Advice & specification
Analyse the workload and produce a technically reasoned system architecture, purchase specification or upgrade plan.
CMP / 03 Urquhart / capability
Computing & systems
Computers designed around the work they need to perform.
Specialist computer-system design, optimisation, technical diagnostics and advice for AI, engineering, development, astronomy, remote operation and demanding data workloads.
Computing principle / 01
The most expensive computer is not automatically the correct computer.
Performance before branding. Workload before specification. Value before unnecessary expenditure. Reliability and total system balance determine whether a machine is fit for purpose.
Physical compute
Where software, local AI and engineering meet the machine.
Workload-driven computing is part of the system architecture. Local inference, development, data processing, thermals, power and reliability must be considered together—not as an isolated list of components.
Compute→Local AI→SARIEL
Workload-driven architecture
Custom workstations
Computers should be designed and configured around the work they need to perform. Component balance, thermal behaviour, power, storage and upgrade paths matter more than a headline specification.
- Workstation design and specification
- Component and compatibility analysis
- Upgrade planning
- Storage and network architecture
- Cooling and thermal planning
- Power-supply sizing
- Operating-system configuration
- Performance validation
Local compute
AI workstations
Local AI systems are specified against model size, inference patterns, development workload and reliability requirements. The objective is sufficient, balanced compute—not expensive components without a reason.
- GPU and VRAM planning
- CPU and GPU workload balance
- System memory requirements
- Storage throughput
- Model-size planning
- Local inference and development
- Cooling and power requirements
- Upgrade paths
Measure first. Upgrade second.
Performance diagnostics
Slow or unreliable systems should be measured before parts are replaced. Diagnostics isolate the actual limiting resource and distinguish hardware constraints from software, configuration, thermal or network problems.
- CPU and GPU bottlenecks
- RAM and VRAM limitations
- Storage performance
- Thermal throttling
- Background processes
- Driver and software conflicts
- Network limitations
- Power and cooling configuration
Recover useful performance
System optimisation & remediation
A structured system review can remove unnecessary load, resolve conflicts and improve clarity without treating the machine as a disposable asset.
- Operating-system cleanup
- Bloatware and startup reduction
- Application rationalisation
- Driver and firmware review
- Process and resource analysis
- Disk-utilisation review
- Windows configuration review
- Reliability diagnostics
Acquisition to processing
Astronomy computing
Observatory and astrophotography computers must remain stable around demanding devices, large image data and unattended operation. Architecture considers the complete imaging workflow and the conditions at the telescope.
- Telescope control computers
- Remote imaging systems
- Processing workstations
- Plate-solving and guiding
- Weather and safety controllers
- Image-storage systems
- USB and device stability
- Remote recovery
Beyond the office
Remote & edge computing
Remote sites need efficient local processing, observable health and a planned recovery path. Systems can be designed for workshops, observatories, monitoring stations and off-grid installations.
- Mini-PC and embedded architecture
- Remote administration
- Unattended startup
- Remote recovery
- Secure networking
- Low-power systems
- Local data processing
- Edge AI inference
Data with structure
Storage & data systems
Storage is planned around working-set speed, capacity, backup, archive and recovery needs rather than a single drive specification.
- SSD and NVMe architecture
- Local and external backup
- NAS integration
- Archive storage
- Data migration
- Storage consolidation
- File organisation
- Scientific and image data
Reliable technical environments
Development systems
Development machines and remote technical workstations can be configured as coherent, reproducible environments for software, AI and engineering work.
- Development environment setup
- Git and source control
- Compilers and package managers
- Python and Node environments
- Containers where appropriate
- GPU compute configuration
- Local AI runtimes
- Remote development
Privacy & system hardening
Reduce unnecessary exposure. Preserve practical usability.
Configuration can include privacy settings, telemetry reduction, unwanted software removal, browser and account review, remote-access review, backup planning and sensible security settings. This is system configuration—not penetration testing or offensive cybersecurity.
- Privacy and telemetry review
- Software exposure reduction
- Startup and account review
- Remote-access review
- Backup strategy
- Sensible security configuration
Technical advice
Ask before purchasing—or before discarding a useful system.
Independent advice can clarify what GPU, VRAM, RAM, storage, mini PC or workstation architecture the workload actually needs, and whether an existing machine can be upgraded or repurposed.
Optimisation
Measure and improve an existing machine, resolving avoidable constraints before recommending replacement hardware.
Complete system solution
Design, specify, configure and validate a complete computing system where the requirement calls for integrated delivery.
Clear commercial starting points
Measure the machine before replacing it.
Defined diagnostics and specifications make specialist computing accessible without turning the service into generic repair work.
Computing enquiry
Describe the workload before choosing the hardware.
Tell us what the system must run, the data it handles, where it operates and what is limiting the current machine. We will start with the evidence.
Discuss a project