Edge AI · On-device inspection
Zero-cloud defect detection camera on ESP32-S3
A production conveyor inspection camera running a quantized INT8 CNN entirely on an ESP32-S3 — 18 FPS at 0.4 W, no cloud, 6× lower capex per station.
Edge AI & embedded intelligence
Edge AI & Embedded Machine Learning Services
Latency, privacy, cost, and connectivity all push compute out of the cloud. We design the hardware and the model together so edge inference is actually viable in the real world.
What we do
Practical support for targeted engineering work
Yantrix builds end-to-end edge-AI systems: the custom PCB or module, the quantized model, the firmware that drives it, and the OTA pipeline that keeps it updated. We work across NVIDIA Jetson (Nano, Orin Nano, Orin AGX), Google Coral (Edge TPU), Raspberry Pi, and microcontrollers with NPUs such as the ESP32-S3 and i.MX RT series.
What problems we solve
- Eliminate cloud round-trips for latency-critical or privacy-sensitive products.
- Fit capable ML into milliwatts and megabytes on microcontroller-class hardware.
- Co-design the PCB, camera/sensor, and model so the whole device actually performs.
- Productionize edge devices with OTA updates, telemetry, and monitoring.
Tools we use
- NVIDIA Jetson (Nano / Orin)
- Google Coral / Edge TPU
- ESP32-S3 with ESP-DL / TFLite Micro
- Raspberry Pi 5 + Hailo
- TensorRT
- ONNX Runtime
- TFLite / TFLite Micro
- OpenVINO
- PyTorch quantization
- Custom PCB design (KiCad, Altium)
- Zephyr RTOS / FreeRTOS
Deliverables
- Custom PCB or SOM integration with AI accelerator
- Quantized and hardware-optimized model package
- Firmware drivers and inference runtime
- Power, thermal, and latency benchmarks
- OTA update and device-fleet management plan
Use cases
Industries where this service applies
We adapt the same engineering service to different product contexts depending on the load case, packaging problem, validation target, or deployment environment.
IoT devices
Relevant when the project needs focused edge ai & embedded machine learning support.
industrial inspection
Relevant when the project needs focused edge ai & embedded machine learning support.
robotics
Relevant when the project needs focused edge ai & embedded machine learning support.
smart cameras
Relevant when the project needs focused edge ai & embedded machine learning support.
agritech
Relevant when the project needs focused edge ai & embedded machine learning support.
consumer hardware
Relevant when the project needs focused edge ai & embedded machine learning support.
Related work
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FAQ
Questions teams ask before they engage
Service-specific questions are useful for both users and search visibility around intent-driven queries.
Can ML really run on an ESP32-class microcontroller?
For the right model it absolutely can. Quantized CNNs for visual anomaly detection, keyword spotting, and gesture recognition all run comfortably on ESP32-S3 and similar chips. We start with the model size budget and design backwards from there.
Do you do the PCB and enclosure as well?
Yes. That's the advantage of working with us — we already handle mechanical, thermal, and embedded design, so we don't hand off the hardware problem to a third party.
How do you update models in the field?
We design an OTA path into the firmware from day one — signed model artifacts, rollback, and metrics on how the new model performs vs. the previous one on a validation slice.
Start your project
Need edge ai & embedded machine learning support?
Send the problem, your current design stage, and any existing files. We can scope the work from there.