Google Cloud
We build data, analytics and AI systems on Google Cloud, with BigQuery, Vertex AI and GKE under one control model.
What We Do on Google Cloud
We start with one engagement. A data foundation moves warehouses into BigQuery, the analytics warehouse. An AI platform puts models on Vertex AI, the model service. A container platform runs services on Google Kubernetes Engine (GKE), the managed Kubernetes service.
Each engagement starts with a landing zone: folders, projects, network, identity and logging as code. Data lands first, applications follow and models come last. Cost labels and budgets sit on each project.
You provide owners for data, security and finance, access and service levels. You decide the migration order and which data may move. The plan records both, with a rollback step for each wave.
The Services
The services we deliver on Google Cloud, and what each one does here.
- AI Strategy and GovernanceAI use cases are chosen against your data and a written risk policy.
- Generative AI ApplicationsAssistants and search on Vertex AI use your own documents.
- AI Agents and AutomationAgents call Google Cloud services through scoped accounts and leave an audit trail.
- Private AIModels run inside your projects, on your data, with your own keys.
- Speech and Vision AISpeech and vision models read media in Cloud Storage, the object store, close to the data.
- Document AutomationDocuments are read with Document AI, the document service, checked against your rules and posted.
- Machine Learning and Predictive AnalyticsModels train on Vertex AI and serve with drift and quality monitoring.
- Data Engineering and GovernancePipelines load BigQuery and Cloud Storage, with quality checks and lineage.
- Analytics and Business IntelligenceDashboards read BigQuery through one governed set of definitions.
- Product Engineering and SaaSMulti-tenant products run with per-tenant quotas and cost limits.
- Cloud Strategy and MigrationWorkloads move in waves onto Google Cloud, with a landing zone and a tested rollback.
- Hybrid Cloud and Data CentrePrivate networking joins your data centre to Google Cloud, with placement written down.
- AI InfrastructureGPU capacity is sized from the workload and tracked to each project.
- DevOps and Platform EngineeringPipelines use Cloud Build, the build service, and your identity provider.
- Managed Cloud and Site ReliabilityWe run the estate: monitoring, patching, incident response and monthly reviews.
Where These Projects Go Wrong
Google Cloud rewards a tidy estate. These faults are the ones we change first.
Cost Sized From a Price Sheet
Teams size the estate from list prices and meet the bill after go-live. We measure what runs today, then set budgets and labels before migration.
Projects Multiply Without Owners
New projects appear without labels, logging or a budget. We design folders and policy first, so each project has an owner and a cost limit.
Data Copied Without a Model
Teams copy data into separate stores and the numbers diverge. We agree one governed model and one route into BigQuery before any pipeline moves.
How a Project Starts
The first weeks end with a plan finance and security can sign.
Map the Estate
We inventory projects, workloads, data sources and spend. You receive an estate map and a cost baseline. You choose the first workload.
Build a Landing Zone
We write folders, network, identity and logging as code. You receive the design and a test project that proves it. You approve it before migration.
Rehearse the First Wave
We write the migration runbook and run one workload through it. You receive the timed test result and a way back. You pick the start date.
Questions
What buyers ask before a Google Cloud programme starts.
Can you take over the Google Cloud estate we already run?
Yes. We review the projects, folders, networks and billing you have. We keep what works and fix what blocks control.
How do you move workloads without a long outage?
We move in waves and check data before traffic moves. Each wave has a tested rollback and settles before the next.
How do you stop Google Cloud spend rising?
Budgets and labels sit on every project. We compare use with the baseline and review commitments before renewal.
Can analytics and AI teams share one governed platform?
Yes. One route into BigQuery carries access rules and quality checks, so both teams report the same numbers.
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