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Cloud Modernization for AI-Ready Enterprise Systems

Enterprise Cloud Migration | Application Modernization | Infrastructure Transformation

Modernization is no longer a technology decision. It is a revenue and risk decision

Most enterprise systems were not designed for continuous change. They were built for stability, control, and scale within known boundaries. That design has held – until now. Today, growth depends on how quickly systems can adapt: to new channels, new partners, and increasingly, machine-driven decisioning. Legacy environments struggle not because they are obsolete, but because they are opaque and difficult to evolve. The question is not whether to modernize. It is how to do so without disrupting the systems that still run the business. We help enterprises extract, preserve, and transform decades of legacy logic into cloud-native, AI-ready systems making it safe to evolve without disruption.
Contact Us

Five questions that define your modernization ROI

Modernization programmes succeed when the decisions that govern them are grounded in
economic reality and architectural fact. We help you address five critical sequencing and investment
decisions that define your transformation ROI:

image

Targeted Investment:

Where is change effort currently concentrated - and which legacy bottlenecks are actively throttling AI adoption, cloud integration, or product velocity?

image

Strategic Sequencing:

What must be migrated or modernized now to unlock AI and cloud-native capability, and what should remain stable to protect operational and revenue continuity?

image

Risk and Technical Debt Exposure:

Which systems carry avoidable security, compliance, or architectural risk - and where does undocumented legacy logic create enterprise vulnerability?

image

Economic Thresholds:

At what point does the cost to maintain exceed the cost to migrate - and how can transition occur incrementally without a destabilising big-bang rewrite?

image

AI and Ecosystem Readiness:

Can your core business logic be safely exposed to new channels, partner platforms, and AI agents - without disrupting the systems of record that underpin them?

How KRE works with you to achieve these

Most programmes encounter difficulty not during migration, but before it - when complexity is underestimated. We focus on reducing uncertainty upfront by mapping dependencies, extracting logic, and quantifying impact early, so execution proceeds without avoidable surprises.

Methodology-led discovery

A structured baseline of dependencies, data flows, and operational patterns surfaces hidden coupling, undocumented logic, and risk concentrations before design begins.

Staged decomposition

Each application is treated based on its characteristics – whether lift-and-shift, replatforming, refactoring, or selective rebuild – ensuring the right modernization path rather than a one-size-fits-all approach.

Integration enablement

Core systems are extended, not disrupted. API and event-driven encapsulation enable participation in modern workflows while preserving system stability.

Automated governance

CI/CD, automated testing, and embedded security ensure consistent, predictable change – supporting continuous modernization with clear control over cost, performance, and evolution.
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Why KR ELIXIR ?

Proven IP

Mirror Validation, Data Ingestion Frameworks, Data Fabric Extension, and Test Data Generator are proprietary accelerators built from large-scale migration programmes. They reduce delivery timelines by up to 50% while reducing data integrity and dependency risk during and after migration.

Delivery track record

19 years of enterprise delivery across 200+ projects, supported by 300+ certified cloud engineers across GCP, AWS, and Azure. This foundation has enabled delivery at scale, including programmes exceeding $300MM, with 98% client retention and 23+ Fortune 500 clients across regulated and industrial sectors.

Automated governance

Delivery experience across FINRA, HIPAA, SOC 2, GDPR, and data residency–restricted environments. Compliance is designed into architecture and discovery phases, not addressed after migration begins.

Our Cloud Modernization Services

Application Modernization

How do you change a system without changing what it fundamentally does?

Your legacy applications still deliver value; accumulated complexity and specialist dependency are what constrain them. We apply a proprietary reverse engineering methodology to extract,document, and convert legacy code into cloud-native architectures.

  • Dependency mapping (Matrix + Sequence Diagrams) to surface hidden logic before
    conversion
  •  Incremental modernization using Strangler Fig to avoid big-bang rewrites
  •  Four-strategy approach: Lift & Shift, Replatform, Refactor, Rebuild by workload
  •  DDS to DDL conversion and full integration replacement (FTP/SFTP, MFT, EDI, MQ, DDMF)
  •  Parallel system operation with controlled rollout and rollback planning
  •  Cloud-native APIs and modern data layer replacing legacy structures
  •  AI-assisted refactoring to accelerate conversion
  •  End-to-end functional, regression, and data integrity validation
  • IBM AS400/iSeries, RPG, SQLRPGLE, COBOL, CL/400, SQL/400
  •  Java, Spring Boot, REST APIs
  •  GCP DataProc, BigQuery, AlloyDB
  •  Test Data Generator and Mirror Validation IP accelerators
  •  GitHub Copilot, Cursor, Amazon CodeWhisperer for AI-assisted refactoring
  •  Jenkins CI/CD, automated QA frameworks
  • Modernization cycle times reduced by up to 30-40% through structured reverse engineering
    and AI-assisted tooling replacing manual analysis and rewrite
  •  5-10× improvement in development velocity on modernized codebases versus legacy
    AS400/COBOL environments
  •  Up to 99.99% data match between legacy and target environments validated through Mirror Validation
  •  Up to 40% reduction in ongoing maintenance cost through reduced specialist dependency
    and broader engineering supportability
  •  Zero customer incidents across modernization go-lives enabled by pre-release validation
    across 3,000+ use cases
Assess your modernization readiness →

Legacy to Cloud Migration

How do we move our most critical workloads to cloud without exposing the business to migration risk?
Legacy estates are becoming cost-intensive, harder to sustain, and increasingly disconnected from AI and cloud-native ecosystems. We enable controlled, large-scale migration through a structured methodology and proven accelerators -ensuring predictable execution, regulatory alignment, and zero-disruption outcomes.
What We Deliver
  • 9-stage methodology: Discovery through to Transition & Operate
  •  Per-application assessment with Lift/Shift, Replatform, Refactor, Rebuild options and full TCO modelling
  •  Hot and cold migration strategies with defined rollback plans before production cutover
  •  Phased wave migration with validated non-production runs before production moves
  •  Built-in compliance for FINRA, HIPAA, SOC 2, and data residency, designed pre-migration
  •  Hyperscaler funding support across Google PSF, AWS MAP, and Azure MMP programs
  • GCP (Composer, Dataflow, Spanner, BigQuery, Firestore, PubSub)
  •  AWS (EC2, EKS, RDS, Redshift), Azure (VM, AKS, SQL Managed Instance)
  •  Mirror Validation, Data Ingestion Frameworks, Test Data Generator, Data Fabric Extension IP accelerators
  •  Terraform, Ansible, ArgoCD, Jenkins, GitLab CI
  •  SSH/WMI/SNMP discovery tooling, CMDB population, dependency mapping frameworks
  • Zero customer incidents across all major production migration launches – validated across programmes of up to $300MM+ in mainframe-to-GCP scope
  •  Estimated 50% reduction in migration timelines using KRE proprietary IP accelerators
  •  Sub-second API response times achievable post-migration on cloud-native target
    architectures – not possible on legacy mainframe platforms
  •  Data quality certification up to an estimated 99.99% match between legacy and cloud
    environments – validated through automated Mirror Validation across all migrated data
    domains
  • Up to 30-40% infrastructure cost reduction post-migration through FinOps optimization –
    delivering ongoing savings that fund further innovation investment
Request a free discovery assessment →

Data Center Migration

How do we decommission aging infrastructure and move to cloud – without a single incident affecting the business?

On-premises infrastructure is no longer economically or operationally viable at scale. We deliver end-to-end data center exit – combining dependency-led migration, parallel decommissioning, and cloud optimization to ensure a seamless, incident-free transition.

What We Deliver
  • End-to-end migration methodology from discovery to phased execution and post-migration optimization
  • Parallel physical decommissioning aligned with cloud migration, including hardware, vendor, and compliance exit
  • Hybrid and edge architecture design for sovereign, low-latency, and regulated workloads
  • AI/ML-ready landing zones embedded from day one of migration
  • Multi-cloud architecture across GCP, AWS, and Azure to reduce lock-in and optimize workload placement
  • Post-migration FinOps covering right-sizing, reservations, spot optimization, and cost control
  • Cloud-agnostic delivery across major hyperscalers and target environments
  • GCP, AWS, Azure – cross-trained engineering teams across all three hyperscalers
  •  SSH/WMI/SNMP discovery tooling, automated dependency mapping, CMDB population
  • Terraform IaC, Jenkins CI/CD, GCP/AWS/Azure native services
  • NVIDIA A100/H100 GPU infrastructure, Kubernetes (GKE/EKS/AKS)
  •  Vertex AI, SageMaker, Azure ML for AI/ML-ready landing zones
  • CloudHealth, Apptio, native cloud cost management tooling
  • Full data center exit achievable in as few as 8 months through parallel workstreams 30–40% infrastructure cost reduction via embedded FinOps optimization
  •  Reduced carbon footprint through migration to lower-PUE hyperscaler infrastructure
  •  Up to 30% reduction in operational downtime where AI/ML predictive workloads are deployed
  •  99.999% uptime SLA target across managed environments
  •  Zero service disruption during migration through phased execution and rollback controls
Assess your data center readiness →

Infrastructure Modernization

How do we modernize our infrastructure estate without locking into a single vendor or creating new technical debt?
Legacy infrastructure limits agility, increases cost, and delays AI adoption. KRE modernizes estates through hybrid and multi-cloud architectures, enabling flexibility, resilience, and cost control—without introducing new technical debt.
  • VMware migration across GCP, AWS, and Azure using HCX and VXLAN, with cloud-agnostic design to avoid lock-in
  • Cloud landing zones with built-in security, IAM, network, and governance foundations across multi-cloud environments
  • Network modernization from hub-and-spoke to zero-trust with Direct Connect and ExpressRoute for hybrid connectivity
  • AI/ML-ready infrastructure with GPU clusters, high-bandwidth networking, NVMe storage, and containerized ML platforms
  •  Edge and hybrid architectures for sovereign, low-latency, and operational technology workloads
  •  Multi-cloud governance via unified control of cost, security policy, identity, and connectivity across providers
  •  Embedded FinOps covering reservations, right-sizing, spot optimization, and cost anomaly detection
  • GCP (Compute Engine, GKE, Spanner, BigQuery, Vertex AI, Apigee)
  • AWS (EC2, EKS, Aurora, Bedrock, SageMaker)
  • Azure (VM, AKS, SQL Managed Instance, Azure OpenAI)
  •  VMware (vSphere, NSX, vSAN), HCX, VXLAN
  •  Terraform, Pulumi, AWS CDK, Ansible, Crossplane for IaC
  •  ArgoCD, Flux, Spinnaker for GitOps; Prometheus, Grafana, ELK, OpenTelemetry, Datadog for observability
  •  AWS Organizations, GCP Organization Policy, Azure Policy, Azure Arc for multi-cloud
    governance
  • Direct Connect, ExpressRoute, VPN Gateway, SD-WAN for hybrid connectivity
  • Up to 30-40% infrastructure cost reduction post-migration through embedded FinOps optimization – right-sizing, reserved instances, and cost anomaly detection embedded from day one
  •  Elimination of infrastructure drift – GitOps-managed environments ensure consistency across dev, staging, and production; estimated near-zero recurrence of environment-mismatch incidents post-implementation
  •  Compliance logging and audit trail operational from landing zone day one – all infrastructure changes version-controlled, auditable, and reproducible
  • Estimated up to 40% reduction in cloud spend waste achievable within 6 months of FinOps practice activation
  •  99.9% uptime SLA target maintained across all KRE-managed infrastructure environments
Request a free Architecture Assessment →

DevOps & Site Reliability Engineering

How do we build the delivery discipline that makes every migration, modernization, and AI deployment actually reach production?
Transformation delivers value only when it is executed reliably. KRE embeds DevOps and SRE disciplines to enable continuous, high-confidence delivery – ensuring every migration, modernization, and AI initiative translates into production outcomes. With 300+ certified engineers across GCP, AWS, and Azure, we operate as either an embedded extension of engineering teams or a managed service.
  • CI/CD pipelines with quality gates, security scanning, performance controls, and automated rollback
  • Platform engineering via internal developer platforms, self-service provisioning, and reusable deployment templates
  •  Full-stack quality engineering across API, UI, data, performance, mobile, and security embedded in delivery pipelines
  • Site Reliability Engineering covering SLOs, error budgets, chaos testing, and reliability operations
  •  DevSecOps with automated SAST, DAST, dependency scanning, and compliance enforcement (SOC 2, HIPAA, GDPR, PCI-DSS)
  • FinOps embedded in CI/CD through cost controls, right-sizing, and spot optimization as part of deployment
  •  OpenTelemetry-based observability for unified logs, metrics, and traces with tool-agnostic portability
  • Jenkins, GitLab CI, Azure DevOps, GitHub Actions
  • ArgoCD, Flux for GitOps; Terraform, Ansible for IaC
  • Selenium, Cypress, Playwright (UI); JMeter, K6, Gatling, LoadRunner (performance);
    Postman, REST Assured (API); Appium (mobile)
  •  Prometheus, Grafana, ELK, OpenTelemetry, Datadog for full-stack observability
  •  LangSmith for LLM application monitoring and debugging
  • GitHub Copilot, Cursor, Tabnine, Amazon CodeWhisperer – AI-augmented development as standard
  •  Azure Event Hubs, Databricks, Azure Synapse; Kafka, Spark Streaming, BigQuery, Snowflake; AWS DMS, Kinesis
  • Jenkins, GitLab CI, Azure DevOps, GitHub Actions
  • ArgoCD, Flux for GitOps; Terraform, Ansible for IaC
  • Selenium, Cypress, Playwright (UI); JMeter, K6, Gatling, LoadRunner (performance);
    Postman, REST Assured (API); Appium (mobile)
  •  Prometheus, Grafana, ELK, OpenTelemetry, Datadog for full-stack observability
  •  LangSmith for LLM application monitoring and debugging
  • GitHub Copilot, Cursor, Tabnine, Amazon CodeWhisperer – AI-augmented development as standard
  •  Azure Event Hubs, Databricks, Azure Synapse; Kafka, Spark Streaming, BigQuery, Snowflake; AWS DMS, Kinesis
Assess DevOps and SRE capabilities →

Our Cloud Modernization Services

Application Modernization

How do you change a system without changing what it fundamentally does?

Your legacy applications still deliver value; accumulated complexity and specialist dependency are what constrain them. We apply a proprietary reverse engineering methodology to extract,document, and convert legacy code into cloud-native architectures.

  • Dependency mapping (Matrix + Sequence Diagrams) to surface hidden logic before
    conversion
  •  Incremental modernization using Strangler Fig to avoid big-bang rewrites
  •  Four-strategy approach: Lift & Shift, Replatform, Refactor, Rebuild by workload
  •  DDS to DDL conversion and full integration replacement (FTP/SFTP, MFT, EDI, MQ, DDMF)
  •  Parallel system operation with controlled rollout and rollback planning
  •  Cloud-native APIs and modern data layer replacing legacy structures
  •  AI-assisted refactoring to accelerate conversion
  •  End-to-end functional, regression, and data integrity validation
  • IBM AS400/iSeries, RPG, SQLRPGLE, COBOL, CL/400, SQL/400
  •  Java, Spring Boot, REST APIs
  •  GCP DataProc, BigQuery, AlloyDB
  •  Test Data Generator and Mirror Validation IP accelerators
  •  GitHub Copilot, Cursor, Amazon CodeWhisperer for AI-assisted refactoring
  •  Jenkins CI/CD, automated QA frameworks
  • Modernization cycle times reduced by up to 30-40% through structured reverse engineering
    and AI-assisted tooling replacing manual analysis and rewrite
  •  5-10× improvement in development velocity on modernized codebases versus legacy
    AS400/COBOL environments
  •  Up to 99.99% data match between legacy and target environments validated through Mirror Validation
  •  Up to 40% reduction in ongoing maintenance cost through reduced specialist dependency
    and broader engineering supportability
  •  Zero customer incidents across modernization go-lives enabled by pre-release validation
    across 3,000+ use cases
Assess your modernization readiness →

Legacy to Cloud Migration

How do we move our most critical workloads to cloud without exposing the business to migration risk?
Legacy estates are becoming cost-intensive, harder to sustain, and increasingly disconnected from AI and cloud-native ecosystems. We enable controlled, large-scale migration through a structured methodology and proven accelerators -ensuring predictable execution, regulatory alignment, and zero-disruption outcomes.
  • 9-stage methodology: Discovery through to Transition & Operate
  •  Per-application assessment with Lift/Shift, Replatform, Refactor, Rebuild options and full TCO modelling
  •  Hot and cold migration strategies with defined rollback plans before production cutover
  •  Phased wave migration with validated non-production runs before production moves
  •  Built-in compliance for FINRA, HIPAA, SOC 2, and data residency, designed pre-migration
  •  Hyperscaler funding support across Google PSF, AWS MAP, and Azure MMP programs
  • GCP (Composer, Dataflow, Spanner, BigQuery, Firestore, PubSub)
  •  AWS (EC2, EKS, RDS, Redshift), Azure (VM, AKS, SQL Managed Instance)
  •  Mirror Validation, Data Ingestion Frameworks, Test Data Generator, Data Fabric Extension IP accelerators
  •  Terraform, Ansible, ArgoCD, Jenkins, GitLab CI
  •  SSH/WMI/SNMP discovery tooling, CMDB population, dependency mapping frameworks
  • Zero customer incidents across all major production migration launches – validated across programmes of up to $300MM+ in mainframe-to-GCP scope
  •  Estimated 50% reduction in migration timelines using KRE proprietary IP accelerators
  •  Sub-second API response times achievable post-migration on cloud-native target
    architectures – not possible on legacy mainframe platforms
  •  Data quality certification up to an estimated 99.99% match between legacy and cloud
    environments – validated through automated Mirror Validation across all migrated data
    domains
  • Up to 30-40% infrastructure cost reduction post-migration through FinOps optimization –
    delivering ongoing savings that fund further innovation investment
Request a free discovery assessment →

Data Center Migration

How do we decommission aging infrastructure and move to cloud – without a single incident affecting the business?

On-premises infrastructure is no longer economically or operationally viable at scale. We deliver end-to-end data center exit – combining dependency-led migration, parallel decommissioning, and cloud optimization to ensure a seamless, incident-free transition.

  • End-to-end migration methodology from discovery to phased execution and post-migration optimization
  • Parallel physical decommissioning aligned with cloud migration, including hardware, vendor, and compliance exit
  • Hybrid and edge architecture design for sovereign, low-latency, and regulated workloads
  • AI/ML-ready landing zones embedded from day one of migration
  • Multi-cloud architecture across GCP, AWS, and Azure to reduce lock-in and optimize workload placement
  • Post-migration FinOps covering right-sizing, reservations, spot optimization, and cost control
  • Cloud-agnostic delivery across major hyperscalers and target environments
  • GCP, AWS, Azure – cross-trained engineering teams across all three hyperscalers
  •  SSH/WMI/SNMP discovery tooling, automated dependency mapping, CMDB population
  • Terraform IaC, Jenkins CI/CD, GCP/AWS/Azure native services
  • NVIDIA A100/H100 GPU infrastructure, Kubernetes (GKE/EKS/AKS)
  •  Vertex AI, SageMaker, Azure ML for AI/ML-ready landing zones
  • CloudHealth, Apptio, native cloud cost management tooling
  • Full data center exit achievable in as few as 8 months through parallel workstreams 30–40% infrastructure cost reduction via embedded FinOps optimization
  •  Reduced carbon footprint through migration to lower-PUE hyperscaler infrastructure
  •  Up to 30% reduction in operational downtime where AI/ML predictive workloads are deployed
  •  99.999% uptime SLA target across managed environments
  •  Zero service disruption during migration through phased execution and rollback controls
Assess your data center readiness →

Infrastructure Modernization

How do we modernize our infrastructure estate without locking into a single vendor or creating new technical debt?
Legacy infrastructure limits agility, increases cost, and delays AI adoption. KRE modernizes estates through hybrid and multi-cloud architectures, enabling flexibility, resilience, and cost control—without introducing new technical debt.
  • VMware migration across GCP, AWS, and Azure using HCX and VXLAN, with cloud-agnostic design to avoid lock-in
  • Cloud landing zones with built-in security, IAM, network, and governance foundations across multi-cloud environments
  • Network modernization from hub-and-spoke to zero-trust with Direct Connect and ExpressRoute for hybrid connectivity
  • AI/ML-ready infrastructure with GPU clusters, high-bandwidth networking, NVMe storage, and containerized ML platforms
  •  Edge and hybrid architectures for sovereign, low-latency, and operational technology workloads
  •  Multi-cloud governance via unified control of cost, security policy, identity, and connectivity across providers
  •  Embedded FinOps covering reservations, right-sizing, spot optimization, and cost anomaly detection
  • GCP (Compute Engine, GKE, Spanner, BigQuery, Vertex AI, Apigee)
  • AWS (EC2, EKS, Aurora, Bedrock, SageMaker)
  • Azure (VM, AKS, SQL Managed Instance, Azure OpenAI)
  •  VMware (vSphere, NSX, vSAN), HCX, VXLAN
  •  Terraform, Pulumi, AWS CDK, Ansible, Crossplane for IaC
  •  ArgoCD, Flux, Spinnaker for GitOps; Prometheus, Grafana, ELK, OpenTelemetry, Datadog for observability
  •  AWS Organizations, GCP Organization Policy, Azure Policy, Azure Arc for multi-cloud
    governance
  • Direct Connect, ExpressRoute, VPN Gateway, SD-WAN for hybrid connectivity
  • Up to 30-40% infrastructure cost reduction post-migration through embedded FinOps optimization – right-sizing, reserved instances, and cost anomaly detection embedded from day one
  •  Elimination of infrastructure drift – GitOps-managed environments ensure consistency across dev, staging, and production; estimated near-zero recurrence of environment-mismatch incidents post-implementation
  •  Compliance logging and audit trail operational from landing zone day one – all infrastructure changes version-controlled, auditable, and reproducible
  • Estimated up to 40% reduction in cloud spend waste achievable within 6 months of FinOps practice activation
  •  99.9% uptime SLA target maintained across all KRE-managed infrastructure environments
Request a free Architecture Assessment →

DevOps & Site Reliability Engineering

How do we build the delivery discipline that makes every migration, modernization, and AI deployment actually reach production?
Transformation delivers value only when it is executed reliably. KRE embeds DevOps and SRE disciplines to enable continuous, high-confidence delivery – ensuring every migration, modernization, and AI initiative translates into production outcomes. With 300+ certified engineers across GCP, AWS, and Azure, we operate as either an embedded extension of engineering teams or a managed service.
  • CI/CD pipelines with quality gates, security scanning, performance controls, and automated rollback
  • Platform engineering via internal developer platforms, self-service provisioning, and reusable deployment templates
  •  Full-stack quality engineering across API, UI, data, performance, mobile, and security embedded in delivery pipelines
  • Site Reliability Engineering covering SLOs, error budgets, chaos testing, and reliability operations
  •  DevSecOps with automated SAST, DAST, dependency scanning, and compliance enforcement (SOC 2, HIPAA, GDPR, PCI-DSS)
  • FinOps embedded in CI/CD through cost controls, right-sizing, and spot optimization as part of deployment
  •  OpenTelemetry-based observability for unified logs, metrics, and traces with tool-agnostic portability
  • Jenkins, GitLab CI, Azure DevOps, GitHub Actions
  • ArgoCD, Flux for GitOps; Terraform, Ansible for IaC
  • Selenium, Cypress, Playwright (UI); JMeter, K6, Gatling, LoadRunner (performance);
    Postman, REST Assured (API); Appium (mobile)
  •  Prometheus, Grafana, ELK, OpenTelemetry, Datadog for full-stack observability
  •  LangSmith for LLM application monitoring and debugging
  • GitHub Copilot, Cursor, Tabnine, Amazon CodeWhisperer – AI-augmented development as standard
  •  Azure Event Hubs, Databricks, Azure Synapse; Kafka, Spark Streaming, BigQuery, Snowflake; AWS DMS, Kinesis
  • Jenkins, GitLab CI, Azure DevOps, GitHub Actions
  • ArgoCD, Flux for GitOps; Terraform, Ansible for IaC
  • Selenium, Cypress, Playwright (UI); JMeter, K6, Gatling, LoadRunner (performance);
    Postman, REST Assured (API); Appium (mobile)
  •  Prometheus, Grafana, ELK, OpenTelemetry, Datadog for full-stack observability
  •  LangSmith for LLM application monitoring and debugging
  • GitHub Copilot, Cursor, Tabnine, Amazon CodeWhisperer – AI-augmented development as standard
  •  Azure Event Hubs, Databricks, Azure Synapse; Kafka, Spark Streaming, BigQuery, Snowflake; AWS DMS, Kinesis
Assess DevOps and SRE capabilities →
  • FAQ

Frequently Asked Questions

Most integrators approach legacy modernization as a rewrite project. We approach it as a knowledge extraction and risk elimination exercise first – using our proprietary Dependency Matrix and Sequence Diagram methodology to document what the legacy system actually does before any code is converted. This is what enables zero-incident go-lives on systems with decades of undocumented business logic.

Timelines vary significantly by estate size, complexity, and migration strategy. Our IP accelerators
compress timelines by an estimated up to 50% versus manual approaches. A focused application modernization engagement can deliver in weeks; a large-scale mainframe decommission programme spans years. Every engagement begins with a Discovery phase that produces an accurate, workload-specific timeline estimate.

Compliance architecture is established before the first workload moves – not retrofitted at the end. Our IAM and Security stage covers identity provider setup, encryption, network segmentation, SIEM integration, and audit trail configuration as prerequisites to migration. We have delivered for organizations operating under FINRA, HIPAA, SOC 2, GDPR, and data residency requirements.

Yes. We are platform-agnostic and cross-trained across GCP, AWS, Azure, and VMware. We design for your workload and your existing investments – not the platform with the best partnership incentive. Our teams work within existing CI/CD, IaC, and observability tooling or recommend replacements where the business case supports it.

Every engagement begins with a free assessment – Discovery for migration programmes, Application Assessment for modernization, Architecture Assessment for infrastructure. This produces the dependency map, workload inventory, and strategy recommendation that defines the programme before any commitment is made.

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