eXplain 10

Why AI for Legacy Systems Needs a Reliable Factual Foundation

Language models can explain code and describe relationships. But LLM-only approaches fall short when applied to large, complex application systems that have evolved over many years. Individual code fragments do not provide reliable system-wide context, outputs are non-deterministic, and large volumes of unstructured source code increase token consumption and processing overhead.

eXplain therefore provides the LLM with the relevant facts. Parsers and preprocessors identify structures, references, and dependencies. The LLM handles interpretation, documentation, and natural-language queries.

What eXplain 10 Provides for AI Applications: New in eXplain 10 is the export of Abstract Syntax Trees (ASTs) in JSON format. This makes the identified program structures available to external AI tools as a consistent, machine-readable model. RAG or the Model Context Protocol (MCP) can act as an interface between eXplain and the LLM of your choice. The factual foundation remains independent of the model.

What You’ll Learn in the Brochure: The brochure covers the architecture, use cases, and integration scenarios of eXplain 10. It explains how parsers, preprocessors, and the repository work together and the role AST export plays.

Real-world examples show how eXplain is used in complex legacy environments and how organizations can get started with AI-assisted modernization.

Learn how to systematically unlock the knowledge contained in your legacy systems and use it for modernization and AI applications.