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Services

AI Platform Engineering

Give your data scientists and engineers one platform to train, deploy and run models. We design and build AI platforms on Google Cloud, Azure, AWS or hybrid — containerised on Kubernetes or OpenShift, GPU-ready, and defined as code so every environment is repeatable.

We design and build the platform your AI runs on — cloud or hybrid, containerised, GPU-ready and defined as code — so teams ship models on shared, secure foundations instead of one-off setups.

What's included

  • AI platforms on Google Cloud, Azure, AWS or hybrid
  • Containerised workloads on Docker, Kubernetes and OpenShift
  • GPU-enabled training and inference environments
  • Infrastructure as code with Terraform, CloudFormation and Ansible

How it works

A clear path from the first conversation to a system your team runs.

  1. Step 1:

    Platform assessment

    We map your workloads, teams, security needs and current spend, and agree what the platform must do.

  2. Step 2:

    Reference architecture

    Cloud, network, clusters, GPU pools and access controls designed around your constraints.

  3. Step 3:

    Build with infrastructure as code

    Every environment is written in Terraform, CloudFormation or Ansible, reviewed and repeatable.

  4. Step 4:

    Enable teams and hand over

    Templates, runbooks and pairing, so your engineers onboard new models on their own.

Technologies we work with

Chosen for your environment: we work in your cloud and with the tools you already use.

Cloud

  • AWS
  • Google Cloud
  • Vercel
  • Microsoft Azure

Containers and orchestration

  • Kubernetes
  • Docker
  • OpenShift

Infrastructure as code

  • Terraform
  • CloudFormation
  • Ansible

In practice

Professional services

A Repeatable ML Deployment Platform for a French Professional-Services Firm

Several AI prototypes, but every deployment was manual and no two environments matched.

Days → minutesDeployment time, on one automated pipeline

Client engagement · anonymised

Read the case study

Frequently asked questions

Which cloud do you recommend?

The one you already run. We build on Google Cloud, Azure or AWS, or across them in a hybrid setup, and only recommend a change when there is a clear reason.

Can the platform run on-prem or without internet access?

Yes. Kubernetes and OpenShift run in your own data centre as well as in the cloud, including air-gapped environments.

How do you keep GPU costs under control?

Separate node pools for training and inference, autoscaling down when idle, quotas per team and cost reporting by namespace, so spend is visible and bounded.

Why infrastructure as code?

Every environment is described in code and reviewed like code, so it can be rebuilt, audited and changed safely instead of drifting over time.

What does our team own at hand-over?

The repositories, infrastructure code, runbooks and dashboards. We pair with your engineers during the build so they can run the platform themselves.

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