🛒 Arduino, ESP32 & modules

Edge AI, TinyML and Computer Vision

TinyML on microcontrollers (TensorFlow Lite, Edge Impulse), computer vision on NVIDIA Jetson and Google Coral. Anomaly detection and object recognition — on the device, without the cloud.

Typical project:Predictive maintenance sensor + Edge ML· 8–16 weeks

Project

Tell us briefly about the project: what you want to build, which functions it needs, the rough timeframe. If we have done something similar we will send an example.

TinyML on STM32/ESP32, TensorFlow Lite Micro, Edge Impulse, ONNX. NVIDIA Jetson, Coral Edge TPU integration. Anomaly detection, keyword spotting, object classification for embedded.

The point of edge AI is to put the model on the device rather than in the cloud. That buys three things: latency drops to milliseconds, the system keeps working when the connection dies, and the data never leaves the device.

At microcontroller level we work with TensorFlow Lite Micro and Edge Impulse — anomaly detection in vibration, sound and motion data belongs here. For heavier work (object recognition, counting, reading) we use edge platforms such as NVIDIA Jetson or Google Coral.

In this field the real work is not choosing a model but collecting the data. Without samples gathered in real conditions a model works in the lab and fails in the field. So a project usually starts with a data collection phase.

How we work

Edge AI — a single-board computer with a camera module, detection boxes on screen behindTesting in progress — a camera module aimed at a moving part on a test rigThe finished result — a compact vision unit in a metal enclosure on a bracket

What this area covers

  • TensorFlow Lite Micro on STM32/ESP32
  • Edge Impulse pipeline (data → model → deploy)
  • NVIDIA Jetson Nano/Orin computer vision
  • Coral Edge TPU integration
  • Predictive maintenance (vibration, acoustic)
  • Keyword spotting, gesture recognition

What you receive

  • Data collection plan and the collected dataset
  • Trained model and accuracy report
  • Inference code running on the device
  • Power and latency measurements
  • Retraining procedure
  • Device configuration and deployment documentation

Technologies we work with

TinyMLTensorFlow LiteEdge ImpulseNVIDIA JetsonCoral

From concept to production

  1. 01

    Consultation and Requirements Analysis

    We jointly define the project's goals, functional requirements, and budget/timeline constraints. NDA signed if needed.

  2. 02

    Architecture and Schematic Design

    System architecture, part selection, schematic drafted. Initial BOM and PCB block diagram agreed upon.

  3. 03

    PCB Layout and Firmware

    PCB design (KiCad/Altium), signal integrity, EMC checks. Firmware developed in parallel. Interim reviews.

  4. 04

    Prototype and Testing

    First PCB batch assembled and lab-tested. Validation with firmware. Required adjustments.

  5. 05

    Production and Support

    DFM optimization for mass production. Documentation, user manual, long-term technical support.

Frequently asked questions

Have an idea? Let's talk

Small prototype, medium-scale R&D, or long-term technical support — regardless of your project size, I respond within 48 hours.