Edge AI Development Services
Edge AI development for products that have to decide on the device: hardware selection, models trained on your sensor data, INT8 compression, TinyML on microcontrollers, NPU and embedded Linux deployment, and over-the-air model updates across your fleet.
When the Device Has to Think for Itself
Sending every image, sound and sensor reading to the cloud works until it does not. Latency rules out real-time control, connectivity drops in the field, bandwidth and cloud costs grow with every device, and some data, such as audio from a bedroom or video from a clinic, should never leave the room at all. Edge AI moves the model onto the device, so it can decide in milliseconds, keep working offline and send only the results.
It is also harder than cloud AI. The model has to fit in a few hundred kilobytes or a small NPU, run within a power budget, survive on data from a cheap sensor and be updated across a fleet without bricking it. MetaDesk Global does edge AI development with firmware and machine learning engineers on the same team, which is what this work needs.
Edge AI Development Services
From a feasibility test on a development kit to models running and updating across a fleet of devices.
Feasibility and hardware selection
We test whether the model you need can run on the hardware you can afford, and recommend a microcontroller, NPU, application processor or GPU module against accuracy, latency, power and unit cost. Our guide to edge AI hardware explains the choice.
Models designed for the edge
Small architectures trained on data captured with your actual sensor and in your actual environment, because a model trained on clean public datasets rarely survives a cheap microphone or a dusty camera.
Compression and optimization
INT8 quantization, pruning and knowledge distillation, measured on the target chip for accuracy, latency, memory and power. Read edge AI optimization techniques for how we diagnose before we compress.
TinyML on microcontrollers
Keyword spotting, sound event detection, vibration analysis and simple vision on microcontrollers such as the ESP32-S3 and Arm Cortex-M parts, often running for months on a battery. See TinyML: bringing AI to the smallest devices.
Vision at the edge
Detection, counting and inspection on cameras and embedded Linux boards, from single-camera sensors to multi-camera systems with hardware video encoding. See our computer vision development services.
Integration into firmware
The model wired into the device firmware with the sensor pipeline, pre-processing, scheduling and power management around it, so inference runs reliably next to everything else the device has to do.
Fleet updates and monitoring
Over-the-air model updates with rollback, field metrics on model confidence, and data collection for retraining. Where data cannot leave devices, federated learning is sometimes an option.
Choosing Edge AI Hardware
The cheapest chip that meets the target is usually the right one. These are the four classes we choose between, from smallest to largest.
| Hardware class | Examples | Good for | Typical power |
|---|---|---|---|
| Microcontroller (TinyML) | ESP32-S3, Arm Cortex-M4, M7 and M55 parts | Audio events, keywords, vibration and anomaly detection, very small vision models | Milliwatts; months on a battery |
| Microcontroller with NPU | MCUs with Arm Ethos-U or vendor neural accelerators | Person detection, simple vision and richer audio models at very low power | Tens to hundreds of milliwatts |
| Application processor with NPU | Rockchip RK3588, NXP i.MX 8M Plus, Raspberry Pi with an AI accelerator | Multi-camera vision, several models at once, embedded Linux products | A few watts |
| GPU module | NVIDIA Jetson Orin family | Heavy vision, large models, robotics and many video streams | Roughly 7 to 60 watts |
Real numbers depend on the model, the runtime and the duty cycle. We benchmark the candidate model on the candidate hardware during feasibility, before you commit to a bill of materials.
Edge Intelligence We Have Shipped
Published case studies where the processing happens on the device.
On-device snore detection
A privacy-first sleep device running a CNN with INT8 quantization on an ESP32-S3. It classifies snoring at about 91% accuracy in real time, entirely offline, so no audio is ever uploaded.
Acoustic analysis on a low-cost chip
An ESP32 sound analyzer that runs FFT locally and posts compact frequency-band summaries every five seconds instead of streaming raw audio to the cloud.
Multi-camera edge recording
A clinical recording system on RK3588 that captures two visible cameras and a thermal camera, encodes them in hardware and uploads finished sessions, with no keyboard or operator training needed.
Real-time vision in the field
Production-line bag counting and drone-based crop disease detection, both built to work in real time under changing light and motion.
Measured on the Target Hardware at Every Step
Edge AI fails when accuracy is measured on a laptop and power on a datasheet. Our process measures both on the device. For the problems we plan around, see edge AI deployment challenges in real fleets.
Feasibility on a development kit
A baseline model on candidate hardware, measured for accuracy, latency, memory and power. You get a go or no-go answer and a hardware recommendation.
Data from the real sensor
Data captured with the production sensor in the conditions where the device will live, labeled and split so the test set reflects the field.
Train, compress, measure, repeat
Each model version is quantized and benchmarked on the target chip. The loop stops when the agreed accuracy, latency and power targets are all met.
Integrate into the product
The model goes into the firmware with the sensor pipeline, power management, logging and an update path for future model versions.
Pilot and fleet rollout
A field pilot to check real-world accuracy, then staged OTA rollout with monitoring, so a weaker model can be rolled back before it reaches every device.
What Drives the Cost of Edge AI Development
We quote after the feasibility stage, when the model size, data needs and hardware are known.
| Cost driver | Lower effort | Higher effort |
|---|---|---|
| Task | Detecting a few sound or vibration events | Multi-class vision or several models running together |
| Data | Existing labeled recordings from the target sensor | New data collection campaigns in the field |
| Hardware | A board with an NPU and mature toolchain | A small microcontroller with tight memory and power limits |
| Accuracy target | Advisory alerts a person checks | Automated actions with safety or medical consequences |
| Fleet | A few devices updated by hand | Thousands of devices with OTA model updates and monitoring |
Machine Learning and Firmware Under One Roof
Most AI firms have never written an interrupt handler, and most firmware firms have never trained a model. Edge AI needs both, working on the same chip.
- One team from model to firmwareThe people who train the model also fit it into the memory map, schedule it next to the radio and measure its power draw.
- Privacy by designOn-device processing means raw audio, images and personal data can stay on the device, which simplifies compliance in every market you sell in.
- Hardware-neutral adviceWe recommend the cheapest hardware that meets the target, whether that is a microcontroller or an NPU, and benchmark it before you buy in volume.
- Built for the fleetModels ship with an update and rollback path, so improving accuracy after launch is routine work rather than a recall. Our IoT product development team handles the rest of the device.
- You own the model and the codeTrained weights, training pipelines, firmware and documentation for the work you pay for are delivered to you and confirmed in the contract.