What cloud platforms do you work with?

We work with AWS, Microsoft Azure, and Google Cloud Platform. For federal government clients, we prioritise platforms with Canadian data centre regions and existing Government of Canada authorisations through the Cloud Service Provider assessment process.

How do you handle Canadian data residency requirements?

We architect cloud environments to keep data within Canada by default, selecting services and regions that satisfy both organisational policy and applicable legislation including PIPEDA and provincial health privacy laws. Data residency requirements are documented explicitly in the architecture specification.

Can cloud AI infrastructure meet Protected B requirements for government?

Yes. We design to the CCCS Medium Cloud Profile, which supports Protected B workloads. We work with your security team and, where required, coordinate with the Canadian Centre for Cyber Security on architecture review.

How do you manage the transition without disrupting existing operations?

We use a phased migration approach with parallel-run periods. Existing workloads remain operational until the cloud environment is validated. Rollback plans are defined before any migration step is executed.

What ongoing operational support is included?

We document infrastructure-as-code for every environment we build and train your operations team on day-to-day management. Post-migration support is available under a separate Evolve phase engagement for ongoing optimisation and capacity planning.

What cloud platforms do you work with?

We work with AWS, Microsoft Azure, and Google Cloud Platform. For federal government clients, we prioritise platforms with Canadian data centre regions and existing Government of Canada authorisations through the Cloud Service Provider assessment process.

How do you handle Canadian data residency requirements?

We architect cloud environments to keep data within Canada by default, selecting services and regions that satisfy both organisational policy and applicable legislation including PIPEDA and provincial health privacy laws. Data residency requirements are documented explicitly in the architecture specification.

Can cloud AI infrastructure meet Protected B requirements for government?

Yes. We design to the CCCS Medium Cloud Profile, which supports Protected B workloads. We work with your security team and, where required, coordinate with the Canadian Centre for Cyber Security on architecture review.

How do you manage the transition without disrupting existing operations?

We use a phased migration approach with parallel-run periods. Existing workloads remain operational until the cloud environment is validated. Rollback plans are defined before any migration step is executed.

What ongoing operational support is included?

We document infrastructure-as-code for every environment we build and train your operations team on day-to-day management. Post-migration support is available under a separate Evolve phase engagement for ongoing optimisation and capacity planning.

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Cloud AI Infrastructure & Migration

Structured migration of AI workloads to cloud environments with the right architecture for performance, security, and compliance — including Canadian data residency requirements.

Why Cloud Infrastructure Decisions Matter for AI

AI workloads are not like traditional application workloads. Training runs and inference at scale consume compute resources that on-premises infrastructure cannot cost-effectively provide. Equally, moving sensitive data to cloud environments without proper architecture creates compliance exposure that is unacceptable in regulated sectors.

The gap between "lift and shift" cloud migrations and properly architected AI infrastructure is significant — in performance, in cost, and in the compliance posture of the result.

What We Design and Build

Cloud Architecture for AI Workloads. We design cloud environments specifically for the compute, storage, and networking requirements of AI systems — not generic enterprise workloads. This includes GPU resource provisioning for training, optimised inference serving configurations, and data pipeline architecture that feeds models efficiently.

Canadian Data Residency Controls. For every client, we document which data assets are subject to residency requirements and architect the cloud environment accordingly. This includes selecting appropriate cloud regions, restricting cross-border data replication, and configuring data loss prevention controls that enforce residency at the infrastructure level.

Security Architecture. We implement security controls aligned with the Canadian Centre for Cyber Security's cloud security guidance. For government clients, this means designing to the CCCS Medium Cloud Profile as a baseline, with additional controls where workloads require it. For healthcare clients, we align with provincial privacy commissioner guidance and applicable health information legislation.

Cost Architecture. AI compute costs can escalate rapidly without proper governance. We implement tagging, budget alerting, auto-scaling policies, and reserved capacity planning to control costs and ensure cloud spend is visible and manageable.

The Government of Canada Cloud Context

Federal departments and Crown corporations operate within the Government of Canada Cloud Adoption Strategy and the Directive on Service and Digital. Cloud migrations must consider Protected classification levels, the GC Cloud Framework assessment process for cloud service providers, and alignment with the enterprise architecture requirements of Shared Services Canada.

We have designed cloud AI infrastructure for this environment. We understand the distinction between SaaS, PaaS, and IaaS procurement under GC frameworks, the implications of each for data sovereignty, and the documentation requirements for departmental security assessments.

Healthcare and Financial Sector Considerations

Provincial health authorities and regulated health information custodians operate under PHIPA, PIPA, and equivalent provincial legislation that imposes strict controls on where and how personal health information may be processed. We architect cloud environments that satisfy these requirements and produce the documentation that privacy officers need for their assessments.

For financial institutions, OSFI's guidance on cloud adoption (B-10) requires that federally regulated financial institutions maintain oversight and control over outsourced functions. We architect cloud environments with the audit logging, contractual controls, and operational oversight mechanisms that satisfy B-10 requirements.

Audit and Strategy Before Implementation

Cloud migrations that skip the architecture design stage produce environments that must be rebuilt. Our approach begins with a cloud readiness audit that assesses current workloads, data classifications, compliance requirements, and existing infrastructure dependencies. The strategy phase produces an architecture specification, migration sequencing plan, and cost model before any implementation work begins.

This front-loaded approach prevents the expensive course corrections that result from moving quickly into cloud environments without proper design.

How We Deliver Cloud AI Infrastructure and Migration

Cloud Readiness Audit. We begin with a structured audit of your current AI workloads and data infrastructure: what is running where, what data classifications are involved, what compliance requirements apply, and what the actual cost and performance characteristics of the current environment are. The audit produces a classification of workloads into migration categories — cloud-native candidates, lift-and-shift candidates, and workloads that should remain on-premise — and identifies the blockers that need to be resolved before migration.

Architecture Design. We design the target cloud environment specifically for AI workload requirements: compute profiles for training and inference, storage architecture for training data and model artifacts, network design for data pipeline throughput, and security controls aligned with applicable Canadian regulatory requirements. The architecture document is a formal deliverable, reviewed and approved before any implementation begins.

Staged Migration and Validation. We migrate workloads in defined waves, with validation gates between waves. No workload moves to production cloud until it has been validated in a staging environment that mirrors the production architecture. Parallel-run periods ensure that cloud-based workloads produce consistent results with on-premise equivalents before the on-premise version is decommissioned.

Operations Handoff and Ongoing Optimisation. Every environment we build is delivered as infrastructure-as-code (Terraform or Pulumi), documented in runbooks, and handed off to your operations team with training. Cloud cost governance — tagging, budget alerting, right-sizing analysis, and reserved capacity planning — is configured before handoff, not added later when the first large bill arrives.

What to Expect: Timeline and Milestones

Weeks 1–3: Cloud Readiness Audit. Workload inventory, data classification, compliance requirement mapping, cost baseline, and migration feasibility assessment. Deliverable: cloud readiness report with workload migration classification and blocker list.

Weeks 4–7: Architecture Design. Target state cloud architecture, security design, data residency controls, cost model, and migration sequencing plan. Deliverable: signed-off architecture specification and migration plan.

Weeks 8–16: Wave 1 Migration. First workload wave migrated, validated in staging, parallel-run period, and production cutover. Deliverable: first wave live in cloud with monitoring dashboards active.

Weeks 17–24: Subsequent Waves. Additional workload waves migrated per the sequencing plan. Operations team training and infrastructure-as-code handoff at end of final wave.

Complex migrations — multi-cloud environments, Protected B workloads, large data volumes, or multi-department scope — run 6–9 months. Targeted single-workload migrations on cloud-ready data can complete in 8–10 weeks.

Integration and Technology Stack

Remolda cloud AI infrastructure deployments typically involve:

Cloud Platforms: AWS (Canada Central region), Microsoft Azure (Canada Central and Canada East), or Google Cloud (Montréal region) depending on your data residency requirements and existing enterprise agreements
AI Compute: AWS SageMaker, Azure Machine Learning, or Google Vertex AI for managed ML infrastructure; Kubernetes (EKS, AKS, GKE) for custom containerized AI workloads
Data Residency Controls: AWS S3 Object Lock and SCPs, Azure Policy, or Google Cloud Organization Policies to enforce Canadian data residency at the infrastructure level
Security: CCCS-aligned security architecture using AWS Landing Zone Accelerator (Government of Canada configuration), Azure Landing Zone, or Google Cloud Secure Foundation blueprints
Infrastructure-as-Code: Terraform for multi-cloud environments; AWS CDK or Azure Bicep for single-cloud deployments; GitOps workflows for environment management
Cost Governance: AWS Cost Explorer + Budgets, Azure Cost Management, or Apptio Cloudability for multi-cloud cost visibility; auto-scaling policies and reserved instance planning
Monitoring: Amazon CloudWatch, Azure Monitor, or Google Cloud Monitoring; Datadog or Grafana for cross-platform observability

All environments are delivered with documented runbooks, alerting configurations, and disaster recovery procedures.

Canadian Context

Cloud AI infrastructure for Canadian organizations is governed by a layered set of requirements that vary by sector and data classification. For federal government clients, cloud deployments must align with the Government of Canada Cloud Adoption Strategy, the Directive on Service and Digital, and the CCCS Medium Cloud Profile for Protected B workloads. Cloud service providers used by federal institutions must hold Government of Canada authorizations under the Cloud Service Provider assessment process administered by Shared Services Canada. PIPEDA and the forthcoming Bill C-27 require that personal information stored in cloud environments be subject to contractual protections equivalent to Canadian privacy law — a requirement that must be addressed in cloud vendor contracts and data processing agreements. For financial institutions, OSFI Guideline B-10 on technology and cyber risk outsourcing requires that federally regulated financial institutions maintain ongoing oversight of cloud providers and ensure contractual rights to audit and inspect. For Ontario healthcare organizations, PHIPA restricts where personal health information may be stored and processed — cloud deployments handling health data require specific architectural controls and privacy assessments. We produce the compliance documentation — privacy impact assessments, security assessment documentation, and architecture review packages — that your legal, privacy, and security teams require.