We design and build cloud-native platforms on AWS for organisations whose applications can no longer scale to match growth, where deployments take days, or where a monolithic architecture has become a real bottleneck. As an AWS Premier Partner, our recommendations stay objective: not every system needs Kubernetes, and not every workload belongs in cloud.
Applications can no longer scale to match user or transaction growth.
Deploying a change takes days and carries significant operational risk.
A monolithic architecture has become a real bottleneck preventing engineering from matching business speed.
Illustrative reference architecture. The exact services, account structure, and modernisation path are tailored to your existing estate, regulatory context, and team capability. The shape stays consistent.
Business and tech together. ROI ranked.
~ 4–6 weeks
Quick wins. Real environment. Measurable.
First 90 days
Stakeholder alignment. Cross-division rollout.
Months 4–12
Hand-off when ready.
Use cases identified and prioritised on financial impact and data readiness. Stakeholders aligned across Technology, Business, Compliance, and C-Level. Output: a defensible roadmap, not a wish list.
The first system delivered as real proof , running in a production environment, not a sandbox. Designed for measurable wins inside the first 90 days.
Systematic expansion across divisions. Stakeholder alignment carries forward so the rollout reflects the real needs of the entire organisation.
Operational handover to your internal team or to ICS Managed Cloud & AI Operations , based on your capacity, not on a vendor lock-in model.
We merge service scope and anti-patterns into one operating model so the platform is useful, governable, and realistic for the team that has to run it.
Microservices and containers are decomposed only as far as the operating team can sustain, avoiding service proliferation that makes the platform harder to run than the monolith.
EKS, ECS, and serverless each earn their place through workload shape, traffic pattern, and ownership needs, not because Kubernetes became the default answer.
AWS accounts start with security, network, identity, governance, and cost baselines in place, preventing greenfield account sprawl from becoming quiet technical debt.
Blue-green releases, feature flags, and canary patterns make change reversible in minutes, removing the need for weekend windows and high-risk deployment events.
Monoliths are decomposed through strangler fig migration, with edge delivery and protection added where it improves resilience, performance, and customer-facing continuity.Stack and model choices follow your data, team capacity, governance, and long-term ownership path.
Use cases are filtered by impact, data readiness, and adoption risk. The first 90 days produce a live proof, while architecture choices stay tied to your context instead of vendor lock-in.
Container platforms, serverless workloads, landing zones, edge delivery, and modernisation waves are selected around the workload and the team. Cost optimisation starts at design time, and recommendations stay objective even where AWS is the target platform.
An e-commerce platform faced traffic spikes during major promotional events that the existing fixed-capacity infrastructure could not absorb without overprovisioning year-round.
Fixed-capacity servers sized for peak traffic. Performance was acceptable during peaks but the always-on capacity was paying a steep cost on normal days.
Transition to an auto-scaling EKS architecture that absorbs peak traffic during promotional events and rightsizes back down during normal operation.
Infrastructure cost on normal days
Zero downtime during peak periods. Infrastructure costs reduced by 40% on normal days.
A financial technology company operated a monolithic system more than seven years old. Deployment velocity was the binding constraint on the engineering team’s output.
Manual deployment cycles produced 2 releases per month, each one a multi-hour event with end-customer service implications.
Incremental decomposition over 8 months using the strangler fig pattern, with blue-green deployment and feature flags enabling gradual traffic shifting.
Deployment frequency
Deployment frequency increased from 2 times per month to 15 times per week without service disruption to end customers.
The services below define the scope of a Cloud & Platform Engineering engagement with ICS. Service selection is tailored per workload.
Service granularity sized to team capacity
Containerised microservices designed to run sustainably with the team that will operate them , not a textbook architecture.
For variable traffic and event workloads
Serverless where the workload pattern fits , not as a default. The trade-offs are deliberate.
Security · network · governance baselines
A standardised foundation that every workload sits on, with guardrails in place before applications land.
Performance · DDoS resilience
Edge protection and delivery close to your users, sized to your traffic profile.
Incremental, never big-bang
Monolith decomposition done in waves so customer-facing service stays uninterrupted through the migration.
After the platform is live, you can keep ICS engaged through Managed Cloud & AI Operations for 24/7 monitoring, incident response, and continuous FinOps , or your team can take over directly. The handover decision is based on your internal capacity, not on a vendor lock-in model.