Understand the system.
Define the operational problem, physical behaviour, interfaces, environment, resources and consequences of failure.
01 / CAPABILITIES
Engineering across the boundaries.
Six connected disciplines. We bring physical systems, computing, intelligence and security together to address engineering problems that do not fit within a single specialism.
We welcome engineering enquiries, feasibility discussions and research collaborations. For each enquiry, we agree the scope, evidence, resources and any specialist partners required. Research interests and development work are distinguished from demonstrated product capabilities.
EXPLORE OUR EXPERTISE
CAPABILITY 01
Electronics, embedded computing & systems engineering
We design and integrate electronic, computing and control systems for demanding applications. Our experience brings together hardware, firmware, software, sensing, communications and power, with architectures selected according to the requirements of the system rather than a particular processor or platform. We consider performance, reliability, resource efficiency, security and maintainability as interconnected engineering requirements.
Connected sensing and energy-aware design ↗The engineering challenge
A device can satisfy its component specifications and still fail as a system. Timing, electrical noise, power states, communication losses and environmental exposure interact across the hardware–software boundary.
Technical expertise
We bring electrical and electronic engineering together with embedded firmware, application software, instrumentation and system integration. Work can span requirements and interface definition, sensing and signal acquisition, power architecture, communication protocols and control. Bare-metal, real-time and operating-system-based designs are choices to evaluate against the application, not limits on our expertise.
How we investigate
We examine duty cycles, latency, sampling behaviour and recovery from faults alongside compute and energy budgets. Prototypes and measured traces help distinguish a component limitation from an integration problem, and make trade-offs visible before an architecture is committed.
Potential applications
Remote instruments, connected equipment, low-power sensing, monitoring systems and embedded computing for intelligent machines.
CAPABILITY 02
Artificial intelligence, machine learning & distributed intelligence
We research, develop and evaluate intelligent systems using machine learning, computer vision and distributed AI. Our experience includes computer vision, model development and evaluation, adversarial robustness and security analysis of distributed and federated learning. We assess how intelligence can be integrated into physical and connected systems for each project. We consider the complete lifecycle, including data quality, model behaviour, computational constraints, security, reliability and performance in real operating environments.
Perception and adversarial machine learning ↗The engineering challenge
A model that performs well on a curated dataset may behave differently under changed lighting, unfamiliar equipment, corrupted measurements or constrained computing resources. Distributed learning adds questions about identity, coordination and the trustworthiness of contributions.
Technical expertise
Our experience connects data preparation and experimental design with computer vision, machine learning, model evaluation and embedded intelligence. It includes detection and segmentation, comparative model evaluation, analysis of adversarial behaviour and security research into distributed and federated learning. Resource constraints and deployment requirements are assessed for the intended system. We consider centralised, edge and distributed architectures according to the data, operational need and available resources.
How we investigate
We define meaningful baselines and evaluation conditions, examine failure cases and dataset limitations, and measure the consequences of model and deployment choices. Accuracy, latency, energy, robustness and maintainability need to be considered together. A result is useful when its limitations are understood as well as its headline metric.
Potential applications
Intelligent inspection, interpretation of sensor data, visual perception, distributed monitoring and learning across connected devices.
CAPABILITY 03
Cybersecurity, threat modelling & secure systems
We bring cybersecurity research and engineering to connected technologies, distributed systems and critical cyber-physical infrastructure. Our expertise encompasses security architecture, threat analysis, network and device security, identity and privacy, vulnerability research, side-channel analysis, adversarial machine learning and the security of intelligent and autonomous systems. We examine threats, dependencies and operational constraints to inform evidence-led decisions about security and resilience.
Network privacy and security analysis ↗The engineering challenge
Security weaknesses often arise between components, organisations or assumptions. Authentication alone does not establish trust in a physical measurement, and encryption alone does not remove every information leak.
Technical expertise
We combine security architecture and threat modelling with research into network privacy, device identity, side channels, malware detection and the security of distributed intelligence. Analysis considers assets, trust boundaries, dependencies, attacker capabilities and the consequences of compromise. Structured methods such as attack trees and STRIDE can support the reasoning without replacing system-specific judgement.
How we investigate
We map credible attack paths, examine how failures combine and use targeted experiments to test assumptions. Findings can inform architectural changes, defensive priorities and verification criteria. The scope and authority for any security testing must be agreed for the system in question.
Potential applications
Connected products, networked instruments, distributed computing, intelligent systems and security-sensitive engineering programmes.
CAPABILITY 04
Cyber-physical systems & industrial security
We work at the intersection of computing, communications, control and physical processes, where the integrity of digital systems directly affects equipment, infrastructure and operational behaviour. Our engineering and research experience includes industrial control security, sensor and actuator integrity, device identification, process-aware security and the resilience of interconnected industrial systems. We consider cybersecurity alongside physical behaviour, operational constraints and the consequences of system failure.
Industrial and cyber-physical security ↗The engineering challenge
In an industrial system, compromised data or delayed commands can change the physical process. Security controls must account for availability, deterministic behaviour, equipment limitations and the practical constraints of an operating plant.
Technical expertise
Our research covers industrial-control networks, process-aware security, sensor and actuator identification, and experimental testbeds. We bring together the behaviour of the controller, network and process to understand what a security event means for the wider system. This includes considering legacy interfaces and the dependencies introduced when operational technology is connected to new services.
How we investigate
We use process knowledge, threat analysis and controlled experimentation to investigate plausible failure and attack conditions. Independent observations of physical behaviour can complement digital telemetry. Evaluation should distinguish a useful anomaly signal from evidence strong enough to justify an operational intervention.
Potential applications
Manufacturing equipment, process monitoring, water and energy systems, remote industrial assets and connected control environments.
CAPABILITY 05
Robotics, autonomy & intelligent systems
We bring together sensing, perception, computing, navigation, control and secure communications in the research and development of intelligent robotic and autonomous systems. Our engineering and research work connects embedded integration with investigations of sensing, perception, navigation, physical interaction and robotic-system security. New platform capabilities require project-specific feasibility work, prototyping and testing. We consider uncertainty, degraded sensing, communication failures and challenging conditions, with expertise relevant across terrestrial, aerial, maritime, subsea and industrial environments.
Robotic behaviour and verification ↗The engineering challenge
Autonomy depends on more than perception accuracy. Sensing uncertainty, actuation, communication, power and the surrounding environment all affect what a machine can safely and reliably do.
Technical expertise
We connect systems engineering with research in robotic integration, perception, distributed intelligence and robotic-system security. Work can address the interfaces between sensing, navigation, control, onboard computing and communications. The same system-level questions arise in different forms across terrestrial, aerial, maritime, subsea and industrial platforms.
How we investigate
We investigate behaviour under degraded sensing, limited connectivity and changing operating conditions. Integration prototypes, observable workflows and repeatable tests help establish which functions are supported by evidence and where further development is needed. Autonomy claims should be bounded by the tested operating conditions.
Potential applications
Robotic inspection, remote sensing platforms, autonomous-system research, connected robotic operations and human-supervised intelligent machines.
CAPABILITY 06
Applied engineering R&D, verification & assurance
We combine practical engineering and scientific research to investigate challenging technical problems, develop system architectures, create prototypes and evaluate performance against measurable requirements. Our approach integrates electronics, computing, intelligent systems and cybersecurity, with emphasis on system-level behaviour, experimental validation, resilience and reproducibility. We use engineering evidence to support informed decisions about feasibility, technical risk, design improvement and future development.
Selected research and experimental methods ↗The engineering challenge
Early technical decisions are often made with incomplete evidence. A convincing demonstration may leave unanswered questions about repeatability, integration, failure handling or the limits of the proposed approach.
Technical expertise
We combine practical engineering with research design, experimental measurement and critical evaluation. Work can range from feasibility studies and architecture development to prototypes, comparative experiments and verification against defined requirements. Electronics, computing, intelligent systems and cybersecurity are considered as parts of the same engineering problem.
How we investigate
We identify the decision an experiment must support, define acceptance criteria and record assumptions before interpreting results. Baselines, controlled comparisons, reproducible procedures and clear reporting make evidence transferable. Negative results and unresolved limitations are part of the engineering record, not details to conceal.
Potential applications
Technology feasibility, design comparison, integration risk reduction, research collaboration and evidence to support further development or independent assurance.
A SHARED ENGINEERING APPROACH
From the question to the evidence.
Architect & threat-model.
Design hardware and software together. Identify trust boundaries, attack surfaces, adversaries and resilience requirements.
Prototype & integrate.
Connect the hardware, software, sensing, intelligence and communications needed to address the critical technical questions.
Test performance & security.
Evaluate efficiency, robustness, failure handling and adversarial behaviour under representative operating constraints.
Verify, document & refine.
Compare reproducible evidence with acceptance criteria. Record limitations and use the results to refine the system.