Five layers. Thirty-five services. One fixed-price engagement.

Move applications from unsupported frameworks to current, cloud-ready versions such as .NET 8.

Shift on-premise applications to managed platform services that reduce infrastructure ownership.

Use static analysis and engineering review to expose maintainability, reliability, and risk issues.

Reduce duplicate code, simplify brittle assets, and improve the internal structure of high-value systems.

Identify hot paths and performance bottlenecks that slow dashboards, APIs, and transaction flows.

Rebuild selected components for cloud-native patterns across Azure, AWS, serverless, and API-first workloads.

Define the mobile path for modernized platforms, including React Native and cross-platform delivery choices.

Embed tools such as Copilot, Claude, and Cursor into governed engineering workflows.

Modernize legacy database platforms such as SQL Server into supported, scalable versions.

Move databases to managed services such as Azure SQL and cloud-native storage platforms.

Normalize, de-duplicate, and unify business content so applications and AI systems can trust it.

Improve indexes, constraints, data consistency, and operational query behavior.

Analyze real data shape, volume, duplication, and anomalies before migration or redesign.

Move the right workloads from relational-only models to scalable stores such as Azure Cosmos DB.

Use AI agents to analyze, compare, and recommend migration paths across multiple data versions.

Build machine-learning data foundations for forecasting, prediction, and business decision support.

Combine static analysis, dynamic testing, and human validation to reduce exploitable application risk.

Move secrets and credentials into managed vaults such as Azure Key Vault.

Protect sensitive data through hashing, encryption, access control, and stronger storage practices.

Assess and harden mobile applications across authentication, API usage, storage, and release risk.

Apply AI-assisted analysis to detect, prioritize, and respond to modern threat patterns.

Centralize security telemetry so incidents can be detected, correlated, and investigated quickly.

Modernize identity, access policies, and role governance through platforms such as Azure AD.

Prepare systems and evidence for standards such as SOC 2 and GDPR.

Define the target cloud architecture, AI adoption model, migration sequence, and cost strategy.

Implement CI/CD pipelines, release discipline, and engineering practices through platforms such as Azure DevOps.

Replace manual environment management with repeatable, version-controlled infrastructure automation.

Instrument systems with Azure monitoring services and operational telemetry for production visibility.

Define manual test coverage where exploratory validation, business workflows, or human judgment still matter.

Build automated regression coverage that reduces release risk and shortens validation cycles.

Prove system behavior under load before peak traffic, batch windows, or enterprise rollouts.

Create the experimentation process needed to compare product decisions with production evidence.

Set up process, ceremonies, boards, and delivery tooling for predictable sprint execution.

Support portfolio and program-level delivery practices for larger enterprise modernization programs.

Use AI agents and automation to remove manual form submission, approvals, and operational handoffs.