National marketing technology provider · Full AWS migration · CI/CD pipeline implementation · Infrastructure as Code
A national marketing technology provider had built its platform on on-premises infrastructure that had served the business well in its early years. By the time Shelorve engaged, that infrastructure had become the principal constraint on the business's ability to grow. The platform could not scale to absorb peak traffic volumes — marketing campaign launches, which drove predictable but intense demand spikes, regularly produced outages and degraded performance at precisely the moments that mattered most to clients.
The operational cost of maintaining on-premises infrastructure had become disproportionate to its capability. Hardware refresh cycles, energy costs, and the engineering time consumed by manual maintenance represented significant fixed overhead that grew with the age of the estate rather than the growth of the business. Each hardware upgrade required capital expenditure and planning cycles that the pace of the marketing technology market no longer accommodated.
The deployment process compounded the challenge. Releasing new features or security patches required manual coordination across teams, taking three to five days per release cycle. In a market where client expectations for rapid feature delivery and security response were accelerating, the gap between what the business could ship and what the market demanded was widening. The security posture — built on traditional firewall-based controls with limited visibility — was not positioned for a threat landscape that had moved significantly beyond what those controls were designed to address.
Shelorve began with a structured assessment of the existing infrastructure — mapping every critical dependency, identifying migration sequencing risks, and defining the architecture that would replace it. The output was a phased migration plan designed around a single non-negotiable constraint: zero unplanned downtime. Marketing technology clients cannot absorb outages, and the migration plan was built to ensure the platform remained available throughout every phase of the transition.
The full infrastructure migration moved to AWS, with Terraform as the Infrastructure as Code layer for all resource provisioning. Every AWS resource — compute, networking, storage, security controls — was defined in code from the start, making the infrastructure reproducible, auditable, and version-controlled. The security architecture was rebuilt from the ground up: VPCs for network isolation, AWS WAF for application-layer protection, and IAM policies enforced in alignment with AWS best-practice security baselines. Centralized patching and compliance monitoring were implemented through AWS Systems Manager and Security Hub, replacing the reactive, manual patching process with automated, continuously monitored coverage.
In parallel with the infrastructure migration, Shelorve designed and implemented a complete CI/CD pipeline that eliminated manual deployment coordination entirely. The toolchain combined Jenkins and GitHub Actions for pipeline orchestration, SonarQube for continuous code quality analysis, and Trivy for container image security scanning — security was embedded in the pipeline rather than treated as a pre-release gate. Applications were containerized and deployed through AWS EKS, providing the orchestration layer for consistent, scalable deployment across environments. AWS CloudWatch, Prometheus, and Grafana provided end-to-end observability — real-time monitoring and alerting replaced the reactive, log-based approach that had previously been the limit of operational visibility.
Infrastructure costs fell 40% from day one of steady-state cloud operations. The elimination of hardware capital expenditure, energy costs, and manual maintenance overhead — replaced by pay-as-you-go cloud consumption optimized against actual usage — produced savings that were immediate and ongoing. The platform now scales automatically to absorb demand spikes, making the outages that had defined peak campaign periods a resolved problem rather than a managed risk.
Deployments that previously took three to five days of manual coordination now complete in fifteen to twenty minutes through fully automated pipelines. Security patches that previously required planning cycles and coordination now deploy automatically through AWS Systems Manager. The CI/CD pipeline ships features faster, with higher confidence, and with security scanning embedded in every release rather than bolted on at the end. The platform achieved 99.99% uptime in the twelve months following migration completion.
The strategic position of the business changed as much as its operational metrics. The platform can now onboard new clients without infrastructure rework. New marketing campaign capabilities can be launched rapidly through the CI/CD pipeline. Compliance, governance, and disaster recovery are built into the architecture rather than managed through manual processes. The infrastructure that was a ceiling on growth is now the foundation for it.
The shift from on-premises infrastructure to cloud-native architecture changed every operational metric that mattered to the business.
| Dimension | Before — On-premises | After — AWS Cloud |
|---|---|---|
| Deployment time | 3–5 days per release, manual coordination | 15–20 minutes, fully automated pipelines |
| Infrastructure cost | High CapEx: servers, hardware, maintenance | 40% reduction — pay-as-you-go, optimized usage |
| Uptime | Variable — outages at peak demand | 99.99% — auto-scaling absorbs demand spikes |
| Security & patching | Manual patching, limited visibility, reactive | Automated patching, continuous monitoring, WAF |
| Scalability | Fixed capacity — costly and slow to expand | Auto-scalable — demand-driven, no CapEx required |
| Monitoring | Reactive, basic log review | Real-time — CloudWatch, Prometheus, Grafana |
| Time to market | Delayed by manual release coordination | Rapid feature releases through CI/CD pipeline |
"The infrastructure was the constraint on everything — on what we could ship, on how fast we could respond to clients, on how we handled campaign peaks. Shelorve removed that constraint. We now operate on a platform that grows with the business rather than limiting it."
The migration was sequenced from the dependency map produced during the assessment phase. Each component was migrated in an order that preserved all live dependencies at every stage. Where components were interdependent, parallel running was maintained — the on-premises version continued to serve traffic while the cloud version was validated. Cutover for each component happened only when the cloud version was confirmed stable under production conditions. The sequencing was the migration plan — not a schedule overlaid on top of it.
Security tooling was designed into the pipeline from the first sprint. SonarQube runs on every code commit, surfacing quality and security issues before they reach a build. Trivy scans every container image for known vulnerabilities before it is pushed to the registry. A build that fails a security scan does not proceed. This means security is enforced at the point of creation rather than as a pre-release review — and the team gets feedback in the same pipeline run, not days later.
AWS EKS auto-scaling adjusts compute capacity in response to demand automatically — no human intervention, no capacity planning cycles, no lead time for hardware procurement. When a campaign launch drives a traffic spike, the platform scales to absorb it. When demand normalizes, capacity scales back and cost follows. The platform is no longer a fixed-capacity asset that the business has to manage around — it is a variable resource that responds to the business.
The reduction comes from three sources. First, the elimination of hardware CapEx — no server refresh cycles, no data centre costs, no hardware maintenance contracts. Second, pay-as-you-go cloud consumption optimized against actual usage patterns — the platform no longer pays for capacity it does not use. Third, the reduction in engineering time previously consumed by manual maintenance, patching, and deployment coordination — that time is now available for product development.
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