Take Over Legacy Systems. Understand Them. Make Them Future-Ready.

For us, a legacy system is not necessarily an old system — it is any system where changes take too long, seem too risky or only work through workarounds. This can be custom software that has grown over many years, a business-critical Excel setup or an application without up-to-date documentation. What matters is not the age of the system, but whether it still supports your business or has already started to slow it down.

Not every legacy system has to be replaced. In many cases, the better first step is to take over and stabilise ongoing operations — without big-bang risk. Only on that stable foundation do we decide together whether gradual modernisation, targeted reengineering of individual modules or, in exceptional cases, a new development project makes sense.

This is exactly our core use case, not an exclusion criterion. We use AI-supported analysis tools to automatically explore codebases, dependencies and data flows, making business logic visible that may no longer be documented anywhere. The goal is transparency instead of a black box — before we change even a single line of code.

Reengineering means modernising existing applications in a targeted way instead of replacing them blindly: monoliths are decoupled step by step, interfaces are made API-capable and individual components are improved — while operations continue. A greenfield rebuild, by contrast, is often more expensive, slower and riskier because existing business logic is reinvented instead of reused. We recommend reengineering whenever the functional substance of the existing system is still sound.

That mainly depends on two factors: available know-how, meaning whether there are still people who know the system, and how up to date the documentation is. With AI-supported analysis, onboarding times can be reduced significantly today. In any case, ongoing operations remain stable throughout the takeover — without downtime and without unclear responsibilities.

We work in a controlled and gradual way rather than through risky big-bang projects: parallel operation, step-by-step handover of critical processes and a stabilisation phase before any major change. This gives you room for well-considered decisions instead of having to act under time pressure.

There is no general answer, as it depends heavily on the scope of the system. For an initial, low-risk entry point, we therefore offer paid short projects with a clearly defined deliverable — for example an AI-supported legacy code analysis with a risk report or a proof of concept for AI reengineering of a single module. This gives you a reliable basis before deciding on a larger project.

At its core, MCP is a standardised bridge between AI agents and existing systems: an AI agent uses an MCP server to query specific data, statuses or functions of a system instead of collecting information manually. For legacy systems, this means that before an AI agent can meaningfully “talk” to an evolved application, the system needs a clean, documented interface to the outside. This is exactly the foundation we create with our reengineering approach — for example through API-capable interfaces for previously isolated systems — so that your legacy system can later connect to MCP-based AI agents.

In principle, yes — provided the underlying system is understood and can be accessed through defined interfaces. MCP itself does not replace reverse engineering: an AI agent can only interact with your legacy system as well as its data flows, business logic and interfaces have previously been understood and documented. That is why we generally start with understanding rather than integration: only once the structure and dependencies of a system have been made visible through AI-supported analysis can an MCP-capable connection be implemented in a meaningful way and without risk to ongoing operations.