Legacy Modernization & Migration Services

Q: Do you only migrate to one cloud?

No. Migrations can target AWS, Microsoft Azure, and Google Cloud, and multi-cloud or hybrid architectures are supported when the business case is real. Multi-cloud strategies suit organizations with workloads that benefit from different cloud provider strengths, or those with regulatory data residency requirements. Hybrid cloud keeps sensitive or latency-critical workloads on-premises while moving others to public cloud. The right cloud architecture should be selected based on technical fit, cost modeling, and business requirements, not vendor preference, and designed to avoid lock-in where future flexibility has strategic value.

What Is Legacy Modernization?

Legacy modernization is the structured process of upgrading outdated software, infrastructure, and architecture to modern, scalable, cloud native equivalents. It is a portfolio decision across every legacy asset: deciding which to rehost, replatform, refactor, replace, retire, or retain, and executing each path in the right order. Done well, legacy modernization reduces operating cost, accelerates delivery, removes dependencies on end of life software, and prepares the organization for AI, analytics, and event driven workloads that cannot run on yesterday's stack.

Our Legacy Modernization and Migration Services

Application Modernization

We refactor and re-architect aging applications into clean, maintainable, cloud native services. This includes breaking up tightly coupled modules, removing dead code, replacing deprecated frameworks, and introducing patterns like API gateways and asynchronous messaging where they earn their keep.

Cloud Migration to AWS, Azure, and GCP

We move workloads from on premise data centres to AWS, Azure, or Google Cloud, choosing rehost, replatform, or re-architect per workload. Our migrations include landing zone design, networking, identity, observability, and FinOps controls so cost stays predictable after go live.

.NET Framework to Modern .NET

We migrate .NET Framework 4.x applications to modern .NET (8 and 9), modernize WebForms and WCF services, containerize on Docker, and deploy on Azure App Service, AKS, or ECS. The result is faster runtime performance, lower hosting cost, and a long supported framework with predictable release cycles.

Monolith to Microservices

We decompose monoliths using a domain driven approach, extracting bounded contexts, introducing API contracts, and routing traffic incrementally. We use the strangler fig pattern to retire the legacy system safely, with no big bang cutover.

Database Modernization

We migrate from Oracle and SQL Server to managed cloud equivalents like Postgres, Aurora, and Cloud SQL, or evolve schemas to fit a microservices architecture. We handle replication, dual writes, and cutover so data stays consistent throughout.

DevOps and Continuous Modernization

We set up CI/CD pipelines, infrastructure as code (Terraform, Pulumi), automated testing, and observability so modernization continues after we leave. Modernization is a capability, not a one off project.

Modernize the Systems that Hold you back and Keep the Ones that Work

Legacy systems start as assets and quietly become liabilities. They get harder to staff, more expensive to run, slower to change, and more fragile every quarter. inVerita's legacy modernization and migration services help engineering and IT teams move legacy applications, data, and infrastructure to modern cloud platforms, without breaking what already works. We modernize on premise systems to AWS, Azure, and Google Cloud. We migrate .NET Framework applications to modern .NET. We decompose monoliths into microservices. Our focus is on three things: protecting the business during the transition, capturing measurable cost and performance gains, and leaving your team with a stack they can confidently own.

Book a free modernization assessment

Our Six Phase Approach to Legacy Migration

1. Discover

We audit code, dependencies, data, infrastructure, integrations, and team capacity. The output is a complete map of what you own, what is risky, and what is worth modernizing first.

2. Decide

We assign each application a strategy from the 6 Rs framework based on business value, technical debt, and risk. Every recommendation includes a cost estimate and a clear ROI rationale.

3. Architect

We design the target state: runtime topology, integration boundaries, data contracts, security model, and observability standards. This phase locks in the engineering decisions that protect performance, cost, and compliance long after delivery.

4. Modernize

We execute in phased waves, smallest viable scope first, so business value lands early and risk stays contained. Each wave ships behind a feature flag with full rollback.

5. Migrate

We move workloads to their target environment with parallel runs, dual writes, and zero downtime cutovers wherever possible. Observability is in place before production traffic touches the new system.

6. Optimize

We measure post migration performance, cost, and reliability against baseline, and iterate. We hand over runbooks, dashboards, and documentation so the new stack lives independently.

From Databricks to Snowflake: 70% Lower Cloud Costs

A data driven SaaS company was running its analytics platform on Databricks. Costs were climbing every quarter, machine learning workloads were hard to scale, and data governance was thin. The team needed a modern data foundation without losing any of the analytics already in production. inVerita led a phased migration from Databricks to Snowflake. We redesigned the raw data handling layer, replaced third-party ingestion tools with native AWS services, and implemented streaming and CDC pipelines for near real-time analytics. Snowflake cost optimization targeted the usual culprits: oversized warehouses, missing auto-suspend, full table scans, and inefficient transformations. Governance was established from day one.

FULL CASE

Outcomes: Infrastructure costs reduced by up to 70%, faster and more reliable analytics across teams, full data governance in place, and a platform now ready for machine learning workloads.

FULL CASE

Why Teams Choose inVerita

AI-Enhanced Delivery

We use AI tooling across the lifecycle to accelerate legacy code understanding, refactoring, and test generation. Static analysis and AI-assisted code review compress discovery from weeks to days, automated test scaffolding raises coverage on legacy modules, and AI-supported documentation captures tribal knowledge before it walks out the door. The result is faster delivery at meaningfully lower cost than traditional modernization engagements.

Security and Compliance by Default

HIPAA, GDPR, and SOC 2 are not bolted on at the end of our projects. Encryption in transit and at rest, identity and access controls, audit logging, and data residency are designed into the target architecture from the Architect phase forward. We have shipped modernization work in healthcare and financial services where regulatory exposure is the project's biggest risk, and we treat it that way.

Senior Engineers and Real Knowledge Transfer

Our delivery teams are senior. There is no offshore handoff, no mystery box, no consultants spinning down with the runbook in their heads. Every project leaves your internal team with documented architecture decisions, runbooks for every workflow, dashboards for every key metric, and the confidence to evolve the system on their own.

Outcomes Our Clients See

Lower total cost of ownership through reduced hosting fees, removed licensing costs, and less maintenance burden. Faster release cycles as deployment moves from monthly to weekly or daily. Higher reliability as cloud native patterns replace fragile legacy infrastructure. Stronger security and compliance and the ability to add modern capabilities like AI, analytics, and real time data.

Ready to retire your legacy stack on your terms?

Tell us what you are running today. We will return a phased modernization roadmap, a realistic cost estimate, and a clear timeline within two weeks.

BOOK A CALL

LET'S CREATE TOGETHER

DATA AUTOMATION AND BI PLATFORM FOR A FINANCIAL ADVISORY FIRM

OPTIMIZING LIVE CONNECTION PERFORMANCE

AI-POWERED TELEMEDICINE APPLICATION

Frequently Asked Questions

What is legacy modernization?

Legacy modernization is the structured process of upgrading outdated software, infrastructure, and architecture to modern, scalable, cloud-native equivalents while preserving the business value of the original system. It encompasses strategies ranging from rehosting (lift-and-shift to cloud), replatforming, refactoring, and re-architecting, to replacing legacy monoliths with microservices, migrating on-premises workloads to cloud infrastructure, and eliminating technical debt that limits the ability to scale operations or ship new product capabilities.

What are the main legacy modernization strategies?

The six core legacy modernization strategies are: rehost (lift-and-shift to cloud without code changes), replatform (move to managed cloud services with minor optimizations), refactor (restructure code to improve architecture without changing behavior), re-architect (redesign using cloud-native patterns such as microservices), rebuild (rewrite from scratch using modern technology), and replace (retire the legacy system with a SaaS product). Most large programs combine multiple strategies, applying the most cost-effective approach to each workload based on business criticality and modernization ROI.

How long does a legacy modernization project take?

A legacy modernization project typically takes 3 to 12 months depending on portfolio size, system complexity, and the strategies applied. A focused rehosting migration of 20 to 50 workloads can complete in 6 to 12 weeks. Refactoring or re-architecting a complex legacy monolith into microservices takes 6 to 18 months. Every project should be structured in phased delivery waves so business value lands continuously rather than at the end, reducing organizational risk and allowing feedback from early phases to improve later ones.

Will my system have downtime during migration?

Most legacy migrations can be executed with near-zero downtime using parallel run architectures, incremental cutovers, and blue-green deployment patterns. In a parallel run, the legacy system and new system operate simultaneously, with traffic gradually shifted to the new platform after validation. Where brief maintenance windows are unavoidable, they are planned off-peak, communicated in advance, and rehearsed through dry-run exercises. Every migration cutover plan should include automated rollback procedures so the legacy system can be restored immediately if an issue is detected post-cutover.

Can you modernize incrementally instead of doing a full rewrite?

Yes, and incremental modernization is the recommended approach for most legacy systems. Full rewrites carry high risk: they are expensive, take longer than estimated, and frequently replicate the problems of the original system. The strangler fig pattern, which replaces legacy components incrementally with modern services while the original system continues operating, allows teams to deliver value continuously, validate each component in production before retiring its legacy equivalent, and reduce the probability of project failure. Phased modernization consistently outperforms big-bang rewrites in both cost and delivery success rate.

What is the strangler fig pattern in legacy modernization?

The strangler fig pattern is an incremental modernization strategy where new services are built alongside an existing legacy system and traffic is progressively routed to them until the legacy system can be safely retired. Named after the strangler fig tree that grows around and eventually replaces its host, the pattern lets organizations modernize piece by piece without a high-risk full replacement. Each new service is built, tested, and validated in production before the corresponding legacy component is decommissioned, making it the industry-standard approach for high-traffic, business-critical systems.

How much does legacy modernization cost?

Legacy modernization costs depend on portfolio size, complexity, and strategy. A focused rehosting migration of 20 to 50 workloads typically costs $50,000 to $200,000. Refactoring or re-architecting a legacy monolith into microservices typically costs $150,000 to $600,000 depending on codebase size and integration complexity. Full legacy platform replacement with modern custom development costs $500,000 or more for enterprise systems. Cloud infrastructure costs typically fall by 20 to 40% compared to on-premises maintenance after migration, improving total cost of ownership over a three-to-five-year horizon.

Do you only migrate to one cloud?

Migrations can target AWS, Microsoft Azure, and Google Cloud, and multi-cloud or hybrid architectures are supported when the business case is real. Multi-cloud strategies suit organizations with workloads that benefit from different cloud provider strengths, or those with regulatory data residency requirements. Hybrid cloud keeps sensitive or latency-critical workloads on-premises while moving others to public cloud. The right cloud architecture should be selected based on technical fit, cost modeling, and business requirements, not vendor preference, and designed to avoid lock-in where future flexibility has strategic value.

How is AI being used in legacy modernization?

AI is being used in legacy modernization to accelerate code analysis, automated documentation generation, dependency mapping, and code translation between languages. Tools such as Amazon Q Developer, GitHub Copilot, and purpose-built migration platforms use large language models to analyze legacy COBOL, RPG, and mainframe code, generate modern Java or Python equivalents, and produce documentation for systems with no existing specs. AI reduces the manual effort of legacy code discovery and analysis by 40 to 70% in typical engagements and accelerates test case generation for validating migrated components.

Can AI automatically migrate legacy code?

AI can accelerate and partially automate legacy code migration, but fully autonomous migration of complex enterprise systems still requires human oversight. AI tools excel at translating well-structured COBOL or RPG code into modern languages, generating unit tests for migrated components, and producing documentation from undocumented legacy systems. Complex business logic, deeply coupled architectures, and undocumented edge-case behaviors require experienced engineers to validate AI-generated outputs. The most effective approach combines AI-assisted code translation for well-defined patterns with human-led architectural decisions and quality assurance for business-critical logic.

How does AI help analyze and document legacy systems?

AI dramatically reduces the time required to analyze and document legacy systems, which is typically the slowest and most expensive phase of a modernization project. Large language models analyze existing codebases to generate functional documentation automatically, identify undocumented business rules embedded in procedural code, map dependencies between components and databases, and produce data flow diagrams. AI-assisted discovery reduces the legacy analysis phase from months to weeks. This compresses the planning horizon, reduces consultant hours, and gives engineering teams a structured knowledge base to build the modernization roadmap from.