Application Modernisation Guide
A guide for technology leaders deciding what to do with ageing applications — how to assess them, choose the right modernisation approach for each, and migrate without disrupting the business.
What you'll take away.
- How to assess an application portfolio for modernisation
- The main modernisation approaches and when each fits
- How AI can speed up code analysis and migration
- Migration patterns that keep the business running
- How to build a phased, low-risk roadmap
The guide in brief.
Start with an honest assessment
Look at each application's business value, technical health, cost to run and risk of change. The goal is to decide where modernisation pays off — some systems should be retired or left alone.
Choosing an approach
Options range from rehosting as-is, through re-platforming and refactoring, to rebuilding or replacing. The right choice depends on how much the business logic is worth keeping and how much the architecture is holding you back.
Understanding the existing code
Undocumented business logic is the biggest risk in most modernisation projects. AI-assisted code analysis can map dependencies and explain what older code does, which shortens discovery and reduces surprises.
Migrating incrementally
Replace a system piece by piece behind stable interfaces rather than in a single cutover. Each step can be tested and rolled back, and users keep working throughout.
Testing as a safety net
Before changing anything, capture how the current system behaves with automated regression tests. They are what lets you modernise with confidence that nothing important has broken.
Building the roadmap
Sequence the work so early phases deliver visible value and reduce the riskiest dependencies first. Revisit the plan after each phase as you learn more about the system.
Where we can help.
Application Modernisation
Assess legacy applications, modernise their architecture and migrate them in stages — without a big-bang rewrite.
AI Code Modernisation
Code analysis, impact assessment, refactoring and technical debt management — grounded in an AI-indexed model of your real codebase.