Edge AI Hardware
Engineering
On-device machine learning for industrial and IoT systems. We design, train, and deploy AI models that run directly on embedded hardware — delivering real-time intelligence without cloud dependency, bandwidth cost, or data exposure.
Edge AI is a core capability within WIRL Engineering's broader embedded systems practice — not a standalone service. We integrate AI directly into the hardware and firmware systems we design and build.
The Case for
On-Device Intelligence
For most industrial and IoT applications, cloud inference is not a viable architecture. These are the engineering reasons why.
Latency
Cloud inference introduces round-trip delays that are incompatible with real-time control and detection applications. On-device inference responds in milliseconds — bounded by the hardware clock, not network conditions.
Privacy
Sensor data processed on-device never leaves the hardware. This is a structural privacy advantage with direct implications for regulatory compliance, data sovereignty, and enterprise security requirements.
Connectivity Independence
Industrial and infrastructure deployments cannot assume reliable network access. Edge AI systems continue operating during connectivity interruptions — critical for applications where downtime has operational consequences.
Bandwidth & Cost
Transmitting raw sensor data to the cloud at scale is expensive and slow. On-device inference produces structured results — events, anomaly flags, classification outputs — that require a fraction of the bandwidth.
Edge AI
Engineering Disciplines
Five specialized practice areas within WIRL Engineering's Edge AI capability.
Embedded AI on Microcontrollers
Running inference on STM32, ESP32, and ARM Cortex-M hardware. Memory-constrained model deployment, TensorFlow Lite for Microcontrollers, and hardware-specific optimization.
Low-Power AI & Inference Optimization
Designing inference systems that operate within milliwatt power budgets. Quantization, pruning, duty cycling, and hardware-aware model optimization for battery-operated deployments.
Vision Systems at the Edge
Embedded computer vision on constrained hardware. Image classification, object detection, and anomaly detection running locally without cloud processing.
Audio & Sensor Anomaly Detection
Industrial predictive maintenance through vibration analysis, acoustic monitoring, and multi-sensor anomaly detection. Deployed directly on field hardware.
Private AI Architectures
Cloud-independent AI systems designed for environments with strict data control requirements. Air-gapped operation, local model storage, and zero-exfiltration architectures.
Integrating AI into
Your Hardware System
Edge AI is most effective when designed into the hardware from the start. Contact us to discuss your system requirements.