TinyML · Edge AI · Embedded AI
AI that runs on the chip, not in the cloud.
Edgehound is an embedded AI engineering studio. We design, compress, and deploy TinyML models on microcontrollers, fuse sensor data on-device, and ship Edge AI for manufacturing floors, factories, and consumer products.
- 256 KB
- models sized for MCU flash, not datacenter GPUs
- <10 ms
- real-time inference entirely on-device
- mW
- power budgets that run for years on a battery
Core capabilities
Embedded AI, end to end
TinyML
AI on Microcontrollers
Neural networks that run on Cortex-M, RISC-V, and DSP-class MCUs. We compress, quantize, and tune models to fit kilobytes of flash and milliwatt power budgets — then integrate them into your firmware.
- —int8 quantization, pruning & distillation
- —TensorFlow Lite Micro, CMSIS-NN, vendor NPUs
- —Bare-metal and RTOS (Zephyr, FreeRTOS) integration
- —Wake-word, gesture & anomaly detection on-device
Signal intelligence
Sensor Fusion
Raw accelerometer, acoustic, current, and environmental streams fused into decisions. We build the DSP and feature pipelines that turn noisy sensor data into reliable on-device events.
- —IMU, audio, vibration & current signal pipelines
- —Feature extraction in fixed-point DSP
- —Multi-sensor anomaly & event classification
- —Kalman filters and state estimation at the edge
Edge AI
Edge AI Deployment
Intelligence at the node and the gateway — no round trip to the cloud. We architect, benchmark, and ship embedded AI systems that run in the field for years on battery or harvested power.
- —On-device inference on Linux gateways & NPUs
- —OTA model updates and fleet management
- —Latency, memory & energy benchmarking
- —Field data flywheels for model improvement
Industries
Anywhere a device could be smarter
If your product has sensors and a power budget, embedded AI can add intelligence without adding a cloud dependency.
Manufacturing & Factories
Predictive maintenance, machine health monitoring, and defect detection running directly on motors, PLCs, and line-side sensors — no cloud required on the factory floor.
Consumer Devices
Always-on voice, gesture, and context awareness for wearables, hearables, and smart home products — at coin-cell power budgets.
Industrial IoT & Energy
Asset monitoring, fault detection, and metering intelligence on constrained nodes across oil, gas, grid, and utilities.
Automotive & Mobility
In-cabin sensing, driver monitoring, and road-event detection on vehicle ECUs and low-power telematics.
Agriculture & Environment
Solar- and battery-powered sensing nodes that classify events in the field and sleep the rest of the time.
Medical & Wearables
On-device biosignal classification — ECG, PPG, motion — where patient privacy is preserved by design.
Why on-device
The case for Edge AI
Millisecond latency
Decisions on the chip, not after a network round trip.
Privacy by default
Raw sensor data never leaves the device.
Works offline
No connectivity dependency in the field or on the floor.
No cloud bill
Inference at the edge costs milliwatts, not per-request fees.
How we work
From sensor to shipped firmware
- 01
Feasibility audit
We review your data, target hardware, and power budget — and tell you honestly whether TinyML fits.
- 02
Model development
Dataset strategy, architecture selection, and training with on-device constraints from day one.
- 03
Compression & tuning
Quantization, pruning, and operator optimization benchmarked on your actual silicon.
- 04
Deployment & support
Firmware integration, OTA update pipelines, and field monitoring so models stay accurate in production.
Have a device that should be smarter?
Tell us about your hardware, your sensors, and what you want the device to know. We'll tell you what TinyML can do for it.