Cloud Migration Excellence

AI automation compresses discovery, planning, and cutover cycles—typically enabling up to 40% faster timelines and ~35% cost reduction through right-sizing, automated testing, and policy-driven orchestration. Beyond speed and savings, AI baselines KPIs (cost per workload, performance, risk) and continuously optimizes them post-migration for durable value—versus a one-and-done project.

Our “zero-downtime by design” framework uses progressive cutovers, automated rollback, and real-time dependency checks. We establish clear go/no-go criteria, simulate failure modes, and keep mission-critical services hot while migrating non-critical tiers—so transformation happens without interrupting revenue operations.

We design cloud-agnostic guardrails (Terraform/IaC, Kubernetes, Istio, GitOps) with provider-native optimizations where they matter (e.g., Azure SQL vs. Cloud SQL), separated by abstraction layers. The result: portability where you need it, and platform benefits where they pay back—without a brittle, least-common-denominator architecture.

All three.

• Assessment: automated discovery, dependency mapping, and TCO/RoI modeling.
• Execution: policy-driven orchestration, test automation, drift detection, and safe cutovers.
• Run/Optimize: continuous rightsizing, autoscaling, anomaly detection, and FinOps guardrails that keep costs flat as usage grows.

Yes, Korcomptenz offers post-migration support powered by AI, with a focus on ongoing optimization, performance tuning, cost savings, and keeping the cloud environment secure, scalable, and in conformity with changing business requirements.

We segment by business criticality, elasticity, and modernization payoff. Commodity systems often migrate via lift-and-shift to capture quick wins; revenue or latency-sensitive services skew to re-platforming; high-change, high-value domains (e.g., pricing, personalization) justify re-architecture to cloud-native. The roadmap balances time-to-value vs. tech-debt reduction by quarter.