Google Cloud engineered for the regulator, not for the demo
Google Cloud is the strongest data and AI platform in the market — and the easiest to misconfigure for a regulated enterprise. We are a Google Cloud Premier Partner with deep certification across Cloud Architect, Data Engineer, and ML Engineer, and we specialise in the work that makes GCP defensible inside a bank, an insurer, or a healthcare system: landing-zone design, VPC Service Controls, CMEK, Assured Workloads, and an AI platform built on Vertex that survives a security review.
What we deliver
Migration without the disruption
Lift-and-shift, re-platform, or re-architect — we design the right migration approach per workload and execute against a wave plan that minimises business disruption. Includes parallel-run periods, automated cutover playbooks, and documented rollback for every wave. Most clients are in production on GCP within ninety days of kick-off.
Vertex AI production stack
Vertex AI Workbench for development, Vertex Pipelines for orchestration, Vertex Model Registry for governance, and Vertex AI Endpoints for serving — with the model-monitoring, feature-store, and explainability components your data scientists actually need to ship past a model risk committee. We have shipped this end-to-end for regulated clients more times than we can count.
Workspace for regulated enterprises
Google Workspace configuration that meets the security baseline regulated industries actually need: Context-Aware Access, advanced DLP rules, Vault retention, encryption-key management, and Drive access control at scale. We migrate from Microsoft 365 and on-prem Exchange estates with calendar and mail integrity guaranteed.
Security and compliance by default
VPC Service Controls perimeters around your sensitive data, CMEK on every storage class, Cloud KMS or external HSM for key management, IAM Conditions for least-privilege access, Assured Workloads for regulated controls, and a Cloud Logging and Security Command Center setup that feeds your existing SIEM. The default deployment passes audit; the optional hardening passes a CISA red team.
BigQuery lakehouse and AI-ready data
BigQuery as the warehouse, Dataflow and Pub/Sub for streaming, Dataform or dbt for transformation, and Looker for BI — a complete data platform that doubles as the data foundation for Vertex AI. Reservations and slot management tuned for your workload mix to keep cost predictable.
Managed operations
Twenty-four-seven monitoring, incident response, FinOps cost optimisation, security patching, capacity planning, and quarterly business reviews. We operate GCP environments at scale for clients across three continents, with SLAs that reflect the criticality of the workloads.
Cloud migration progress
How a query actually flows.
A real trace through the sovereign stack. Six stages, ~1.4 seconds end-to-end, zero packets leaving your perimeter.
How we deliver
Cloud readiness and discovery
Three-week assessment covering current estate, workload portfolio, security and compliance requirements, FinOps baseline, and the AI and data ambitions that GCP is being chosen to support. Output is a target-state architecture, migration wave plan, investment profile, and credible business case.
Landing zone and foundation
Build the GCP landing zone — organisation hierarchy, folder and project structure, IAM model, network design, identity federation, Cloud KMS, VPC Service Controls perimeters, logging and monitoring, billing alerts, and the platform engineering stack your delivery teams will use. Foundation done right means every later workload moves faster.
Migration and modernisation waves
Workloads moved in waves of two to four weeks each, with parallel-run and automated cutover. Re-platform where it pays back in months, re-architect for the workloads that need to be cloud-native, lift-and-shift where re-engineering does not earn its keep. Every wave includes documented operational readiness.
AI and data platform build
BigQuery lakehouse with semantic layer, Vertex AI development and serving stack, first AI use cases delivered in parallel with platform build so business value accrues during the engagement, not after it. Typically eight to sixteen weeks depending on data complexity.
Operate and optimise
Twenty-four-seven managed operations under SLA with continuous FinOps optimisation, security hardening cycles, and quarterly business reviews. Most clients save fifteen to thirty percent on initial infrastructure spend within the first six months of managed operations through tuning that the rush of migration did not allow.
Google Cloud & Workspace across every sector
The stack we build on
Compute and serverless
Data platform
AI and ML
Security and governance
"MindMap migrated our entire analytics estate to GCP in twelve weeks, hardened it to our regulator's standard, and cut our run-rate infrastructure cost by thirty-five percent. The Vertex AI platform they built on top now serves three production models inside the bank."— Head of Technology, Regional Insurance Holding
How we work together
Common questions
What is your relationship with Google Cloud?+
We are a Google Cloud Premier Partner with certifications across Professional Cloud Architect, Professional Data Engineer, Professional ML Engineer, Professional Cloud Security Engineer, and Google Workspace. Our delivery teams hold a deep bench of these certifications and we run regular re-certification cycles. We have a dedicated Google Partner Sales contact for joint pursuits and access to Google specialist engineers for the hard problems.
Can you migrate from AWS or Azure to GCP?+
Yes, and we do this regularly. Most enterprise migrations are not greenfield — they are multi-cloud rationalisation. We handle the data migration including Snowflake-to-BigQuery and Redshift-to-BigQuery patterns, the application re-platforming including ECS-to-GKE and Lambda-to-Cloud-Run, network and identity reconfiguration, and the cost modelling that justifies the move. We are honest when the move does not justify itself — sometimes the answer is to optimise where you are.
Do you offer Workspace deployment for regulated industries?+
Yes. We specialise in Workspace for financial services, healthcare, and government — including Context-Aware Access design, advanced DLP rule sets, Vault retention policies, Meet recording governance, Drive sharing controls at scale, and eDiscovery readiness. We migrate from Microsoft 365 and on-prem Exchange with mail and calendar integrity guaranteed. Most enterprise migrations take twelve to twenty weeks depending on user count and complexity.
How do you handle cost optimisation on GCP?+
FinOps is built into our managed operations from day one. We instrument cost by project, label, and workload; set anomaly alerts; review committed-use discounts and reservation strategy monthly; right-size compute and storage quarterly; and run an annual deep-cut review. Most clients save fifteen to thirty percent on their initial post-migration run-rate within six months of managed operations starting.
What about VPC Service Controls and the security model?+
VPC Service Controls is non-optional for any GCP deployment handling sensitive data — it is the perimeter that prevents data exfiltration even by authenticated identities. We design the perimeter set as part of landing zone, configure access levels and ingress and egress rules, and integrate with your identity provider for context-aware access. We have built this for clients including some who passed central-bank security reviews on first attempt.
Can you build sovereign AI on GCP?+
Yes — both with Vertex AI inside your GCP tenant under Assured Workloads, and with Vertex hosting open-source models from the Model Garden that meet your data-residency and tenant-isolation requirements. For deployments where 'sovereign' means 'no cloud at all' we deploy outside GCP on your hardware. The right answer depends on what 'sovereign' means in your regulatory context — we have done both.
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