Home / AI-Assisted Software Development / Legacy Application Modernisation
The same functionality on a more secure, maintainable foundation. AI assists the repetitive development and testing as we move proven functionality onto a modern platform — engineers direct the process, remain accountable for the code and approve the results.
Problem and outcome
Your application may still serve the business well — in a browser or on a desktop. The pressure usually comes from what sits underneath it.
Ageing components lose support. Unsupported components increase exposure and make security fixes harder to deliver.
Specialist developers become scarce and hidden dependencies slow every fix. Changes demand increasing effort and maintenance cost rises.
Retain the workflows users trust on a supported, maintainable platform. Preserve the agreed business behaviour; add new features after a successful migration.
Three-step method
We capture behaviour broadly, migrate in small parts, then compare results before human approval.
Source-code analysis and real business scenarios on production-like data. Automated baseline tests cover screens, access rules, data changes and generated outputs such as e-mails and exports.
AI assists engineers as they migrate manageable parts, preserving existing functionality. Engineers review each component. Where desktop-to-web migration is appropriate, users keep familiar screens and processes.
Automated comparisons of screens, access rules, data, e-mails and files — then human review. A parity report records matches, differences and unresolved exceptions. Engineers approve the result.
Architectural change remains an option: business logic held in stored procedures and triggers can move into a clearly structured application layer, with checks against the original behaviour and data.
Data migration and cutover
A successful migration must also protect data and day-to-day operations during the switch.
Optional shadow run: the new system processes the same operations alongside the original, with external effects isolated, and results are compared automatically in a measurable parity report.
Case study
A business web application for surveys and reporting, built on an end-of-life framework generation, moved to a current, supported platform. Same data, same screens, same behaviour — verified against the original.
The old application was generated with a scaffolding tool that is no longer maintained. Every layer sat on software past its support date.
| Area | Before | After |
|---|---|---|
| Language and runtime | Java 7 | Java 25 |
| Framework | Spring Roo 1.3, Spring 4.3 | Spring Boot 4.1 |
| Server | Tomcat 7 as a WAR file | Embedded server in a container image |
| Persistence | Hibernate 5.1 | Hibernate 7 |
| Security framework | Spring Security 3.2 | Spring Security 7 |
| Passwords | Reversible encryption, passwords sent by e-mail | Argon2id hashing, reset by mailed link, lockout, CSRF protection |
| Views | JSPX pages with Tiles, 317 files | Thymeleaf templates, 332 files |
| Front end | Old jQuery, DataTables 1.9, CKEditor and CKFinder, analytics scripts | jQuery 3.7, DataTables 1.13, a maintained editor, pinned assets, no tracking |
| Database changes | Manual SQL scripts in a folder | Flyway migrations, verified against the original schema |
| Generated code | 22k lines of generated aspects | Explicit, readable Java |
| Dependencies | 76 | 31, with vulnerability scanning |
| Delivery | No container, no pipeline, manual deployment | Docker images, automatic HTTPS, CI pipeline, backups with tested restore, metrics and a runbook |
Modernising a working system is mostly a question of proving nothing broke. The old and the new application ran side by side, and an automated harness compared them.
The original stayed live as the reference. Screens, API responses and access rules were compared automatically, including the differences in how each reacts to bad input.
Each business process ran on both systems from identical databases. The harness then diffed database rows, e-mails sent and generated Excel files.
Weak password storage, injectable query filters and over-exposed data were closed as part of the move, with checks that attack both versions.
Containers, scans, backups, monitoring, a runbook and a tester guide with anonymised data, so the team can try it on a clean machine.
Calendar time, from the first migration commit to a tagged release candidate.
The client is anonymised. The application is in team testing and has not yet replaced production; cutover follows testing. Timing is calendar time from commit history and includes waiting periods; line and file counts come from version control. No cost-saving percentage is claimed — timelines and savings are assessed for each application.
Confidentiality and feasibility
The AI-assisted process can run within your infrastructure using local models, without sending code or business data to external AI providers. We assess model suitability and security requirements for your application.
We confirm coverage for niche legacy technologies during assessment. You choose the target stack, including Java, .NET, modern JavaScript, Python, Go or Rust.
Share the source code and access to a working environment. We will assess scope, verification and effort — a clear scope, verification plan and migration path. Prefer email? [email protected].