
We design, optimize, and deploy AI and ML models directly on edge devices, from NVIDIA Jetson and ARM Cortex to microcontrollers, enabling real-time inference, on-device intelligence, and cloud-edge orchestration at minimal latency.
When latency matters in milliseconds, connectivity is unreliable, or sending raw data to the cloud creates privacy and bandwidth problems; you need intelligence on the device itself. Waiting for the cloud is not a viable architecture when the decision has to happen at the sensor, the camera, or the machine.

We assess your hardware environment, use case requirements, and latency constraints to design an edge AI architecture that fits your devices, your data, and your operational reality before any model development or deployment begins.
We apply quantization, pruning, knowledge distillation, and neural architecture search to reduce model size and compute requirements, achieving INT8 and FP16 inference performance that runs reliably within your edge hardware memory and power envelope.
We package, convert, and deploy optimized ML models directly onto edge devices using TensorRT, TensorFlow Lite, ONNX Runtime, and OpenVINO, with hardware-specific tuning that extracts maximum inference performance from every target platform.
We develop and deploy TinyML models on ARM Cortex-M, STM32, ESP32, and Nordic nRF devices that perform keyword detection, anomaly detection, and sensor classification at microwatt power levels without any cloud dependency.
We deploy real-time object detection, defect inspection, and activity recognition models on NVIDIA Jetson, Hailo-8, and Intel Movidius platforms using YOLO, DeepStream, and MediaPipe pipelines optimized for on-device inference at production frame rates.
We build edge MLOps pipelines that manage model versioning, OTA updates, performance monitoring, and drift detection across deployed device fleets using AWS IoT Greengrass, Azure IoT Edge, Balena, and Mender.io so every device stays accurate and current.
We design cloud-edge orchestration architectures using MQTT, OPC-UA, AWS IoT Core, and Azure IoT Hub that synchronize on-device inference results with cloud analytics platforms, enabling hybrid intelligence that runs locally but learns globally.
We implement hardware-rooted security across edge AI deployments using secure boot, TPM attestation, OP-TEE trusted execution environments, OTA signing, and encrypted model storage so your AI assets and device fleet are protected from tampering and extraction.
We deploy edge AI across industries where real-time on-device intelligence directly determines operational safety, quality, and efficiency outcomes.

We deploy edge AI for real-time defect detection, predictive maintenance, and quality inspection directly on production line hardware, reducing defect escape rates and cutting unplanned downtime without cloud connectivity dependency.

We integrate AI inference on medical edge devices for patient monitoring, diagnostic imaging support, and anomaly detection, meeting HIPAA privacy requirements by keeping sensitive clinical data processed locally rather than transmitted to the cloud.

We deploy computer vision and behavior analytics models at the edge for shelf monitoring, customer flow analysis, and checkout automation, enabling real-time store intelligence without the latency or bandwidth cost of cloud-dependent video analytics.

We deploy edge AI on construction site cameras and safety devices for real-time PPE compliance detection, hazard identification, and machinery proximity alerts, operating reliably in environments where network connectivity is inconsistent or unavailable.
We evaluate your target hardware specifications, inference requirements, power constraints, and connectivity environment to define a realistic edge AI deployment strategy that fits your devices and delivers the latency and accuracy your use case demands.
We select or develop the right model architecture for your use case and apply quantization, pruning, and compilation techniques to reduce model size and computational cost while preserving the accuracy required for reliable operation on your target hardware.
We package and deploy optimized models onto your edge devices with hardware-specific runtime configuration, integrating inference outputs with your existing sensor pipelines, control systems, and local storage so the AI fits naturally into your operational workflow.
We configure the cloud-edge communication layer that synchronizes model updates, aggregates inference telemetry, and manages device health reporting across your fleet, giving you central visibility and control without compromising on-device autonomy or latency.
We deploy monitoring and OTA update pipelines that track model performance across every deployed device, detect accuracy drift, push validated model updates to the fleet, and scale the infrastructure as your device deployment grows from pilot to full production.
We evaluate your target hardware specifications, inference requirements, power constraints, and connectivity environment to define a realistic edge AI deployment strategy that fits your devices and delivers the latency and accuracy your use case demands.
01
Hardware & Use Case Assessment
02
Model Selection & Optimization
03
Edge Deployment & Integration
04
Cloud-Edge Orchestration Setup
05
Monitor, Update & Scale

We work across the full edge AI hardware and software stack, from silicon to MLOps, selecting the right tools for your target device and deployment environment.





















































































































































































































































































































Dynamisch developed a centralized telemetry platform that enables continuous monitoring of appliance performance across distributed device fleets. The solution delivered 65% faster issue detection, a 27% reduction in repair time, and a 34% increase in genuine spare part sales, while reducing annual platform maintenance costs.
At Dynamisch, we engineer edge AI and ML integration across the full hardware spectrum, from ultra-low-power microcontrollers to real-time multi-model inference on NVIDIA Jetson platforms. Every solution includes the fleet management infrastructure to keep your deployed models accurate, updated, and performing reliably as you scale.
Full Hardware
Spectrum Coverage
TinyML to Full
GPU Edge Inference
Edge MLOps &
Fleet Management
Secure & Compliance-
Ready Deployments
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From TinyML on microcontrollers to real-time inference on NVIDIA Jetson, we deploy AI on the hardware your operations actually run on.
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