based solution, or a hybrid architecture will be the best fit.
Edge AI can process data from cameras, microphones, sensors, IoT devices, industrial systems, wearable devices, and other edge devices. This may include image, measurement, diagnostic, environmental, or operational data. Sii supports data preparation, normalization, and optimization so that the AI model can run directly on the device and deliver results in real time.
Yes. Edge AI helps reduce data transfer to the cloud because data is processed locally, directly on the device. This allows sensitive information to stay closer to its source, supporting privacy, security, and compliance with data protection requirements. Sii experts design Edge AI architectures that take the client’s technical, operational, and regulatory requirements into account.
AI models can run on many types of edge devices, from low-power microcontrollers, bare-metal systems, and RTOS to high-performance platforms based on Embedded Linux. In Edge AI projects, Sii works with technologies such as TensorFlow, ONNX, STM32Cube.AI, Edge Impulse, TensorFlow Lite, CMSIS-NN, eIQ, e-AI Translator, MATLAB, and Simulink.
Yes. Edge AI works well in industrial environments where fast system response, reliability, and local decision-making are critical. Typical AI use cases include computer vision, smart cameras, quality control, predictive maintenance, machine monitoring, and sensor data analysis. Sii selects AI models, architecture, and hardware platforms based on the device’s operating conditions and production requirements.
Sii supports the entire Edge AI project lifecycle, from data collection and preparation, through system architecture design, AI model creation and training, to compression, optimization, embedded system integration, and deployment of on-device model inference. After the solution is launched, Sii can support monitoring, tuning, and further optimization in the production environment.
No. Edge AI can be deployed both in new products and existing devices, provided their architecture, processor, memory, and power consumption allow the AI model to run locally. Sii analyzes hardware constraints, available data, system requirements, and target use cases to determine whether AI can be built into the current product or whether the platform needs to be modernized.
Edge AI helps reduce system response times, limit cloud dependency, lower network bandwidth usage, and enhance data privacy. For companies developing intelligent devices, this means greater control over product performance, offline operation, and the ability to scale AI across large fleets of Edge AI devices. Sii helps turn Edge AI technology into stable, production-ready solutions tailored to specific devices, data, and operating conditions.
Yes. Sii can support the project beyond AI models on edge devices alone. Our teams also deliver work related to embedded software development, embedded cybersecurity and compliance, testing and requirements traceability, as well as hardware design and prototyping. This allows the client to develop Edge AI as part of a complete embedded ecosystem – from hardware to software, security, testing, and production deployment.