Legacy System Modernisation for CTOs and IT Directors

Data integrity is non-negotiable. We run parallel systems during migration with continuous validation checks. Before each cutover, we perform full reconciliation between the old and new systems. We also maintain rollback capability for a defined period post-go-live. Every migration plan includes a detailed data mapping exercise, automated testing of transformations, and sign-off from your team at each stage.

Yes. Most modernisation happens incrementally. We can extract services one at a time, run them in parallel with the monolith, and switch traffic gradually. Your business does not pause while we refactor. This staged approach also gives you chances to validate that the new architecture works before moving the next service.

We embed directly into your engineering org. Our engineers join your daily standups, code reviews, and planning sessions. They report to you or your technical leads, not separately to Evolved Ideas. We document decisions and knowledge as we go so handover is smooth if your team later takes full ownership of the modernised system.

We adjust the plan. Discovery issues are normal in legacy work because the original documentation is often incomplete or outdated. We flag risks early through code reviews and spike work before committing to full migration. Your team always has the option to change scope or timeline based on what we uncover.

Knowledge transfer starts on day one. Our engineers pair with your team, review code together, and maintain a living architecture document. When we finish the engagement, your team should be able to run, operate, and extend the modernised system without us. We also provide a handover period where we step back gradually rather than disappearing abruptly.

It depends on system size, complexity, and how many services you extract per phase. A phased approach typically runs 6-18 months. We work to your business calendar, not a preset methodology. We break the work into milestones so you can release value (cost savings, performance, or feature velocity) at each stage rather than waiting for a big-bang cutover.