Cloud architecture and modernisation that balance delivery speed with resilience, security, operability and cost.
Service overview
Use cloud where it creates an operational advantage
We design cloud adoption around workload characteristics and business constraints. The goal is not migration for its own sake, but an environment that is secure, observable, recoverable, cost-aware and maintainable by the teams responsible for it.
Cloud readiness and target architecture
Migration and modernisation planning
Platform engineering and automation
Resilience, observability and cost controls
HOW WE HELP
How RC approaches Cloud Computing
Explore the specialist capabilities within this service area. Each capability has its own page covering context, scope, delivery approach, controls, expected outcomes and the wider service environment around the work.
Data Security
Protect cloud-hosted data through clear ownership, encryption, identity, backup, logging and retention controls.
We map data classifications, identities, service boundaries, encryption requirements, backup and restore, logging and retention before implementation. Controls are aligned to the sensitivity and use of the data rather than applied uniformly without context. We also consider operational responsibilities so secure configuration can be maintained after deployment.
Move workloads using a migration path matched to dependency, business criticality and long-term architecture goals.
We distinguish rehost, replatform and refactor decisions, map dependencies, define migration waves and establish cutover, rollback and production-verification criteria. Modernisation opportunities are assessed against value, delivery risk and operational maturity rather than forcing every workload into the same pattern.
Integrate intelligent workflows with ERP and enterprise platforms without bypassing business rules or authorisation.
We design interfaces, data contracts, identity boundaries and controls between AI services and enterprise processes. Particular attention is given to approvals, transaction integrity, audit trails and exception handling because AI-generated suggestions should not silently become authoritative enterprise actions.
Build cloud-ready applications with deployment, observability, resilience, configuration and security designed in from the start.
We define service boundaries, APIs, deployment pipelines, secrets, configuration, health checks, scaling, logging, tracing and recovery patterns as first-class application concerns. The objective is software that can be released and operated repeatedly, not just code that runs successfully in a development environment.
Coordinate workloads across public cloud, private infrastructure and existing enterprise environments with explicit ownership.
Hybrid designs include connectivity, identity, latency, data movement, failure domains, security policy and operational support rather than treating hybrid as a network-only problem. We also consider where dependencies create hidden coupling that could limit resilience or migration flexibility.
Use managed public-cloud capability with deliberate governance, workload fit and cost visibility.
We select services against workload requirements, portability, security, operational maturity and lifecycle cost rather than adopting services because they are fashionable. Guardrails, deployment standards and observability are established so multiple teams can use cloud services consistently without losing control.
Support controlled cloud operating models for workloads that require greater isolation, locality or infrastructure control.
We assess whether regulatory, latency, legacy, data-locality or isolation requirements justify private-cloud complexity and define the platform, automation and support model required. The decision includes lifecycle cost and staffing considerations because private cloud still requires disciplined platform operations.
Sequence migrations to reduce business disruption and make cutover risk visible before production change.
We inventory dependencies, group workloads, identify data and identity requirements, define test and rollback procedures and establish acceptance criteria for each migration wave. Progress is measured by verified service behaviour after cutover, not simply by the number of workloads moved.
Modernise data stores without weakening integrity, compatibility, performance or recoverability.
We define schema and data migration, application compatibility, backfill, replication, dual-run or cutover options, rollback and validation around the application’s consistency requirements. Refactoring decisions are coordinated with application releases and operational monitoring so data change does not become an isolated migration task.
Share the business objective, current environment, known constraints and target timeline. We will use that context to identify the most relevant capability and delivery path.
