Edge AI · On-device inspection
On-Device Edge Vision Defect Detection 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 Intelligence
Latency, privacy, and connectivity push compute out of the cloud. We design the embedded hardware, firmware, and quantized models together so on-device inference performs 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
Have a project requiring this engineering scope?
Share your technical requirements, 3D CAD files, or operating specs. NDA support is available where required before confidential file exchange.
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
Compact electronics packaging, connector breakouts, thermal dissipation, and waterproof gasket architectures.
industrial inspection
Tailored engineering, mechanical design, and rapid prototyping solutions designed specifically for industrial inspection applications.
robotics
Tailored engineering, mechanical design, and rapid prototyping solutions designed specifically for robotics applications.
smart cameras
Tailored engineering, mechanical design, and rapid prototyping solutions designed specifically for smart cameras applications.
agritech
Crop health vision inspection, robotic harvesting gripper guidance, and solar-powered outdoor perception units.
consumer hardware
Tailored engineering, mechanical design, and rapid prototyping solutions designed specifically for consumer hardware applications.
Related work
Case studies tied to this service
See how this engineering capability applies across real project scopes, analysis goals, and physical prototype iterations.
Thermal engineering
Conjugate Heat Transfer & Thermal Analysis for Sealed Electronics Enclosure
How CFD-based conjugate heat transfer caught a 14 °C hotspot in a sealed IP66 enclosure before prototype — saved an estimated 2 mold revisions and 7 weeks of rework.
From the blog
Articles that support this service topic
Explore practical engineering guides related to design, manufacturing, simulation, and product development.
Edge AI
Deploying YOLO on Jetson in India: 2026 Production Playbook
The production playbook for deploying YOLOv8 / v11 on Jetson Nano / Orin in Indian factories — quantization, TensorRT, OTA updates, monitoring, and the gotchas you only learn from shipping.
Applied AI
Computer Vision Development Services in India: Detection, Segmentation, OCR
A practical guide to computer vision development services in India — what use cases work, how vendors price, and what production deployment really looks like.
Applied AI
Deploying YOLOv11 to Jetson Orin Nano at 30 FPS
Walkthrough of shipping a segmentation-class YOLOv11 model to a Jetson Orin Nano at production latency — quantization, TensorRT conversion, and the pitfalls.
FAQ
Questions teams ask before they engage
Common questions teams ask before starting a project.
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
Deploying AI to Physical Hardware or Edge Devices? Build It to Ship
Share your target latency, hardware constraints (Jetson / ESP32), and data availability. We build models and firmware that run reliably in the field.