Services
Cloud foundations, delivery pipelines and data platforms your team can run
We work in three practices. Multi-Cloud Consulting builds governed landing zones, DevSecOps puts security gates inside delivery pipelines, and Data & AI sets up data platforms and evaluated AI. Every service below has its own page.
Multi-Cloud Consulting
Cloud foundations on AWS, Azure, Google Cloud or a hybrid estate: one governance model for hierarchy, policy, network, identity and cost, defined in Terraform your platform team owns.
AWS foundations and governance
A multi-account AWS organisation with OUs, service control policies and account vending in Terraform, so new workloads start governed.
Azure landing zones
Azure landing zones in Terraform: management groups, policy at scale and a hub-and-spoke network that every subscription inherits.
Google Cloud foundations
A Google Cloud hierarchy with folders, organisation policies and Shared VPC, plus Google Kubernetes Engine clusters your pipeline deploys to.
On-premises and hybrid cloud
Private connectivity, one identity boundary and shared governance between your data centres and the cloud, with each side kept operable.
DevSecOps
Delivery pipelines with SCA, SAST, image scanning and quality checks running as gates, Terraform for the infrastructure, and one image promoted from SIT to UAT to PROD.
Cloud-native DevOps on Kubernetes
One image built per change, Helm charts per service, and a GitOps controller that promotes it from SIT to UAT to PROD on Kubernetes.
CI/CD pipelines with enforced gates
Pipelines that run unit tests, SCA, SAST, quality and image scans in cost order, and send every failed gate to the team that owns it.
Infrastructure as code with Terraform
Terraform modules, a plan reviewed on every merge request, applies only from the pipeline, and drift reported before it becomes an incident.
Security gates inside the pipeline
SCA, SAST, image scanning, DAST and a penetration test placed as pipeline gates, each result owned by development, QA or security.
Data & AI
Data platforms, pipelines as code, a semantic layer with agreed metrics, and AI applications with evaluation that gates each release, all under governance your team runs.
Data platform architecture
A lakehouse or warehouse organised into landing, curated and serving zones, with data contracts, access policy and lineage your team owns.
Data engineering and pipelines
Contracted ingestion, tested transformations in dbt or Apache Spark, and lineage at every stage, run from version control through one deployment path.
Analytics and semantic models
A semantic model with governed metric definitions, so board reports and team dashboards in Power BI or Looker show the same numbers.
AI solutions on governed data
Retrieval-based AI applications on your governed data, with an evaluation set that gates each release and monitoring that feeds back into it.
How we work
From first conversation to handover
The same sequence applies whichever practice you start with. Each step ends in something your team reviews before the next one begins.
01
Assess and scope
We review your current estate, delivery process and constraints with your engineers, then agree the scope, the owners and the decisions that have to come first.
02
Design and sign-off
We write down the architecture, the controls and who owns each part. Your team reviews and signs it off before any infrastructure or pipeline code is written.
03
Build in your repositories
We build as code in your repositories and through your change process, so your engineers review each increment as it lands rather than at the end.
04
Hand over and tune
We hand over runbooks and decision records and work alongside your engineers until they run it themselves. Then we tune cost, performance and controls with you.
Published work
Engagements written up in full
Each write-up covers the starting point, the architecture as delivered and the stack, with figures you can open full size.
A governed Azure foundation a bank could build on
We designed and built an Azure landing zone for a bank: governance, hub-and-spoke networking, security policy and Terraform-delivered infrastructure.
We delivered a scalable, secure and efficient Azure landing zone that accelerated the bank's cloud adoption. Application teams deploy into subscriptions that already carry networking, policy, monitoring and backup, which…
- Azure
- Terraform
- Azure DevOps
Omnichannel banking on Microsoft Azure, private by default
An omnichannel banking platform on Azure: one controlled edge for every channel, workloads on AKS, and data services reached only through private endpoints.
We delivered a scalable, secure and efficient application architecture on Microsoft Azure. Every channel enters through one controlled edge and every data service is reachable only through a private endpoint,…
- Azure
- AKS
- Front Door
- Keycloak
Security gates inside every pipeline, not after them
A DevSecOps delivery platform with GitLab, Jenkins and Argo CD on GKE, where every quality and security check is a pipeline gate owned by a named team.
The platform is designed to let teams release more often without letting security and quality review fall behind. Because checks run inside the pipeline and every failure reaches its owner…
- Jenkins
- Argo CD
- GKE
- GitLab
Start from the platform you have today
Tell us what you run, what is holding back the next release or workload, and who owns it. An engineer replies with questions and a proposed first step.