Cloud & AI Infrastructure
“I don’t know where or how to run my models reliably and at scale.”
A model that works in a demo but falls over in production is worthless. We design your AI infrastructure to be reliable, scalable and tailored to your real constraints (public, sovereign or air-gapped cloud), from the very first deployment.
Infrastructure built to last, not to impress.
We settle the deployment model based on how sensitive your data is and what regulations you face, then make operations production-grade so your models keep up with the load over time.
- Architecture and migration to the cloud (AWS, Azure, GCP) or to sovereign and air-gapped infrastructure.
- Production readiness: CI/CD, monitoring and scaling for AI workloads.
- MLOps: training pipelines, model versioning, continuous deployment.
- AI infrastructure cost optimisation (FinOps).
The kind of projects we deliver.
Migration to a sovereign cloud
Migrating a data processing platform to a sovereign cloud, to meet strict regulatory requirements.
Industrialised MLOps pipeline
Industrialising a continuous deployment pipeline for predictive maintenance models, from retraining through to production release.
Air-gapped AI infrastructure
Deploying an isolated AI infrastructure for a high-security environment, with no external network connection.
Examples of the kind of projects our teams deliver.
Focused on results
A pragmatic approach built around real results and efficiency, not technology for show.
Scoping first
We define the project before we talk about tools, so we avoid dead ends.
Real-world delivery
We take solutions all the way to production, not just to prototype.
Plain speaking
Complex topics made clear and practical, with no needless jargon and no overpromising.
The questions we hear most often.
What’s the difference between a simple cloud migration and what you offer?
A standard cloud migration lifts what exists without changing it. We first settle the deployment model based on your regulatory constraints and data sensitivity, then make operations production-grade so your infrastructure holds up over time, not just at launch.
Do you work with AWS, Azure and Google Cloud?
Yes. We work with the three main cloud providers (AWS, Microsoft Azure and Google Cloud), as well as sovereign or air-gapped infrastructure when your security or regulatory constraints call for it.
What is FinOps, and why does it matter?
FinOps is the discipline of keeping cloud infrastructure costs under control and optimised over time. With AI workloads, costs can spiral quickly if nobody is watching; we build it in from the design stage rather than patching it up afterwards.
Do you also size GPU (Nvidia) infrastructure for AI?
Yes. We size and deploy the GPU compute needed to train and run your models, notably on Nvidia, whether in the public cloud or in a dedicated environment.
How long does it take to industrialise an MLOps pipeline?
It depends on how mature your current setup is and how many models need industrialising. Tell us about your situation and we’ll come back with an initial estimate tailored to your scope.
Can you take over infrastructure already started by another team?
Yes. We always start with an audit of what’s there, to identify what can stay, what needs consolidating and what should be rethought before going any further.
Have an AI infrastructure project to scope?
Tell us about your technical context and constraints. We’ll get back to you shortly.
