CLOUD & SCALABLE SYSTEMS
We move you to the cloud properly and design systems that absorb growth — so a good month for the business isn't a bad week for your platform, and the bill still makes sense afterwards.
THE MODEL
Scalable doesn't mean big. It means the cost and effort of serving one more customer stays roughly flat — and that growth shows up as a slightly larger bill rather than an outage. Most systems that struggle aren't short of servers; they have a bottleneck nobody has measured.
WHAT WE DO
Most engagements draw on two or three of these. We start with whichever is limiting you now.
Moving workloads to Azure or AWS with a tested cutover plan — rehost, replatform or rebuild, chosen per workload rather than by dogma.
Designing for the load you expect: compute, storage, caching, queues and data layer chosen for the actual access pattern.
Right-sizing, reserved capacity, storage tiering and turning off what nobody uses. Often the fastest measurable win.
Backup, replication and failover with recovery objectives agreed with the business and then genuinely tested.
CAPABILITIES
Azure and AWS foundations with subscription structure, networking, identity and guardrails set up correctly from day one.
Virtual networks, subnets, private endpoints, VPN and hybrid connectivity that stay comprehensible as they grow.
Entra ID, role-based access control, policy and tagging standards applied consistently.
Everything defined in Terraform and version-controlled, so no resource exists only because someone clicked.
Workload discovery, dependency mapping and a migration plan sequenced to limit business risk.
Fast, low-risk rehosting when the priority is leaving a data centre or ending a hosting contract.
Moving to managed databases, containers and serverless where it genuinely lowers cost and effort.
Moving databases and file estates with validation, rollback and minimal downtime.
Horizontal and vertical scaling tied to meaningful metrics, with sensible ceilings and cost guardrails.
Redis, CDN and message queues introduced where they remove real pressure from the data layer.
Read replicas, connection pooling, indexing and partitioning based on measured query behaviour.
Realistic load tests that find the actual bottleneck before you pay for capacity you don't need.
Automated backup with restores tested on a schedule, across both cloud and remaining on-premises workloads.
Multi-zone and multi-region designs sized to the downtime the business will actually tolerate.
A documented, rehearsed plan with agreed recovery point and recovery time objectives.
Cost visibility, forecasting, anomaly alerts and rightsizing reviews that keep spend honest.
ENGAGEMENT
Whether you need a plan, a migration delivered, or someone to keep it healthy afterwards.
PROCESS
We find the real constraint — profiling the application, the data layer and the traffic pattern instead of assuming it's servers.
A target architecture with the trade-offs written down, sized for your next 18 months and costed before anyone builds.
Delivered in stages behind infrastructure as code, with a tested rollback at every cutover.
Once live, we tune capacity and cost against real usage, then hand over the runbooks and dashboards.
QUESTIONS
A single application typically moves in two to six weeks. A full estate of twenty or more workloads usually runs three to six months, sequenced so the lowest-risk workloads go first. We give you a costed plan with dates before committing to anything.
Not automatically — a badly executed lift-and-shift often costs more than the hardware it replaced. Savings come from right-sizing, reserved capacity, storage tiering and switching to managed services. We model the expected running cost before migrating so there are no surprises.
Both are capable. Azure usually wins where you are already invested in Microsoft 365 and Entra ID, because identity and licensing carry across. AWS often suits teams with an existing preference or a specific service dependency. We'll recommend based on your estate, not our habits.
It means serving one more customer costs roughly the same as the last one, and demand spikes show up as a slightly higher bill rather than downtime. In practice that's autoscaling, caching, queueing, and a data layer designed for how it's actually queried.
Yes. Plenty of organisations keep some workloads on-premises for latency, licensing or regulatory reasons. We design the hybrid connectivity and identity so both halves are managed the same way rather than becoming two separate problems.
Staged cutovers, data replication ahead of the switch, a tested rollback for every step, and moving out of hours where it matters. We rehearse the cutover on a copy before doing it for real, and most migrations complete with no user-visible interruption.
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