Legacy Modernization Services

RaftLabs offers three main approaches depending on your risk tolerance and timeline. An API layer in front of the legacy system lets you add new features and integrations without touching the backend, and is the fastest first step. Incremental service extraction replaces one module at a time while the legacy system keeps running. A parallel rebuild replaces the full system on a separate track, migrating users in phases. We run a 2-4 week codebase audit first and tell you honestly which approach fits your situation.

We don't recommend big-bang rewrites. They fail more often than they succeed. When you rewrite a system in full, you spend a year discovering all the edge cases the original system handled, and you've delivered nothing in the meantime. We favour an incremental approach where we replace components one at a time, with the existing system still running until each replacement is proven in production. This reduces risk and keeps the business running.

We start with a code and architecture audit. We look at the codebase, the data model, the dependencies, the infrastructure, and the deployment process. We tell you honestly what's worth keeping, what needs replacing, and what the risks are. That assessment takes 2–4 weeks and produces a written report with a recommended modernisation roadmap. You decide whether to proceed. The audit cost is separate from the build.

We've worked with legacy systems built in PHP, Java, .NET (classic), ColdFusion, Classic ASP, Ruby on Rails, and older Node.js codebases. We've also modernised systems built on outdated frameworks, jQuery-era frontends, monolith Rails apps, and systems with no test coverage. The specific legacy technology matters less than the architecture, the biggest challenge is usually an undocumented data model and business logic buried in code.

A contained modernisation, for example, replacing a legacy frontend while keeping the backend, or extracting one module into a modern service, takes 12–20 weeks. A full platform rebuild on a parallel track typically takes 9–18 months, depending on scope. We break it into phases with clear milestones. You own the decision to proceed at each phase boundary.

The biggest risk is undiscovered business logic. Legacy systems accumulate requirements over years, many of which are never documented, they're just in the code. During a modernisation, it's common to find that the system does something important that nobody knew about until users started complaining after migration. We mitigate this with thorough audit, full test coverage of the legacy system before we touch it, and parallel running (new and old system live simultaneously during transition).

Sometimes. An API layer in front of the legacy system lets you expose data and functions to an AI layer without touching the legacy backend. This works when the data you need is accessible and the latency of the legacy system is acceptable. It breaks down when the AI use case needs to write back to the legacy system (most agentic use cases), when the data is locked in an undocumented schema you can't safely query, or when the legacy system's response time is too slow for real-time AI inference. We assess your specific AI use case during the audit and tell you honestly whether a tactical API layer is sufficient or whether the blocker is deeper in the architecture.