Cloud and AI,built to scale.
Scalable cloud infrastructure and practical AI integration — from moving your first workload to running AI across your operations.
About our Cloud Computing & AI service
We design cloud foundations that stay fast, secure and affordable as you grow — then put AI to work on top of them, where it saves real time.
Cloud done well is invisible: pages load fast, releases don't cause downtime, backups exist and the monthly bill makes sense. We plan, migrate and run infrastructure on the platform that fits you — not the one we happen to prefer.
On top of that foundation we connect the AI models that suit each job — document reading, assistants, search, forecasting — with clear boundaries on what data each model can see.
Cloud ecosystems
Amazon Web Services
Well-architected AWS setups — compute, storage, databases and networking — with security and cost controls from day one.
Microsoft Azure
Azure environments for organisations already on Microsoft 365, with identity, access and backups done properly.
Google Cloud
Google Cloud for data-heavy work — BigQuery pipelines, serverless apps and analytics dashboards.
Kubernetes
Container platforms that scale services up and down automatically, with zero-downtime releases.
Terraform
Infrastructure as code, so every environment is repeatable, reviewable and quick to rebuild.
Observability
Monitoring, logs and alerts, so you hear about problems before your customers do.
AI platforms
Gemini
Google's Gemini models for multimodal work — documents, images and long reports.
ChatGPT
OpenAI models for chat assistants, drafting and pulling structured data out of messy text.
Claude
Anthropic's Claude for careful reasoning over long documents — and the assistants we build for clients.
Copilot
Microsoft Copilot rolled out across Microsoft 365, with sensible data boundaries and team training.
Llama (Meta AI)
Open models self-hosted inside your own infrastructure when data must never leave it.
Perplexity
Research assistants that answer with cited sources, for teams that need to check the facts.
Four steps.No surprises.
You always know what's happening, what's next and what it costs — before any work begins.
- 01AssessMap what you run today, what it costs and where the risks are.
- 02ArchitectA clear target design — platform, security, backups, budget — agreed before we build.
- 03Migrate & buildMove workloads in phases, automate deployments and connect the AI services.
- 04OperateMonitoring, cost reviews and improvements every month.
Proof, not promises.
Questions,answered.
It depends on what you already use and what you're building. Microsoft-heavy organisations often fit Azure, data and analytics work suits Google Cloud, and AWS has the broadest range of services. We recommend one after looking at your systems, skills and budget.
Yes. We migrate in phases — usually starting with the least risky workloads — with backups and a rollback plan at every step.
Right-sized servers, auto-scaling, budgets with alerts and a monthly review of what's being paid for. Most savings come from switching off what nobody uses.
We give each AI model only the data its task needs, use business agreements that exclude your data from training, and self-host open models when data must stay in-house.
Yes — monitoring, updates, security patches and help when something changes in your business.