Edge AI and Embedded AI Development Services
No sales pitch. Just a 30-min technical review.
ByteSnap Design is a UK embedded systems consultancy specialising in production-ready computer vision and on-device ML inference for industrial, automotive, and scientific applications.
We’ve been doing this for years, long before the term “edge AI” entered common use.
If your product roadmap includes on-device AI and your team is stretched, lacking specialist ML resource, or simply unsure where to start – that’s where ByteSnap Design steps in.
Hardware we ship on
Where this applies
- Defect detection on a production line.
- Multi-camera vision in a vehicle.
- Real-time object classification in scientific instruments.
- Gesture recognition in industrial equipment.
- Predictive maintenance on embedded hardware.
If your product needs intelligence that cannot wait for a cloud round-trip and you need it reliable, on-device, and manufacturable edge AI is worth serious consideration. We’ll tell you if it’s right for your product in a 30-minute call.
What is edge AI?
Edge AI runs artificial intelligence models directly on the device, rather than sending data to a cloud server for processing. For embedded products, this eliminates network latency, removes dependence on internet connectivity, keeps sensitive data on the device, and reduces the ongoing cost of cloud inference at scale. The trade-off is that models must be optimised to run within the power, memory, and compute constraints of your target hardware. That optimisation work is where most teams run out of road.
Proof, not promises
CNN-based gesture recognition on ConnectCore 8x. In partnership with Digi International, ByteSnap built an ML demonstration featuring a convolutional neural network classifier processing real-time camera input to recognise body gestures. Digi published the work on their own channel.
Hyperspectral multi-camera vision system. For a client in the imaging sector, ByteSnap designed and delivered a multi-camera vision pipeline on NVIDIA Jetson.
Three separate camera streams from a hyperspectral imaging system were ingested, composited and output as a single unified real-time video stream. When the system failed to interface correctly with the custom camera hardware, despite following NVIDIA’s specifications precisely,
ByteSnap Design diagnosed the fault at hardware and driver level and resolved it.
That depth of diagnosis is what production-grade embedded AI development looks like.
Real-time computer vision. ByteSnap’s latest demonstration runs a real-time AI object detection system entirely on an Infineon PSOC Edge microcontroller. No cloud connection. No external processing. No latency compromise. The engineering behind it is exactly what we deliver for production clients.
HD multi-camera vision for a transport application. ByteSnap Design is working on a further NVIDIA Jetson project, processing several HD camera inputs simultaneously.
Real-world, high-bandwidth edge AI, under active development.
From brief to production. We handle the full pipeline.
- Edge AI system architecture and platform selection (Jetson, PSOC Edge, NXP i.MX, STM32 and others), evaluated against your actual power, compute, cost, and support constraints. Our design partners span the major silicon vendors, and we recommend based on your project, not vendor preference.
- Computer vision and machine vision: object detection, classification, multi-sensor fusion, hyperspectral imaging, real-time inference from camera input.
- ML model optimisation and quantisation for constrained hardware, across TensorFlow Lite, ONNX, PyTorch, and vendor toolchains including Infineon’s DEEPCRAFT suite.
- BSP and firmware integration for on-device inference, with hardware and software engineers on the same project.
- Proof of concept through to production-ready hardware and firmware.
Is edge AI right for your product?
Consider it seriously if your product needs real-time response where cloud latency creates a problem, data privacy where sensor or video data must not leave the device, reliable operation without guaranteed connectivity, lower ongoing cost at scale, or power-efficient battery deployment.
Not sure? Use the tool below to find out in two minutes:
Prefer to talk it through? ByteSnap Design’s feasibility study service gives you a clear answer before significant budget is committed.
More than nine in ten UK manufacturers say AI integration is a priority.
Most don't have the resource to act on it.
In ByteSnap’s 2025 survey of 593 UK manufacturers, 90% said AI integration into future product development was very important or somewhat important.
The direction of travel is not in question. The challenge is having specialist ML resource alongside the embedded hardware and firmware expertise to ship a product. Most teams have one or the other. ByteSnap Design has both.
Read more in our Futureproofing Manufacturing report.
Talk to us about your product roadmap
Embedded AI and Edge AI FAQs
What is the difference between edge AI and cloud AI?
Cloud AI sends data to a remote server for processing and returns the result. Edge AI processes data on the device, delivering lower latency, no cloud dependency, better data privacy, and lower operational cost. The constraint is that models must fit within the device’s memory and compute limits.
Can ByteSnap Design handle the full pipeline?
Yes. ByteSnap Design covers platform selection, model optimisation, firmware and BSP integration, and production hardware design. You do not need an in-house ML team to work with us.
Can you help us build a proof of concept before we commit?
Yes, and for complex or novel hardware targets it is usually the right starting point. A ByteSnap Design feasibility study gives you a clear technical answer before significant budget is committed.
How long does embedded AI development take?
A focused proof of concept can be delivered in a matter of weeks. A production-ready system with custom hardware integration typically takes several months. We’ll give you a realistic estimate after an initial scoping conversation.