We design cloud architectures and modernization paths on Azure, AWS and Google Cloud that make applications, data and integration faster to change, safer to run and cheaper to scale.
Lifting servers into the cloud without changing the architecture moves the cost and keeps the constraints.
Applications are migrated as they are. Bills go up, performance stays the same and nobody can explain the invoice.
Accounts, networks and identities grow organically. Security and compliance are checked after the fact.
Core systems on old middleware, shared databases and batch jobs block modernization one dependency at a time.
Teams that ran data centers are asked to operate Kubernetes, serverless and infrastructure as code without a transition plan.
The cloud pays off when the architecture uses it: elasticity, managed services, automation and observability.
Environments, pipelines and infrastructure as code let teams ship changes in hours instead of change windows.
Landing zones enforce identity, network segmentation, encryption and logging on every workload.
Right-sizing, reserved capacity, serverless where it fits and FinOps routines make spend proportional to value.
The same platform hosts the data lakehouse, integration layer and AI services with shared governance.
Strategy, architecture and hands-on engineering on the three major clouds.
Portfolio analysis, dependency mapping, target operating model and a business case that includes migration, run and modernization costs.
Well-Architected designs for Azure, AWS and Google Cloud: identity, networking, security baselines, policy as code and multi-account or subscription structure.
Wave planning and execution for applications, databases and integration middleware, with rehost, replatform and refactor decisions per workload.
Containerization, microservices where they add value, serverless, managed databases and API enablement of legacy systems.
Architectures that keep sensitive workloads on premise or across providers, with consistent identity, networking and observability.
FinOps practices, tagging, budgets, rightsizing and architecture changes that reduce the bill without reducing capacity.
Assess the portfolio, build the foundation, migrate in waves and modernize what matters.
We map applications, data, integrations and dependencies, and classify each workload by value, risk and modernization potential.
We build or fix the landing zone: identity, network, security, policies, pipelines and cost management.
We move workloads in waves, validating performance, security and cost at each step, with rollback plans.
We refactor the workloads that justify it, automate operations and hand over with runbooks and enablement.
A landing zone with shared services, and workloads that consume them through standards rather than exceptions.
The same structure applies to Azure, AWS and Google Cloud, with native services for each layer.
Native services first, open standards where portability matters.
Where cloud and modernization engagements usually begin.
Planned migration of an application portfolio with a landing zone, waves and a cost model.
Moving integration platforms such as IBM, Oracle, MuleSoft or Boomi to cloud-hosted or managed equivalents without breaking interfaces.
Packaging Java, .NET or Node.js applications for Kubernetes with pipelines and observability.
Replacing batch jobs and schedulers with functions and managed queues.
Keeping regulated data on premise while running applications and analytics in the cloud.
Reducing a growing cloud bill through rightsizing, reservations and architecture changes.
Each is a solid enterprise platform. The decision usually follows existing contracts, the Microsoft or Google workplace footprint, data platform choices and team skills. We are independent and have delivered on all three; when in doubt we recommend against spreading thinly across providers.
Neither for everything. We classify workloads: some are best rehosted quickly, some replatformed to managed services, and a smaller set refactored. The assessment produces this decision per application, with cost and risk for each option.
By designing for it: rightsizing, managed and serverless services where they fit, reserved capacity for stable workloads, tagging and budgets from day one, and a FinOps routine that reviews spend with owners every month.
Yes. Hybrid architectures with private connectivity, consistent identity and shared observability are common, especially for regulated data or systems that are not economical to move.
Integration is often the critical path of a migration. We plan it explicitly, whether that means running the existing platform in the cloud, moving to a managed integration service or modernizing to APIs and events.
Middleware and APIs are usually the critical path of a migration.
Host the lakehouse on a governed landing zone.
Sequence the cloud program inside a technology roadmap.
Moving to the cloud is the easy part. Making legacy systems cheaper to run and faster to change is the goal. A strategy built from assessments and migrations in real enterprise environments.
The debate is usually framed as old versus new. In practice, enterprises that integrate well take the principles of SOA, the autonomy of microservices and the governance of API management, and evolve toward them one interface at a time.
The difference between a successful pilot and a production AI capability is rarely the model. It is the architecture around it. Here is what that architecture contains and which decisions matter.
A cloud architecture assessment gives you a workload-by-workload plan with costs, risks and the order of execution.