Embedded ML for Your Existing Hardware
We design and deploy machine learning models for resource-constrained 32-bit microcontrollers. Our solutions add new analytical capabilities to your products without requiring a hardware redesign or interfering with core real-time operations.
Your Deployed Fleet, Evolved.
Your products in the field are built on proven, cost-effective MCUs. We specialize in extracting new capabilities from that existing hardware. We integrate highly-efficient ML models directly onto your firmware, enabling functions like on-device predictive maintenance, event classification, and intelligent state awareness.
A core principle of our work is deterministic performance. Your device's primary function is paramount. We engineer our models to operate safely within your system's performance margins, providing guaranteed Worst-Case Execution Times (WCET). Even with high CPU utilization from your main application, our ML routines execute in the remaining cycles without ever compromising the timing of your control loops or communication stacks.
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We add ML capabilities to the products your customers already use.
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We enable specific outcomes, from binary event detection to sophisticated state classification using multi-sensor inputs.
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Our primary expertise is the ARM Cortex-M family (M4, M33, M7, M55), but we are proficient with similar 32-bit cores like RISC-V and PIC32, particularly those with an FPU or vector extensions.
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We deliver optimized C libraries that integrate cleanly into your bare-metal or RTOS environment.
Performance Profiles for Your Application's Needs
Every embedded system has a unique set of constraints. We design models that respect them, whether the priority is immediate response or long-term, low-power analysis.
Low-Latency & Real-Time Inference
For applications that require immediate decisions, our models provide answers within strict time budgets.
Industrial Quality Control: A camera captures an image of a CNC tool tip. A model classifies its state as
TOOL_OKorTOOL_DAMAGEDin milliseconds, preventing part damage.System Safety: An IMU on a commercial drone's motor arm runs a model that detects the high-frequency vibrational signature of a blade fracture, triggering an immediate alert.
Deferred & Incremental Computation
For battery-powered or remote devices, insights can be built slowly to conserve power. Our models can aggregate data over hours or days to produce a single, high-value result.
Agricultural Analysis: A solar-powered field sensor incrementally feeds hourly soil readings into a stateful model. At the end of the day, it produces a single, data-rich prediction like
Predicted_Crop_Stress: High, eliminating the need to transmit raw sensor data.Environmental Monitoring: A remote acoustic sensor records audio snippets throughout the day. When power is available, a CNN runs on the collected data to produce a daily report of detected species and an acoustic biodiversity index.