Cloud & AI
Infrastructure
The platform underneath your AI: cloud migration, MLOps pipelines, GPU sizing, and cost control across AWS, Azure, and GCP. Built by a team that has run this in production for 15+ years.
The discipline
AI runs on infrastructure decisions.
Model quality gets the attention, but infrastructure decides the outcome. Where your data lives, how models reach production, what a month of inference costs. These choices get made early and paid for over years.
We're AWS and Microsoft partners with delivery experience across all three major clouds. Our teams design landing zones, build MLOps pipelines, and keep the cloud bill explainable after the migration is done.
Cloud migration & landing zones
Account structure, network layout, identity, and guardrails are designed before the first workload moves. We stage the migration so you can always roll back.
MLOps pipelines
Model registry, CI/CD for models, and drift monitoring. If a model can't be retrained and redeployed in a day, it's a liability, not an asset.
GPU & inference infrastructure
GPU clusters sized correctly for training and inference. We tune quantisation, batching, and autoscaling so you pay for work done, not idle capacity.
FinOps cost optimisation
Consistent tagging, reserved capacity planning, and workload scheduling. In most reviews we find a real share of the spend doing nothing useful.
Applications
The problems we get called for.
Regulated data to Azure
On-prem systems moved to Azure, with data residency, encryption, and audit requirements documented for regulators before cutover.
GPU clusters for LLM inference
Self-hosted LLM serving for teams that can't send data to outside APIs. Sized for your real traffic, not a benchmark chart.
Kubernetes platform builds
Production-grade Kubernetes with GitOps deployment, secrets management, and monitoring your own engineers can run day to day.
DR & backup architecture
Recovery targets set in hours and minutes, then tested. A DR plan that has never run a failover drill is just a document.
Delivery approach
Assess, migrate, operate.
Assessment & baseline
We take stock of workloads, dependencies, and current spend. Every migration decision traces back to this baseline.
Landing zone build
Accounts, networking, identity, and policy guardrails are set up and reviewed before any workload moves.
Staged migration
Workloads move in waves, lowest-risk first. Each wave is checked against the baseline before the next one starts.
Operate & optimise
Monitoring, cost reviews, and runbooks handed to your team. We stay on through the first quarter of running it.
Technology stack
Standard tooling. Portable by design.
Common questions
Cloud & infrastructure, answered directly.
Which cloud do you recommend - AWS, Azure or GCP?
Depends on your existing stack, data residency needs and where your team already has skills. We're AWS and Microsoft partners with delivery experience across all three, and often the right answer is a primary cloud plus a narrow secondary workload, not a single-vendor bet.
Can you migrate regulated or on-prem data safely?
Yes - we've moved on-prem systems to Azure with data residency, encryption and audit requirements documented for regulators before cutover, and every migration is staged in waves so you can roll back.
How do you stop cloud costs from spiraling after migration?
FinOps work - consistent tagging, reserved capacity planning and workload scheduling - built in from the start, not bolted on later. In most cost reviews we run, a real share of spend is doing nothing useful.
Do we need our own GPU cluster, or can we use API-based models?
If you can send data to an outside API, that's usually cheaper to start. Self-hosted GPU infrastructure makes sense once you need data to stay in-house, or once inference volume makes per-token API pricing more expensive than owning the hardware.
What does an MLOps pipeline actually give us that we don't already have?
A model registry, CI/CD for models and drift monitoring, so a model that starts underperforming gets caught and retrained in a day, not discovered three months later in a business review.
How long does a cloud migration take?
Depends on workload count, but we run assessment and landing-zone build first, then move workloads in waves, lowest-risk first. We stay on through the first quarter of operation after cutover, not just through go-live.
Start the assessment
What is your cloud bill
actually buying?
Book a free infrastructure review. We'll look at what you're running, flag the biggest cost and risk items, and outline a migration or MLOps plan your team can act on.