Edge AI & IoT Solutions
Intelligence at the Edge—Without Cloud Dependency
Cloud-dependent AI fails at the worst moments: when connectivity drops, latency spikes, or privacy requirements prohibit sending data off-premises. We deploy Edge AI and tinyML models directly onto resource-constrained microcontrollers and local gateways so your systems process data where it is generated and act in real time.
The Problem: Cloud Dependency in Industrial Systems
Traditional AI and IoT architectures route every sensor reading to a centralized cloud for inference. This creates three compounding problems: high latency that makes real-time decisions impossible, bandwidth costs that scale with data volume, and a single point of failure when connectivity drops.
For industrial operations, autonomous systems, and healthcare devices, this is not just an inefficiency—it is a critical architectural risk. A factory floor sensor that needs to detect an anomaly and stop a machine cannot wait 300 milliseconds for a cloud round-trip.
How We Deploy Intelligence at the Edge
We build specialized hardware-specific inference pipelines that train models offline and compile them for efficient on-device execution. The result: immediate local decisions, minimal bandwidth usage, and systems that continue operating without any network connection.
tinyML on Microcontrollers
We compress and deploy neural networks onto low-power ARM Cortex-M and ESP32 architectures using quantization and pruning techniques, achieving sub-millisecond inference with under 256 KB of RAM.
STM32 Platform Support
We work across the STM32 family: STM32F4 and STM32H7 for inference-heavy workloads where Cortex-M4 DSP extensions and the H7's L1 cache make a measurable difference, and STM32L4/U5 for ultra-low-power deployments running continuous anomaly detection under 10 µA. Model deployment uses STM32CubeAI, which compiles Keras, TensorFlow Lite, and ONNX graphs into INT8-quantized C code and validates it against the reference model on the target device before it ships.
Offline Training Pipelines
For environments where sensitive data cannot leave the premises, we design secure, air-gapped training pipelines that produce production-ready models without cloud exposure.
Autonomous Sensor Networks
We build resilient sensor networks that filter noise, detect anomalies locally, and aggregate only actionable insights before syncing—reducing cloud data ingestion costs by orders of magnitude.
Who This Is For
This works well for teams building...
- Industrial IoT systems deployed in environments with intermittent or no network connectivity
- Robotics or autonomous systems requiring sub-100ms decision latency that a cloud round-trip cannot meet
- Healthcare or defense applications where raw sensor data cannot leave the device or premises
Might not be the right fit if you are...
- Building applications where cloud-round-trip latency of 100–500ms is fully acceptable
- Relying exclusively on standard cloud AI APIs with no embedded hardware component
Frequently Asked Questions
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What is tinyML?
tinyML refers to machine learning models compressed and optimized to run on microcontrollers with severely limited memory and power—typically under 1 MB RAM and 1 mW power consumption. Techniques like quantization, pruning, and knowledge distillation make this possible without meaningful accuracy loss.
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Which microcontrollers do you target?
We primarily work with ARM Cortex-M series processors (STM32, nRF52840) and Espressif ESP32 architectures, but we can adapt inference pipelines to other embedded platforms depending on your hardware constraints.
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Can Edge AI operate completely offline?
Yes. Our solutions are designed specifically for air-gapped and intermittently-connected environments. The device makes all decisions locally and only synchronizes aggregated insights when connectivity is available—raw data never leaves the device.
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What types of models can run on edge devices?
Anomaly detection, keyword spotting, vibration analysis, image classification, and time-series forecasting models can all be compressed to run efficiently on microcontrollers. The right model depends on your latency requirements, available memory, and acceptable accuracy trade-offs.
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Which STM32 series do you target for Edge AI, and how do you deploy models?
We primarily target STM32F4 and STM32H7 for inference-intensive workloads—the Cortex-M4 DSP extensions and the H7's dual-issue Cortex-M7 pipeline with L1 cache make a measurable difference for neural network execution. For ultra-low-power applications we use STM32L4 and STM32U5, which can sustain continuous anomaly detection at under 10 µA average current. Model deployment uses STMicroelectronics' STM32CubeAI toolchain, which compiles Keras, TensorFlow Lite, and ONNX models into optimized C code with automatic layer fusion, INT8 quantization, and on-device validation against the reference model to catch accuracy regressions before code reaches the hardware.
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How does STM32 compare to ESP32 for Edge AI workloads?
STM32 and ESP32 address different ends of the edge AI spectrum. STM32H7 offers deterministic hard real-time execution, superior DSP throughput via Cortex-M7, and tightly coupled memory—preferred for time-critical industrial control and safety-relevant systems. STM32L4/U5 leads on ultra-low-power duty cycling. ESP32, with its dual Xtensa cores and built-in Wi-Fi and Bluetooth, is a better fit when wireless connectivity and rapid prototyping matter more than real-time guarantees. We select the platform based on your latency, power, connectivity, and certification requirements.
Related Services
Edge AI systems need hardware that can survive the field. Our custom metal & hardware design service covers ruggedized enclosures and custom PCBs designed specifically for edge deployments. For the software side of distributed systems, see our software engineering practice. Teams managing cloud data infrastructure for their IoT backends may also benefit from cloud cost optimization.
Deploy Intelligence Where It Matters
Reduce latency, cut bandwidth costs, and ensure your autonomous systems run without a network connection.