Capabilities

From silicon to software to AI at the edge.

Six capabilities that combine into whole systems. Most engagements draw on several at once, that is the point of keeping hardware, firmware, and AI under one roof.

Embedded Systems Design & Integration

We take embedded products from a rough idea to hardware you can field. Architecture and part selection, schematic capture, board bring-up, and the firmware that runs on it. Integration is where most of the risk sits, so we do that too rather than handing over a board and wishing you luck. Everything is built to be testable on the bench and debuggable after it ships.

Typical deliverables

System architecture and component selection
Schematic capture, board bring-up, and hardware debug
Bootloaders, drivers, and board support packages
Integration test rigs and bring-up documentation
Design-for-manufacture review ahead of production

Tools & platforms

ARM Cortex-MESP32Nordic nRFSTM32RTOSZephyr

Edge AI & On-Device ML

A model that works in a notebook is not the problem. Fitting it into a few hundred kilobytes, running it on a coin cell, and holding accuracy after two years of sensor drift is the problem. We do the quantisation, the memory budgeting, and the power measurement, then report what the model actually costs on the target part.

Typical deliverables

Model selection, training, and evaluation against field data
Quantisation and pruning to fit MCU-class memory budgets
On-device inference pipelines with measured latency and power
Data collection and labelling workflows
Accuracy-versus-power trade-off analysis

Tools & platforms

TensorFlow Lite MicroONNX RuntimePyTorchCMSIS-NNEdge Impulse

Custom Software & Firmware

Firmware that runs unattended for years is a different discipline from software you can restart. Bounded memory, defined failure behaviour, and an update path that cannot brick a deployed fleet. Above the device we build the services, APIs, and operator interfaces that make a fleet something a team can actually run.

Typical deliverables

Production firmware with unit and hardware-in-the-loop tests
Secure over-the-air update and rollback paths
Device APIs, backend services, and data pipelines
Operator dashboards and configuration tooling
CI pipelines, code review standards, and technical documentation

Tools & platforms

C / C++RustPythonTypeScriptNext.jsPostgreSQL

Ultra-Low-Power Wireless

Battery life is a systems problem, not a component choice. We budget energy across the radio, the duty cycle, the sensor, and the firmware together, then measure the result on hardware rather than trusting the datasheet. The target is a device that survives its stated service life in the environment it ships into.

Typical deliverables

Radio and protocol selection against range, power, and topology needs
Measured power budgets and projected battery life
Provisioning, pairing, and network commissioning flows
RF bring-up, antenna tuning, and range validation
Pre-compliance guidance ahead of certification

Tools & platforms

BLEThread / MatterLoRaWANZigbeeWi-Fi

Sensor Systems & Electronics

Measurement is where most sensing products quietly fail. We design the analogue front end, the conditioning, and the calibration that turn a weak physical signal into a number you can defend. That includes the drift, noise, and temperature effects that only appear once the device is in the field.

Typical deliverables

Sensor selection and analogue front-end design
Signal conditioning, filtering, and noise analysis
Calibration procedures and compensation routines
Environmental and drift characterisation
Data validation and quality-of-measurement reporting

Tools & platforms

Analog front endsMEMSEnvironmental sensingSignal processing

Technology & Management Consulting

Sometimes the useful deliverable is a decision, not a device. We scope what is genuinely buildable, review architectures before they get expensive, and give an independent read on technical risk. That includes saying plainly when an approach is not going to work.

Typical deliverables

Technical feasibility studies and architecture review
Build-versus-buy and vendor evaluation
Technology roadmaps and phased delivery plans
Independent risk assessment for embedded and AI programmes
Proposal and statement-of-work technical support

Tools & platforms

Architecture reviewRisk assessmentRoadmappingDue diligence

Neuromorphic & Event-Driven Systems

Conventional always-on sensing spends most of its power looking at nothing. An accelerometer sampled at 100 Hz burns almost every cycle confirming the machine is still fine. Event-driven hardware inverts that: the sensor emits data only when something changes, and a spiking co-processor decides whether it matters before the main processor wakes at all. Parts from Innatera, SynSense, and BrainChip have made this buildable rather than academic. We evaluate whether a product actually benefits, and design the system around the answer.

Typical deliverables

Feasibility assessment against your power and latency budget
Benchmarks comparing event-driven silicon with a conventional MCU pipeline
Architecture for a spiking wake stage ahead of a standard inference path
Sensor selection across event cameras, analogue microphones, and DVS parts
A recommendation against it when conventional hardware is the better answer

Tools & platforms

InnateraSynSense XyloBrainChip AkidaSpiking neural networksEvent-based sensing

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Capability statement (PDF)

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