Four disciplines. One team accountable for the result.
Each area below lists the problems it addresses, what we deliver, where it applies and what you should expect to gain. Written for the people who will use the system, not only the people who build it.
Artificial Intelligence
Practical AI built into the way your organisation already works: models, agents and analytics that read documents, forecast demand and answer questions from your own data.
Problems addressed
- Staff spend hours reading, sorting and summarising documents by hand.
- Useful operational data exists but nobody has time to analyse it.
- Off-the-shelf AI tools cannot safely access internal systems or data.
- Forecasts are built in spreadsheets and go out of date quickly.
Services
- AI engineering and implementation
- Machine learning models
- AI agents and assistants
- Computer vision
- Natural language processing
- Predictive analytics
- Enterprise AI integration
Example applications
- 01.1An assistant that drafts engineering reports from site notes and templates.
- 01.2Demand forecasting for maintenance, stock or service teams.
- 01.3Visual inspection that flags defects from camera images.
- 01.4Search across internal documents with answers that cite their sources.
Relevant technologies
- Python
- PyTorch
- Local and hosted LLMs
- Vector search
- OpenCV
- REST APIs
Expected benefits
- Less time spent on repetitive reading and writing.
- Decisions supported by data you already hold.
- Models that can run locally where data must stay on site.
Software & Automation
Custom software and automation that connects disconnected systems, removes manual re-keying and gives teams one reliable view of their operations.
Problems addressed
- The same data is typed into three different systems.
- Critical processes depend on email chains and spreadsheets.
- Legacy software is hard to change and does not integrate.
- Managers lack a live view of what is happening across sites or teams.
Services
- Custom software development
- Workflow and business process automation
- API development and integration
- Enterprise systems integration
- Data management systems
- Operational dashboards
- Modernisation of existing software
Example applications
- 02.1Automatically generating engineering documentation from project data.
- 02.2Connecting a CRM, finance system and job scheduler through APIs.
- 02.3A live dashboard combining asset, job and sensor data.
- 02.4Replacing a fragile spreadsheet process with a maintained web tool.
Relevant technologies
- TypeScript
- Python
- n8n
- REST and webhooks
- PostgreSQL
- Next.js
Expected benefits
- Fewer manual steps and fewer transcription errors.
- Systems that share data instead of duplicating it.
- Software your team can maintain and extend.
Engineering Systems
Electronics, embedded and connected hardware, from sensor integration to working prototypes, designed alongside the software that makes them useful.
Problems addressed
- Equipment condition is only checked during manual inspections.
- An idea for a device needs a working prototype before investment.
- Industrial equipment cannot report data to modern software.
- Hardware and software suppliers do not talk to each other.
Services
- Electronics development
- Embedded systems
- Industrial automation
- IoT and sensor integration
- Robotics
- RF and communication systems
- Engineering prototyping
Example applications
- 03.1An embedded monitoring unit reporting temperature, vibration or humidity.
- 03.2Retrofitting sensors to existing plant and streaming data to a dashboard.
- 03.3A functional prototype of an intelligent assistive device.
- 03.4Wireless links for equipment in hard-to-reach locations.
Relevant technologies
- Embedded C/C++
- Microcontrollers
- Raspberry Pi
- Software-defined radio
- MQTT
- ROS
Expected benefits
- Earlier warning of faults through continuous monitoring.
- A tested prototype to support funding or procurement decisions.
- One partner accountable for hardware and software together.
Research & Development
Structured technical investigation for ideas that are not yet proven: feasibility studies, proofs of concept and prototype validation with clear findings.
Problems addressed
- It is unclear whether a technical idea is feasible or worth funding.
- A new technology looks promising but its limits are unknown.
- A proof of concept is needed to support a bid or business case.
- Internal teams lack time to explore emerging technology properly.
Services
- Technical feasibility studies
- Proof-of-concept development
- Prototype validation
- Applied AI research
- Engineering innovation
- Evaluation of emerging technologies
Example applications
- 04.1Testing whether machine learning can classify a particular signal or image type.
- 04.2A short feasibility report with a working demonstrator.
- 04.3Benchmarking local AI models against a defined task.
- 04.4Validating a prototype against agreed acceptance criteria.
Relevant technologies
- Python
- Jupyter
- SDR toolchains
- Simulation
- Rapid prototyping
- Test rigs
Expected benefits
- An evidence-based decision before committing a full budget.
- Documented findings, including what did not work.
- A clear route from experiment to deployable system.
Not sure which area fits?
Most projects involve more than one. Describe the problem in plain terms and we will suggest an approach.