Cloud migration and landing zones on AWS, Azure and Google Cloud, plus the data platform work — warehouses, pipelines and governed reporting — that makes the numbers trustworthy.
Cloud migrations go wrong in two predictable ways. Workloads get lifted before there is a landing zone, so identity, network and cost governance are retrofitted under pressure. Or everything gets refactored at once, and the programme runs out of patience before it runs out of scope.
The same pattern applies to data. A warehouse without a semantic layer produces four versions of revenue and a standing argument about which is right. We build the foundations — governance, structure, definitions — before the migration wave or the first dashboard.
Portfolio assessment through wave execution, sequenced so that the risky migrations happen when the team has practice.
The account structure, network topology and identity model everything else inherits — built once, properly.
Making the path to production fast enough that engineers stop working around it.
Pipelines built to be observable and recoverable, because the failure mode that matters is silent bad data.
Reporting built on governed definitions, so two dashboards showing the same metric agree.
Cloud spend made visible and attributable, before the finance team asks a question nobody can answer.
EC2, ECS/EKS, Lambda, RDS, S3, Control Tower
AKS, App Service, Functions, Synapse, Entra ID
GKE, Cloud Run, BigQuery, Dataflow
Cloud data warehouse and data sharing
Lakehouse, Delta Lake and ML workloads
Infrastructure as code across all clouds
Transformation, testing and documentation
Enterprise business intelligence
Usually both, in sequence. Refactoring everything up front stalls programmes; lifting everything leaves you paying cloud prices for datacentre architecture. Portfolio assessment gives each application a disposition, and we typically migrate first, then modernise the workloads where the return justifies it.
Tagging and cost allocation from the first account, budget alerts with real owners, rightsizing reviews on a cadence, and commitment planning once usage patterns are established. The expensive version is discovering the problem three quarters in, when the architecture is already committed.
Yes, and it is often the better arrangement. We can lead the programme, augment your team with specific skills, or act as an architecture review function. Whichever it is, everything is infrastructure as code, so nothing is trapped in someone's console session.
Because without one, every report defines its own version of a metric, and within a year nobody trusts any of them. Governed definitions in a semantic layer mean revenue is revenue regardless of which dashboard you open, which is the difference between reporting and arguing.
A short conversation is usually enough to tell whether we're the right fit. If we're not, we'll say so and point you somewhere better.
Or email us directly at reachus@segakiv.com
