Services Cloud and Infrastructure
Migrate Your Applications and Data to the Cloud Without Disrupting Operations
icitech supports enterprise organisations moving applications and workloads to AWS, Azure, or Google Cloud. The migration process is built around dependency analysis, wave planning, and a controlled cutover — because moving infrastructure is only part of the work. When application, data, and operating model are addressed together, the post-migration environment behaves predictably from day one.
01 Context
Conditions That Make Migration a Serious Option
On-premise infrastructure approaching end of support, unpredictable capacity demand, or the need for flexible compute to support a new AI or data platform — each of these points makes cloud migration a considered decision rather than a speculative one. The same applies when environment provisioning has become a bottleneck, when operational time spent on physical infrastructure maintenance is growing, or when ad hoc cloud usage has reached the point where it needs to be brought into a governed target architecture. icitech's migration service is designed for exactly this position: planning and executing the transition with business continuity and application integrity at the centre.
02 Scope
Where the Real Risk Lies
Most of the difficulty in a migration project comes not from infrastructure technique but from dependencies that were not visible at the outset. An application may have undocumented connections to another database, a shared file system, or an internal service; those connections become unexpected outages after cutover. Data transfers carried out without regard for volume and downtime tolerance produce inconsistencies. Applying the same design to every workload leads to cost or performance outcomes that do not match expectations. icitech's approach is to make these risks visible before the migration begins and to build the migration plan on an accurate dependency map rather than an assumed one.
03 Context
How icitech Runs a Migration
Every migration engagement begins with a discovery phase: application inventory, hosting information, data stores, external and internal dependencies, criticality, ownership, and lifecycle status are brought together into a single picture. Each workload is then assessed individually across cloud readiness, security, networking, data, licensing, and operational preparedness. This assessment determines whether a given workload should be migrated as-is, restructured, or retained in its current environment — and on what grounds.
Target Architecture and Pilot Wave
Once the readiness assessment is complete, the target architecture is designed for the chosen platform — AWS, Azure, or Google Cloud — covering networking, identity, compute, storage, backup, and monitoring. Before the broader migration proceeds, a pilot wave moves a real workload that carries genuine dependencies but carries lower or medium risk. This step validates the migration pattern and the network and identity configuration against actual conditions rather than assumptions.
Wave Planning and Cutover
Applications are grouped into waves according to their dependencies and the business calendar. Each wave has its own data transfer plan, cutover runbook, and defined rollback criteria. Security is not treated as a concern limited to the target environment: the migration tooling, temporary access arrangements, and data replication processes are all brought within the same security framework. Monitoring is activated before cutover, data consistency is verified, and the production transition only proceeds once those checks are complete.
Stabilisation and Handover to Operations
Completing the migration does not close the engagement. Functional, integration, performance, and operational checks are carried out; monitoring ownership and support responsibilities are clarified. Once the environment is stable, a backlog of performance, cost, and operational improvements is established for the next phase. This lifecycle is adapted to the nature of each workload — the sequence is not fixed across every project.
04 Context
Platform Selection: AWS, Azure, and Google Cloud
icitech provides architecture design and migration services across AWS, Microsoft Azure, and Google Cloud. Platform selection is determined by existing enterprise agreements, team capability, application and data service requirements, regional availability, compliance framework, and cost model. Managed service adoption can reduce operational overhead, but portability and dependency balance are assessed separately for each workload. icitech's technology-independent approach to software engineering means that platform decisions are made in context rather than on the basis of a single-platform preference.
05 Scope
Why Migration Should Be Considered Alongside Software Engineering
When cloud migration is treated purely as an infrastructure exercise, problems at the application layer travel with the workload into the new environment. Because icitech works across cloud infrastructure, software engineering, and data engineering within the same technical context, how the application will behave after migration is part of the target architecture from the start. This requires a view of migration that extends to application dependencies, API integrations, and user-facing product layers. icitech's more than twenty years of enterprise technology experience is the foundation of that integrated approach.
06 FAQ
Frequently Asked Questions
A current application inventory and an initial view of known dependencies are the most useful starting point. That foundation allows the discovery phase to move quickly and surface the risks that matter most.
Workloads are migrated in planned waves rather than all at once, and each wave carries defined rollback criteria. Critical processes continue to run in the existing environment until the cutover has been validated.
Existing enterprise licensing agreements, technical requirements, regional availability, and long-term cost projections all bear on the decision. There is no single right answer — the assessment is done workload by workload.
Databases carry high dependency and require careful synchronisation to preserve data consistency during transfer. Poor planning at this stage is the most common cause of data loss or extended downtime in migration projects.
Monitoring is in place before cutover and remains active through the stabilisation period, allowing anomalies to be identified and addressed early. The engagement is not closed until the environment is stable and operational ownership has been handed over.
It is technically possible but carries substantial risk and is not recommended as a general approach. Wave planning groups applications by priority and dependency so that risk is distributed and each wave can be validated before the next begins.
Not necessarily — some workloads can be migrated with little or no change, while others may benefit from modernisation. That determination is made during the pre-migration architecture assessment, so there are no surprises at the point of cutover.
The pilot wave moves a small number of workloads — real ones, with genuine dependencies — ahead of the main programme to validate the migration pattern under live conditions. The findings directly improve planning for subsequent waves.
Your team brings the knowledge of existing systems and business processes that external specialists cannot replicate, so they are active participants at every key decision point. During handover, the necessary knowledge transfer is carried out so that your team can own and operate the new environment confidently.
Resource usage should be reviewed on a regular basis and underused or over-provisioned resources resized or retired accordingly. Cost governance is built into the post-migration operating model from the outset, not added as an afterthought.