7 Legacy Modernization Examples We Delivered: Approaches, Results, Lessons

Legacy software modernization is the process of updating existing applications, systems, or infrastructure to meet changing business needs. It can involve replacing outdated technologies, improving system architecture, migrating to modern platforms, or gradually rebuilding functionality.

Understanding how modernization works in practice can help evaluate possible approaches, anticipate challenges, and set realistic expectations for cost, timelines, and results.

The SCAND team has selected seven legacy modernization case studies from our own projects to show how we helped clients address different modernization challenges, the engineering approaches we applied, and the results we delivered.

The projects cover a range of modernization scenarios, from replacing Excel-based business processes and migrating applications away from Xamarin and Silverlight to restructuring a PHP 7 platform. Client names remain confidential, and the results reflect the information available in the corresponding project descriptions.

Legacy System Modernization Examples at a Glance

Legacy modernization can take different forms depending on a system’s age, technical limitations, business requirements, and future goals. While some organizations need to replace outdated applications, others can extend the life of existing software through targeted improvements, architectural changes, or gradual migration.

The key is to choose an approach that addresses technical challenges without disrupting essential business operations.

The table below provides an overview of seven application modernization case studies delivered by SCAND, highlighting each project’s initial challenges, the engineering approach applied, and the reported results.

These modernization examples demonstrate why choosing an approach based on the actual condition of an application matters. A system with valuable business logic may need refactoring, while an unsupported application may require a gradual rebuild. In other cases, understanding the existing codebase or strengthening a prototype is the most practical first step.

Example 1: From Excel to ERP for a UK Energy Company

Legacy modernization often starts when existing tools can no longer support growing business needs. Manual processes, outdated technologies, and disconnected data sources can make daily operations difficult to manage and scale.

In SCAND’s UK energy ERP modernization case, you can learn how the team helped replace Excel-based workflows with a centralized platform, streamline data processing, and maintain business continuity throughout the transition.

The Challenge

A UK branch of a large European energy company relied on several Excel files to collect operational data, perform calculations, and generate invoices.

As the business grew, this approach became extremely difficult to maintain. The company also had to adapt to changing regulatory requirements and process information from more than 50 external data sources.

Replacing the existing workflow presented another problem: business operations could not stop at the time the new platform was being developed.

What We Did

SCAND developed a new ERP platform for the energy market using .NET Core and Angular.

The solution integrated external systems through APIs, email, and file exchange. It also included a formula builder, gas distribution modules, reporting functionality, and transaction reconciliation.

A particularly important part of the project was transferring the calculation logic from Excel into the new application. Instead of automatically generating invoices, the platform allowed an operator to review the calculated results before invoices were created.

To minimize risks, the new server environment initially ran alongside the existing system. Functionality was transferred gradually, allowing the client to validate the new processes before switching over. The modular architecture also enabled several project components to be developed in parallel.

Results and Lessons

The project delivered:

• Integration with more than 50 external data sources.
• First production version delivered in 3 months.
• Approximately 12 months until the project transitioned to maintenance.
• Reported annual database cost savings of $12,000.

The main lesson we learned is that replacing spreadsheets requires more than transferring formulas into another application. Teams must also preserve calculation rules, validation procedures, and the human approval processes that support business operations.

Running the old and new environments in parallel makes it possible to compare results and identify discrepancies before discontinuing the original workflow.

Example 2: Xamarin to Flutter Application Modernization Case Study

Legacy modernization is not always about replacing an entire system at once. It can also involve moving an existing product to a more sustainable technology while preserving its core functionality and user experience.

In the Xamarin to Flutter digital reading platform case study, SCAND assisted in migrating an existing digital reading platform well before the end of support for Xamarin, while fully preserving its functionality.

The Challenge

A digital reading platform had been running on Xamarin applications that were approaching the end of their support lifecycle. Over eight years, the applications had accumulated substantial business logic, platform-specific code, and technical debt. One particularly important component was the legacy reading module.

The client needed to move to a supported technology without interrupting feature development or compromising the existing user experience.

What We Did

SCAND implemented a gradual modernization strategy, keeping the existing Xamarin applications in production while developing Flutter replacements in parallel. As part of the Flutter development approach, the team rebuilt application scenarios using the new architecture rather than automatically converting C# code into Dart.

The migration preserved existing server APIs, data contracts, and authentication mechanisms. Each component was assessed individually and assigned one of four strategies:

• Keep it unchanged
• Refactor it
• Rewrite it
• Replace it

The old reading module was replaced with the epuBear SDK. The team also introduced regression and characterization tests to document existing application behavior and spot potential compatibility problems.

A beta release and phased rollout allowed users to gradually move to the new applications. The Xamarin applications were discontinued only after the Flutter versions achieved feature parity.

AI-powered tools helped accelerate the migration of repetitive components, while engineers retained responsibility for architectural decisions and their implementation.

Results and Lessons

The migration delivered:

• 100% of the migration completed before Xamarin support ended.
• Approximately 90% shared code between iOS and Android.
• Around 30% shorter feature delivery cycles.
• Approximately 45% less time spent on regression testing.
• More than 99.5% crash-free sessions.
• Around 25% faster migration of repetitive components with AI assistance.
• Overall project duration of approximately 18 months.

The main lesson of this project is that automatically converting legacy code does not solve underlying architectural problems. A successful migration requires understanding existing behavior, documenting it through tests, and rebuilding application scenarios where necessary.

AI can accelerate repetitive engineering tasks, but architectural decisions and quality assurance remain engineering responsibilities.

Example 3: Silverlight to a Web-Based SaaS for Task Management

Legacy modernization can also involve moving a product away from a technology that limits its compatibility and future development. As user expectations change, desktop-focused applications may need to become more flexible and accessible across devices.

In the Silverlight task management SaaS case study, we explain how we helped a European software developer transform a Silverlight-based task management product into a web-based SaaS solution, preserving its core functionality.

The Challenge

A European software developer had a task management product built on Microsoft Silverlight. The application supported meeting management, project planning, and task tracking.

As Silverlight support and compatibility with modern environments declined, maintaining the product became increasingly difficult.

The client needed to preserve its existing business logic and functionality while improving accessibility, usability, and performance. Users also needed to access the application through different browsers and devices.

What We Did

SCAND had originally developed the client’s Silverlight solution, including its functionality, business logic, and user interface.

This background gave the team an understanding of how the product worked and allowed modernization to continue as an extension of the existing development and maintenance relationship.

The solution was transformed into a web-based SaaS application built around HTML5. The work included:

• Reworking outdated application components.
• Updating the architecture.
• Introducing additional product functionality.
• Automating the transition from meeting discussions to task creation.
• Improving access across browsers and devices.

The resulting product supported Gantt chart planning, process templates, user roles and permissions, full-text search, reporting, and data export. The technology stack included .NET, REST APIs, AngularJS, JavaScript, jQuery UI, and HTML5.

Results and Lessons

As part of the project, the product was successfully migrated from the Silverlight platform to an HTML5-based web application. The modernized solution enabled access from various devices, improved task tracking, and allowed for a more scalable architecture.

The main lesson is that when a development team already understands the legacy application, modernization can build on that knowledge rather than starting from scratch. Preserving business logic and understanding the product’s history can make a major difference when updating software that has evolved over many years.

Example 4: Modernizing a PHP Data Processing Platform

Legacy modernization often requires more than updating an outdated programming language. As a platform grows, tightly coupled components, poor data handling, and limited test coverage can make it harder to maintain, scale, and introduce new features.

This example explores how SCAND helped modernize a PHP-based data processing platform by gradually refactoring its monolithic architecture, improving data handling and observability, and strengthening automated testing.

The Challenge

A client operated a data processing platform built around a PHP 7 monolith and a single relational database.

The system had several architectural limitations. Message ingestion was closely connected to business logic, historical data was stored in a format designed for daily value sets, logs lacked a common structure and traceable identifiers, and automated test coverage was limited.

These issues made it difficult to maintain the application and introduce changes without affecting existing production processes. The client needed to improve the platform while preserving its existing functionality and data.

What We Did

SCAND applied an incremental modernization strategy rather than rewriting the entire application.

First, message ingestion was separated from the main business logic and moved into dedicated services built with PHP 8.3. RabbitMQ was introduced as a message queue, supporting retry mechanisms and separate storage for messages that could not be processed successfully.

The team also improved historical data storage by introducing TimescaleDB alongside the existing relational database.

To reduce migration risks, data was written to both environments in parallel. Automated reconciliation was used to compare values, while reading operations were switched gradually, with the possibility of returning to the previous setup if needed.

Another important part of the work involved improving observability. Logs were centralized using Elasticsearch and Kibana, and each message received a shared identifier to make tracing easier.

Finally, the team strengthened the testing foundation with regression testing for the existing monolith, helping verify that changes did not affect established application behavior.

Results and Lessons

The modernization improved the platform’s testing foundation:

• The number of test classes increased by more than 10 times.
• Line coverage of the main module reached 93.53%.

The project demonstrates that outdated system modernization does not always require replacing the entire application or changing every component’s technology. It also highlights the importance of maintaining expertise in PHP development when modernizing systems built on established technologies.

When production behavior must remain stable, engineers can begin by introducing tests, separating independent services, and improving observability. This creates a safer foundation for future architectural changes.

AI-Assisted Transformation: Three More Engineering Examples of Legacy Modernization

AI is becoming a useful part of legacy application modernization, helping engineering teams understand existing codebases, accelerate repetitive development tasks, and turn early-stage prototypes into production-ready applications.

However, its value depends on how it is applied: AI-generated output still needs to be reviewed, tested, and integrated into a solid architecture. The following three examples show how SCAND applied AI in different modernization scenarios.

Example 5: Documenting 3,000+ Files with a Private LLM

Legacy modernization can seem quite problematic when a system has grown over time without sufficient documentation. Understanding thousands of files, dependencies, and business logic can take considerable engineering effort and slow down further development.

This AI-powered source code documentation case study demonstrates how SCAND used a private large language model (LLM) to accelerate documentation of a large codebase, generating documentation for more than 3,000 files in approximately 10 hours.

The Challenge

A client had a codebase containing approximately 3,200 files written in Java, Kotlin, and React (TypeScript).

Documentation existed only for some complex components. The company needed to document the entire codebase according to Javadoc and JSDoc standards within a limited timeframe.

Because the source code was confidential, the solution also had to avoid exposing it through public AI services.

This project was not a direct application migration. It addressed an important preliminary step in modernization: understanding and documenting an existing codebase before making architectural changes.

What We Did

SCAND evaluated six open-source language models using a sample set of documentation tasks. The team selected Qwen 2.5 Coder 32B for the project.

To keep the source code private, SCAND rented GPU servers and built a dedicated application that worked with Ollama. The application generated documentation and inserted it directly into the original source files. The implementation included:

• Separate prompts for Java, Kotlin, and TypeScript.
• Logging and execution statistics.
• Error handling and recovery mechanisms.
• Secure communication channels.
• Verification that logs were not retained on the remote side.

Part of the workload was processed using two machines equipped with H100 GPUs.

Results and Lessons

The project delivered:

• More than 3,000 files documented in approximately 10 hours.
• Approximately $25 spent on GPU server rental.

The project demonstrated that a private LLM could accelerate a large-scale documentation task without sending confidential source code to a public AI service.

The main lesson is that undocumented code can become a significant obstacle to modernization. Before deciding what to rewrite, replace, or refactor, teams need to understand what the existing software actually does.

AI-assisted documentation can help turn a poorly understood codebase into a more accessible technical asset and provide a foundation for subsequent engineering decisions.

Application Modernization Example 6: From an AI Prototype to a Production-Ready ERP

Legacy modernization becomes especially important when an early-stage prototype needs to evolve into a reliable business application. While AI-powered development tools can accelerate initial implementation, prototypes may lack the security, data architecture, monitoring, and infrastructure required for production use.

This Google AI Studio to production case study shows how SCAND helped transform a Google AI Studio prototype into a production-ready ERP application by strengthening its architecture, improving security and access control, and introducing reliable deployment, backup, and monitoring processes.

The Challenge

A client had created a progressive web application for managing employees, tasks, and events using Google AI Studio. The prototype demonstrated that the concept could work, but its architecture was not ready for production.

The application relied on a NoSQL database despite having relational data requirements. Authentication and role management needed improvement, while backups, monitoring, and a migration plan were missing.

The solution was also closely tied to Google services, creating additional infrastructure dependencies.

What We Did

SCAND began with an audit of the exported source code and reconstructed the application’s requirements. The team then addressed the main architectural, security, and operational issues. The work included:

• Identifying and fixing vulnerabilities related to the OWASP Top 10.
• Improving authentication and role-based access control.
• Introducing email-based two-factor verification.
• Replacing NoSQL storage with PostgreSQL and Prisma.
• Moving hosting from Firebase and Cloud Run to a dedicated VPS using Docker and Cloudflare.
• Implementing backups with defined RTO and RPO.
• Setting up CI/CD pipelines.
• Introducing Prometheus and Grafana for monitoring.
• Performing load testing.

The client also retained a spec-driven development approach for working with AI assistants, providing a structured way to manage future AI-assisted development.

Results and Lessons

The modernization delivered:

• P99 response latency below 300 ms at 98 requests per second.
• Approximately 200 daily active users and 500 monthly active users.
• Deployment time of approximately 2 minutes.
• Monthly hosting and email expenses reduced from $126 to $22, including VAT, representing approximately 83% savings.
• One critical and four medium-severity vulnerabilities related to OWASP Top 10 identified and fixed.
• Project completed in approximately 1.5 months.

The main lesson is that an AI-generated prototype can demonstrate a product idea quickly, but a working prototype is not necessarily a production-ready application.

Database design, security, authentication, backups, monitoring, and deployment processes still require careful engineering. An initial technical audit helps identify what must be improved before the application can support real business operations.

Example 7: An AI-Built Prototype to a Multi-Tenant Emergency Response Platform

AI-generated prototypes can help bring a product idea to life quickly, but turning a prototype into a reliable, scalable platform requires additional engineering work. Production systems must support secure data management, user access controls, integrations, and the ability to handle growing workloads.

This AI-built emergency response platform case study shows how SCAND helped transform an AI-built emergency response prototype into a multi-tenant platform for storm recovery operations, strengthening its architecture, implementing tenant isolation and role-based access controls, and integrating the tools needed to support real-world workflows.

The Challenge

An American startup working in storm recovery had an interesting starting point: its founder, who had no formal engineering background, had created an initial product prototype using Replit, AI agents, and LLM-based development tools.

The prototype included weather monitoring, photo-based damage assessment, and automated workflows.

However, the startup needed more robust field service management software to support business growth. The solution had to accommodate multiple organizations while keeping their data isolated, support external integrations, and prepare for connections with insurance systems.

This was not a migration from an established legacy application. It was the transformation of an AI-built prototype into a production-oriented platform.

What We Did

SCAND started by auditing the existing application and assessing its architecture. The team then refactored and separated the frontend and backend into more manageable modules. Key engineering tasks included:

• Implementing a multi-tenant architecture.
• Stabilizing AI-powered workflows for photo-based damage assessment.
• Improving coordination between multiple AI agents.
• Integrating Stripe, DocuSign, Twilio, and Google Ads.
• Optimizing database performance.
• Introducing containers, Kubernetes, and CI/CD pipelines.
• Preparing the platform for future insurance-related integrations.

The technology stack included Node.js, TypeScript, React, PostgreSQL, and pgvector.

Results and Lessons

The complete product was developed in approximately six months, with SCAND contributing around 3.5 months of that period. According to the client, the development speed was comparable to the work of a team of four to six engineers.

The platform also reduced insurance report preparation from days to minutes. The client described the result as follows:

What would traditionally take an 18-month team effort was completed in roughly 6 months, with SCAND contributing for about 3.5 of those months.

The main lesson is that AI tools can help founders create functional prototypes without building a full engineering team. However, scaling those prototypes requires more than adding features.

Architecture, testing, database optimization, tenant isolation, and reliable integrations are essential when software moves from an experiment to a business-critical platform.

An engineering audit is a practical first step toward identifying the work needed to make that transition.

Where AI Helps in Legacy Modernization

AI-assisted modernization is justified when applied to specific engineering tasks with measurable outcomes. SCAND’s projects demonstrate three areas where AI can support development teams.

• Understanding existing codebases. AI can analyze source code, identify dependencies, summarize functionality, and generate technical documentation. This helps engineers build a clearer picture of an unfamiliar or poorly documented system before making changes.
• Accelerating repetitive development tasks. AI can assist with code generation, component migration, test creation, and routine refactoring. This allows developers to spend less time on repetitive implementation and more time on architecture and complex engineering decisions.
• Improving AI-generated prototypes. AI-generated applications often require further work before production deployment. Engineers must review the code, address issues, and introduce testing, monitoring, and deployment processes.

This way, AI does not replace engineering expertise. It works best as an additional tool that helps teams modernize legacy systems way better while keeping technical decisions, quality assurance, and production readiness under human control.

Common Challenges in Legacy Modernization and How These Projects Handled Them

Legacy modernization projects often involve more than outdated programming languages or unsupported frameworks. Business logic may be poorly documented, critical processes may depend on manual checks, and changes to data structures can introduce many risks.

The following table summarizes recurring challenges in SCAND’s projects and the best practices used to address them.

How to Choose a Modernization Approach

There is no single modernization strategy that works for every legacy application. The right choice depends on the condition of the existing software, the importance of its business logic, the limitations of the current infrastructure, and the risks associated with changing it.

The following approaches illustrate how different modernization goals can translate into practical engineering decisions. For a broader explanation, see our guide on how to modernize legacy applications.

These approaches are not mutually exclusive. A project may begin with code documentation, continue with refactoring, and eventually involve replacing selected components or moving infrastructure.

What to Prepare Before You Modernize

A successful modernization project begins before development work starts. A clear picture of the existing system helps teams estimate the effort, identify risks, and select an appropriate migration strategy. Consider preparing the following six things:

1. Define the business goal and baseline metrics. Identify why modernization is necessary and what should improve. Record current delivery cycles, application performance, infrastructure expenses, incident rates, and testing time where relevant.
2. Inventory applications and integrations. List the systems, databases, services, external APIs, scheduled jobs, and third-party dependencies connected to the application. Identify which components are business-critical and which can be replaced independently.
3. Locate the business logic. Determine whether essential rules live in application code, Excel spreadsheets, configuration files, database procedures, or employees’ knowledge. Document processes that are not formally described.
4. Assess data quality and migration requirements. Identify data formats, relationships, inconsistencies, and historical storage requirements. Define how old and new systems will be compared and how discrepancies will be resolved.
5. Prepare tests and a validation environment. Establish a baseline of current application behavior using regression and characterization tests. Make sure the team has a representative environment for checking functionality and performance before production changes.
6. Plan the transition and rollback. Decide whether the systems will run in parallel, how functionality will be transferred, what conditions must be met before switching, and how the team can restore the previous environment if something goes wrong.

How SCAND Helps with Legacy Modernization

SCAND has more than 25 years of software development experience, a team of 250+ engineers, and a portfolio of 900+ completed projects. The company maintains ISO 9001 and ISO 27001 certifications.

Our project experience includes modernizing systems built around technologies and processes such as Silverlight, Xamarin, PHP 7, and Excel-based business workflows. We approach each application according to its existing architecture, business requirements, and constraints.

Depending on the project, our engineers introduce automated tests before making changes, develop replacement components in parallel with existing systems, and switch functionality in stages.

We also apply AI tools to repetitive development and documentation tasks when the results can be measured and the client’s data requirements can be respected.

Whether you need to replace an unsupported application, restructure a data processing platform, or bring an AI-generated prototype to production, SCAND can help assess the existing system and develop a practical modernization roadmap.

Frequently Asked Questions (FAQs)

What is a legacy system modernization case study?

A legacy modernization case study describes how an organization updated, replaced, or restructured an existing software system. It covers the original challenges, chosen approach, implementation process, and results, helping businesses evaluate modernization strategies and understand potential risks.

How long does legacy modernization take?

The timeline depends on application complexity, integrations, data requirements, and functionality to preserve. SCAND's projects ranged from approximately 1.5 months for an AI-generated ERP prototype to production to around 18 months for a mobile application migration.

Can you modernize a system without stopping the business?

Yes. Parallel development and phased migration allow businesses to continue operating during modernization. SCAND used this approach for an energy ERP replacement and a Xamarin-to-Flutter migration, keeping existing systems operational until the new solutions were ready.

Should we rewrite or refactor a legacy application?

It depends on the value of existing functionality and the condition of the codebase. Refactoring may include introducing microservices to break down a monolithic architecture while preserving valuable business logic. Rebuilding or replacing may be better when technology is unsupported or no longer meets business needs.

What results should a modernization project measure?

Track measurable outcomes such as feature delivery time, regression testing effort, test coverage, shared code percentage, and maintenance costs. SCAND's projects achieved approximately 30% shorter feature cycles, 45% less regression testing, and an 83% reduction in hosting and email expenses in one case.

How do you keep data accurate during migration?

Document existing data structures, identify dependencies, and establish validation and reconciliation procedures. Comparing results across old and new environments, followed by gradual data switching, helps detect discrepancies before the replacement system becomes the primary source of data.

Can AI speed up legacy modernization?

Yes. AI can accelerate repetitive development and documentation, contributing to better outcomes in application modernization ROI case studies. In SCAND's projects, it helped migrate repetitive components approximately 25% faster and document more than 3,000 source files in around 10 hours. Engineering expertise remains essential for architecture, validation, security, and testing.