The AI Experimentby Xelec

Xelec hardware engineering

IoT, embedded and electronics product development.

Firmware, boards, sensors and connected devices — engineered for production, not the demo table.

Custom embedded board under bring-up on a lab bench with a debug probe attached and an oscilloscope trace behind it

Capability

One team from schematic to shipped fleet.

Most programmes lose months in handovers. We hold the whole chain, so trade-offs get made once.

6 wks
Typical concept to working prototype
11 lines
Largest deployed device fleet
38 ms
Median on-device inference
You own it
Schematics, layout and firmware

Figures on this page are illustrative placeholders pending verified capability data.

Eight services

What we are asked to build most often.

Each engagement is scoped to a physical deliverable.

H-01

IoT systems

A connected product that works on the bench and falls over as a fleet.

Device, gateway, connectivity and telemetry — with provisioning, identity and secure OTA designed in from the start.

Deliverables

  • Device and gateway architecture
  • Provisioning and OTA path
  • Telemetry and fleet dashboard

H-02

Embedded prototyping

You need proof the thing can exist before anyone will fund it.

Dev-kit bring-up to first custom PCB, plus an honest read on power, thermal, size and volume cost.

Deliverables

  • Working prototype unit
  • Feasibility and cost model
  • Component and supply plan

H-03

Firmware development

Firmware written once, then nobody can safely change it.

Bare-metal and RTOS firmware, drivers, secure boot and signed OTA — with a hardware-in-the-loop test rig.

Deliverables

  • Firmware source and toolchain
  • Secure boot and OTA
  • HIL test rig and CI

H-04

Electronics product development

A prototype no contract manufacturer will quote against.

Schematics, PCB layout, signal and power integrity, enclosure coordination, DFM and certification preparation.

Deliverables

  • Schematics, layout and BOM
  • DFM and test fixtures
  • Certification preparation pack

H-05

Sensor and edge AI integration

The model gets blamed when the capture was wrong all along.

Optics, lighting, sampling and synchronisation specified with the model, then validated on target silicon — Jetson-class modules included.

Deliverables

  • Sensor specification
  • On-target model package
  • Accuracy and latency budget

H-06

NVIDIA Jetson edge AI builds

A vision model that only ever ran on a workstation GPU.

Jetson Orin Nano and Jetson-class deployments: model optimisation and quantisation, carrier board, thermals, camera pipeline, fleet OTA.

Deliverables

  • Optimised Jetson model package
  • Carrier and thermal design
  • Camera pipeline and OTA

H-07

Digital twins of your environment

Decisions about a site made from spreadsheets and memory.

Sensors and cameras instrument the real space, feeds fuse into a live model of it, then you simulate changes before you make them.

Deliverables

  • Sensor and camera coverage plan
  • Live twin model and dashboard
  • Replay and what-if simulation

H-08

Sustaining engineering

A shipped device with no one left who understands it.

Board revisions, obsolescence and second-source swaps, firmware maintenance, field diagnostics and fleet health.

Deliverables

  • Revision and errata management
  • Second-source strategy
  • Fleet diagnostics and reporting

How an engagement runs

Five stages, each with an exit you can inspect.

Stop at any stage and keep the artefacts.

  1. 01

    Discovery

    Constraints first: duty cycle, power, environment, certification target, volume and landed cost.

    1–2 weeks

  2. 02

    Prototype

    First board and firmware skeleton — a working unit that proves the risky part.

    4–8 weeks

  3. 03

    Design for production

    Layout hardening, DFM review, thermal and power validation, test fixtures, certification prep.

    6–12 weeks

  4. 04

    Pilot fleet

    A small run in real conditions, instrumented, with OTA and diagnostics already live.

    4–8 weeks

  5. 05

    Volume and sustaining

    Transfer to manufacturing, then revisions, sourcing changes and fleet operation under an SLA.

    Ongoing

Why both under one roof

The model and the machine, decided together.

An AI company sends a cloud API. A design house sends a board. Neither owns the trade-off.

  • Capture designed for the model

    Sensor choice, optics and sampling specified against the accuracy target.

  • Compute sized to the workload

    Silicon selected after the distilled model is profiled — no accelerator you never saturate.

  • Data that never leaves the site

    Inference at the station keeps imagery, audio and documents on your network.

  • One accountable team

    No argument about whether it is the model, the firmware or the sensor.

Next step

Bring the constraint, not the spec sheet.

Cycle time, power budget, certification target, landed cost — we will tell you the size of the programme.