Solutions — Embedded AI & Edge Devices
AI That Runs Where the Data Is Born — On the Device
We compress state-of-the-art models until they run in real time on Jetson modules, Raspberry Pi, mobile NPUs, and microcontrollers — no cloud round-trip, no connectivity dependency, no raw data leaving the device.
Intelligence at the Edge, Not at the End of a Network Cable
Cloud AI has a physics problem: the camera, the sensor, and the machine are here, and the model is hundreds of milliseconds away. For a robot arm, a safety system, or a device in the field with patchy connectivity, that round-trip is the difference between working and not working.
Synexian puts the model on the device. We take networks trained in the cloud and make them small, fast, and robust enough for embedded hardware — through quantization, pruning, and distillation — then compile them for the exact silicon they will run on, from GPU modules down to microcontrollers.
Around the model we build the full device stack: real-time inference pipelines in C++ or Rust, sensor integration, offline-first data handling, and signed over-the-air updates for entire fleets. The result is a product, not a prototype on a devboard.
The Cloud Is the Wrong Place for Some Decisions
Cloud inference is the right default — until latency, connectivity, privacy, or per-device economics say otherwise. Here is what changes when the model moves onto the device.
The Full Edge Stack, One Team
Embedded AI fails when the ML team and the firmware team stop at their own borders. We do both.
From Trained Model to Deployed Fleet
Getting a model onto a device is a pipeline with hard constraints at every stage. Step through how a network trained in the cloud becomes firmware running reliably on thousands of devices.
Where On-Device AI Wins
Anywhere the decision is time-critical, the connection is unreliable, or the data is too sensitive to ship — the edge is the better architecture.
From Feasibility to Fleet Rollout
Four phases built around the hard question first: can the accuracy you need fit in the compute, power, and cost budget you have?
Have a Device? Have a Model? Have Neither?
Wherever you are starting from, our engineers will assess what accuracy fits your hardware budget, recommend the right silicon, and map the path from prototype to a fleet you can update over the air.
- No obligation
- 30-min call
- Hardware recommendation included
The Edge Stack
Trained in the cloud, compiled for the silicon — runtimes and toolchains chosen per target, from GPU modules to microcontrollers.
Where ML Engineering Meets Firmware Discipline
Edge AI lives at the intersection of two hard disciplines. Teams that only do one of them ship prototypes; teams that do both ship products.
Common Questions
Answers to the questions we hear most often from teams evaluating embedded and edge AI.
Ready to Put Intelligence on the Device?
Tell us what the device needs to see, hear, or decide — and the power, cost, and latency budget it has to do it in. Our engineers will scope the model, the silicon, and the path to a deployable fleet.