AI Engineering for IoT and Embedded Devices
Models that fit the device they ship on, inference split sensibly between device and cloud, and firmware you can show has not been tampered with.
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Tell us the device
IoT teams bring us problems at the edge of what their hardware can do: a model that will not fit the memory or power budget of the target processor, a fleet whose data costs too much to move, or a sensor network whose faults look like outages. Others are about trust: secure boot, signed over-the-air updates and protecting the model on the device from extraction.
We work on your hardware and your data, and each engagement ends in something your team keeps and can re-run.
Where We Help
Edge Inference, Network Faults and Device Trust
Separate engagements, each scoped to the device and the problem in front of you.
Porting to the Device
Cost
Port, compress and measure the model on the target processor until it meets memory, latency and power.
Sensor-Network Monitoring
Reliability
Detect implausible readings and separate a faulty device from a network or collector outage.
Device Trust Evidence
Trust
Evidence that secure boot, signed updates and model protection work as declared.
How It Connects
Porting and compression land in the AI Porting & Deployment Pack, with GPU Performance Engineering where the gateway or cloud side is the bottleneck. Monitoring of a deployed fleet lands in the Production AI Monitoring Harness, and device-trust evidence in AI Governance and Trust.
We have not published an IoT case study yet. If you are weighing an edge deployment, tell us about the device and we will say plainly whether we are the right fit.
IoT and Embedded AI FAQ
Will a compressed model still meet the memory and power budget on our target processor?
We find out on the device, not on a workstation. We port a representative model to the target, quantise or prune it, and measure memory, latency and power under the duty cycle the device will really see. The result is a model that fits or a clear statement of which constraint it cannot meet and what would change that.
Should inference run on the device, on a gateway or in the cloud?
It depends on bandwidth, latency, power and what the data is allowed to leave the device. We measure the candidate partitions on your hardware and network and show the cost and latency of each, so the split is a measured decision rather than a default.
How do we tell one faulty sensor from a network or collector outage?
By modelling the network, not just each reading. Fault detection that knows which devices share a segment or collector can separate a stuck or implausible sensor from a wider outage, and flag each for the right team.
Working With Device Teams
We work alongside your firmware, hardware and platform teams; we do not replace them. Bring us the device and the constraint that is not being met.
