Google Cloud where data and machine learning are the point.
Organisations usually arrive at Google Cloud for a reason: BigQuery, Vertex AI, or a team that wants Kubernetes done properly. We build the surrounding structure so those strengths are usable by more than the one team that introduced them.
BigQuery is easy to start and easy to leave running.
Google Cloud rewards teams that move fast, which is also how a single analytics project becomes an estate nobody planned. Projects proliferate, IAM is granted directly to individuals, a scheduled query scans a terabyte every hour, and the cost conversation begins six months after the architecture decisions were made.
Projects with no hierarchy
No folder structure and no organisation policy, so there is nowhere to attach guardrails, budgets or a data residency rule.
Query cost as a surprise
Unpartitioned tables, SELECT * in a scheduled job, and no per-project budget alert until finance asks the question.
Notebooks in production
A model trained in a notebook, deployed by hand, with no pipeline, no versioning and no way to reproduce the result that justified it.
Structure first, then the workloads that made you choose GCP.
Organisation & IAM
Folder and project structure, organisation policies, service accounts with workload identity, and access granted through groups rather than to people one at a time.
BigQuery & the data layer
Partitioning and clustering that reflect how the data is actually queried, dataset boundaries that match ownership, materialised views where they pay, and cost controls that bite before the invoice does.
GKE & Cloud Run
Cloud Run for most services because the operational burden is far lower, GKE when the workload genuinely needs it, with autoscaling and ingress designed rather than defaulted.
Vertex AI
Training and serving pipelines, model registry, evaluation and monitoring, so a model has a lifecycle instead of an author.
MLOps & governanceStreaming & pipelines
Pub/Sub, Dataflow and scheduled transformation built as code with tests, rather than as a chain of scheduled queries that only one person understands.
Cost & quota control
Budgets, committed use discounts, slot reservations where the workload justifies them, and per-team visibility of what each dataset and service costs.
Small increments, your engineers in the room.
Assess
Organisation structure, IAM, network design, data model, cost profile and how work currently reaches production.
Foundation
Folders, policies, networking, identity and the deployment pipeline, as Terraform in your repositories.
Workloads
The analytical or serving workloads themselves, refactored where the return justifies it and left alone where it does not.
Hand over
Runbooks, cost dashboards, monitoring and a named owner, with the reasoning written down rather than held in our heads.
We will tell you when the answer is not Google Cloud.
Provider choice should follow the workload, the data gravity and the skills you can hire in your market. Google Cloud is frequently the best answer for analytics and machine learning, and frequently not the best answer for an estate whose identity and productivity stack is already Microsoft. We do not have a partner quota to fill, so the recommendation can be the honest one.
Questions we get asked
We only use BigQuery. Is that enough of a reason to be here?▼
It is a perfectly good reason, and it is how a lot of estates start. The work then is usually to give it structure — ownership, partitioning, cost control and a pipeline — rather than to expand the footprint for its own sake.
Can you cut our BigQuery costs?▼
Often substantially. Partitioning and clustering, removing SELECT *, pruning scheduled queries that nobody reads, materialising the expensive repeated ones and choosing between on-demand and slot reservations are all common wins. We measure before and after so the saving is a number rather than a claim.
GKE or Cloud Run?▼
Cloud Run for most services. It removes an entire operational discipline you would otherwise have to staff. GKE when you need node-level control, specific hardware, or you already run Kubernetes competently elsewhere.
Do you work across GCP and AWS together?▼
Yes, and it is common where analytics sits on Google Cloud and the product sits on AWS. The work is in making identity, networking, deployment and cost visibility consistent so the split is a decision rather than an accident.
Can everything stay in an EU region?▼
Yes. Projects, datasets, service deployments and model endpoints can be constrained to European regions through organisation policy, so residency is enforced by the platform rather than by everyone remembering.
One free hour. No pitch, no obligation.
Bring the problem you are stuck on — an integration that keeps breaking, a cloud bill nobody can explain, an AI project that is all demo and no product, or a platform you are about to invest in. You will leave with a straight answer and a written summary, whether or not you ever work with us.
- Architecture and integration review
- AI feasibility — what will actually work, and what will not
- AWS, Azure and Google Cloud cost and design
- Business process audit and ISO readiness
- Technical due diligence before you invest or acquire