How a Digital AI Colleague Speeds Up Complex System Migrations
Whenever legacy systems are replaced, many teams face the same challenge: knowledge is scattered, documentation is incomplete, and the impact on existing applications is difficult to assess. In our project, we developed a tool that brings together domain expertise, technical documentation, and code analysis to provide practical migration support.
Why We Need Centralized Knowledge for Software Development?
More and more companies are facing the challenge of moving large system landscapes into new target architectures. What may look like a purely technical migration on paper is, in reality, far more complex and deeply tied to business logic. Existing interfaces cannot simply be replaced, business concepts are redistributed across new domains, terminology is used inconsistently, and critical knowledge often resides with only a small number of experts.
This is where the real problem begins. Migrations rarely fail because of missing technology. They fail because of a lack of transparency and a missing shared target picture. Teams first need to understand which legacy systems are currently in use, which new endpoints will become relevant, and which business relationships must be taken into account. At the same time, day-to-day operations usually leave little room to clarify every question manually or in meetings and to make knowledge consistently available to everyone. The result is high research effort, rising costs, inconsistent decisions, and unnecessary delays.
We found that in situations like this, companies do not simply need another chatbot. They need a digital colleague that makes the right knowledge accessible, understands relationships, and provides concrete support for migration-related questions.
That is exactly why we developed our “X to Knowledge” approach: a solution that connects a structured knowledge base, technical documentation, and project-specific code analysis.
A Core Building Block: The Knowledge Base
Existing documentation, expert interviews, and specialist knowledge are prepared in a way that not only captures content, but also maps the relationships between terms, synonyms, migration paths, endpoints, and IDs. This allows the AI to understand not just isolated text passages, but also the business context behind them. That is especially important when the same concept is described differently across teams. In practice, these differences often lead to misunderstandings. With structured modeling, it becomes possible to provide consistent answers to the same topic, even when users phrase their questions differently.
The second building block is automated code analysis. Existing software projects are not just reviewed at a surface level, but examined in a structured way. Which interfaces are actually used? Where are database accesses taking place? Which external services are affected? From this, an analysis model of the specific project is created and linked to the migration knowledge. This turns abstract knowledge into practical support for real applications.
Building on this, multiple AI agents and a chat interface allow users to ask questions directly. General migration questions are answered based on the knowledge base, while project-specific requests also draw on the results of the code analysis. An orchestrator combines both perspectives and derives concrete recommendations. This means the system does not just explain concepts, but also helps teams reliably assess what actually needs to be changed in a given project or application.
A key aspect is that the solution does not remain a black box. Answers are delivered with specific source references, uncertainties are clearly indicated, and responsible contacts can be named when data is missing. In addition, functional tests help verify the quality of responses for defined use cases and feed improvements back into the knowledge base. This gives responsible stakeholders deliberate influence over the knowledge foundation. Responsibility therefore does not lie with the AI alone, but remains part of a clear human-in-the-loop process.
What Do We Actually Win?
The benefits for companies are clear: knowledge is centralized and standardized, and decisions can be made on a more reliable information base. At the same time, expert knowledge no longer remains locked in individual heads, but is preserved, documented, and made reusable for future projects over the long term.
The real outcome is therefore more than just a tool that answers questions. It is a solution that digitizes knowledge, derives sustainable recommendations for action, and enables development and business analysis teams to manage complex transformations in a more structured, faster, and more reliable way.