Raiffeisen Bank Is Building a Digital Twin of Its IT Landscape and Preparing Agentic RAG

Raiffeisen Bank Builds Digital Twin of IT Landscape and Prepares Agentic RAG

Raiffeisen Bank described why it decided to abandon the traditional corporate architecture model with separate diagrams and manual repository maintenance. Instead, the bank is building its own EA Tool — a platform designed to become a digital twin of the IT landscape and a working foundation for future AI tools.

Why Old EA Systems Don't Work

According to the bank's team, classical solutions like Sparx EA, Alfabet, and iServer long remained industry standards, but today they increasingly struggle to meet speed requirements. The main problem lies not in the feature set, but in the very logic of their operation: such products were created as tools for architects, not as a living environment for team collaboration. As a result, data becomes outdated quickly, relationships between objects are maintained manually, and discussions about changes often end up in draw.

io and presentations anyway. Against this backdrop, the business demands a different mode: faster deployment of changes to production, more precise product personalization, and reduced manual operational support. For this, the bank needs a single source of truth for IT assets that doesn't just store schemas, but is updated alongside actual development.

This is why Raiffeisen decided not to limit itself to replacing one vendor tool with another, but instead launched its own architecture management platform.

How EA Tool Works

The new EA Tool is built around a unified repository of architectural objects. At the bank, they describe it as an auto-populated digital twin: each object in the system has a history, relationships, and a place in the overall model. Moreover, the target audience of the platform is not just architects, but autonomous agile teams that should work with architectural data directly, rather than through a separate layer of approvals and manual exports.

• The architectural model links business capabilities, functions, APIs, data flows, and components
• Agreed-upon models can trigger CI/CD processes through a standardized Green Path
• YAML specifications and data from Git help reconcile the actual landscape with the architectural solution
• In the future, the platform will be able to generate stubs, database schemas, and basic code

This approach brings the bank closer to Spec-Driven Development, where an architectural artifact stops being a picture for approval and becomes an executable specification. The team admits that initially, the speed of solution preparation declined: they had to strictly standardize notations, mandatory fields, and input data quality. But after adopting these rules, some routine work in the final stages disappeared, and the registration of systems and data flows after approval became automatic.

Where SDD and RAG Lead

The next stage involves not visualization, but the use of AI on top of the architectural repository. First, the bank wants to focus on contextual search, NLP, and RAG scenarios. The idea is simple: any team member will be able to ask a question in natural language and quickly find out which systems handle, for example, card processing, or where legacy databases are still being used. This should significantly lower the barrier to entry for architectural knowledge and eliminate dependence on a narrow circle of experts.

"The future is already here.

It's just unevenly distributed."

Next, Raiffeisen is looking toward Agentic RAG, where AI agents not only retrieve knowledge from the database but also take action within defined rules. The article discusses this as a development vector: such agents could verify architectural solutions against security policies, find duplicate business functions, and suggest optimal integration routes based on the bank's historical data. The architect doesn't disappear in this scenario but shifts into a role of one who formalizes knowledge, sets frameworks, and validates AI drafts at the level of strategy and decisions.

What This Means

Raiffeisen Bank's case shows that corporate architecture is gradually transforming from supporting documentation into an operational layer of development. If this model takes hold, value will be created not by the diagrams themselves, but by a combination of structured internal data, production processes, and AI tools built on top of them. For the market, this is another signal: the most useful corporate AI may not emerge in a standalone chatbot, but within a company's digital twin.