Cloud choice is a workload decision, not a corporate one. We run production estates on AWS, Azure and GCP, and most of our enterprise clients end up multi-cloud by accident — a core banking platform on Azure because the vendor certifies it there, analytics on GCP because BigQuery is the shortest path, edge and consumer workloads on AWS because CloudFront and Lambda@Edge do the job cleanly. Our practice is built around that reality rather than a preferred-vendor pitch.
What we do across the three clouds
On AWS the estates we run are typically built around EKS or ECS Fargate for compute, RDS Aurora and DynamoDB for data, MSK or Kinesis for streaming, and a landing-zone pattern using Control Tower, Organizations, SCPs and AWS Config. For BFSI clients we pair this with GuardDuty, Security Hub, Macie for PII discovery, and KMS with customer-managed keys tied to CloudHSM where regulators require it.
On Azure the shape is AKS or App Service, Azure SQL and Cosmos DB, Event Hubs and Service Bus, with governance through Management Groups, Azure Policy, Defender for Cloud and Sentinel for SIEM. Insurance and banking clients running Microsoft Dynamics 365, Finacle add-ons or Guidewire on Azure get an environment where Entra ID, Purview and Key Vault are wired into the platform, not bolted on.
On GCP we lean on GKE Autopilot, Cloud Run, Cloud SQL and Spanner where global consistency matters, BigQuery and Dataflow for analytics, and Anthos Config Management or Policy Controller for guardrails. GCP tends to show up when the analytics or ML workload is the reason for the migration, and the rest of the estate gets pulled along.
Migration and modernisation
Most engagements start with an existing estate — on-prem VMware, colocated hardware, or a first-generation cloud footprint that has drifted. We run a discovery using the native tooling (AWS Application Discovery Service, Azure Migrate, GCP Migration Center) plus dependency mapping, then produce a workload-level disposition: rehost, replatform, refactor, repurchase or retire. Each disposition is tied to a business driver — licence exit, hardware refresh, regulatory data-residency, latency, or a specific product roadmap.
Replatforming typically means containerising the tier that benefits (usually the API and worker tiers), moving databases to managed services where the engine is supported, and leaving legacy monoliths in IaaS with a clear exit path. Refactoring is scoped tightly to the pieces that pay back — an authentication service, a payments router, an underwriting engine — rather than a full rewrite dressed up as a migration.
For regulated clients we align the migration plan with RBI cloud guidelines, IRDAI information and cyber security guidelines, CBK guidance in Kenya, IRA in Uganda, and equivalents in the other African markets we work in. Data localisation, exit strategy, right-to-audit and BCP evidence are drafted alongside the technical plan, not after.
Infrastructure as code, platform engineering and DevOps
Everything we ship is defined in code. Terraform is the default for multi-cloud clients, with modules structured around landing zones, workload accounts and shared services. Where a client is single-cloud and already invested, we work in CloudFormation/CDK, Bicep/ARM or Deployment Manager/Config Controller. Application delivery is GitOps — Argo CD or Flux on Kubernetes, with Helm and Kustomize for packaging, and pipelines in GitHub Actions, Azure DevOps or GitLab depending on the client's existing tooling.
The platform layer that sits above raw cloud is where most of the operational leverage lives. We build internal developer platforms — a paved road for service teams — covering secrets (Vault, Secrets Manager, Key Vault), policy as code (OPA, Sentinel, Azure Policy, Kyverno), progressive delivery, and a shared observability stack (Prometheus, Grafana, Loki, Tempo, or the native equivalents — CloudWatch, Azure Monitor, Cloud Operations Suite).
Site reliability work — SLOs, error budgets, runbooks, chaos drills, incident retros — is part of the delivery, not a separate service. We help clients define what "production" actually means for each service class, and wire the alerting to match.
FinOps and governance
Cloud bills go wrong the same way every time — untagged resources, oversized non-prod, forgotten data-transfer, storage classes never revisited. We set up FinOps as a discipline: tagging policies enforced at provisioning, showback and chargeback dashboards tied to business units, savings plans and committed-use discounts modelled against real utilisation, and a quarterly rightsizing pass. For clients spending seriously on any one cloud, this usually pays for the engagement several times over.
Governance sits alongside cost — a shared responsibility matrix, an audit trail that satisfies internal audit and the regulator, and a control library mapped to ISO 27001, SOC 2, PCI-DSS and the local financial-services rulebooks. Redian is CMMI Level 3 Appraised and ISO 27001 certified, and the controls we ask clients to run are the ones we run ourselves.
How we engage
Most engagements start with a focused assessment — the current estate, the target architecture per workload, the migration or modernisation plan, and the operating model. From there we either run the build with our own engineers, augment the client's platform team through IT staff augmentation, or hand over a codified platform and stay on for managed run.
We often plug into a wider Redian programme — a core banking rollout for our BFSI practice, a CRM & ERP implementation, or an AI/ML build where the training and serving infrastructure is the deciding factor. See /case-studies for representative work, or /contact to talk through a specific estate.
