Predictive power of Neural Networks

 

AIMLUX.ai Automation engineering; Combines the SAP Technical Architect, the Equitus.ai Fusion (KGNN - Knowledge Graph Neural Network) with Intent platform acts as a powerful "intelligent layer" that sits above the technical hurdles of a RISE with SAP transition.

While the SAP Custom Code Migration App identifies what is broken, Equitus Fusion helps architects understand the why and how to refactor efficiently, particularly in complex, heterogeneous landscapes.

How KGNN Improves the RISE Process

The Fusion platform combines the structural logic of Knowledge Graphs (mapping relationships) with the predictive power of Neural Networks (identifying patterns). This improves the four pillars you mentioned:

• Accelerated Code Modernization: Instead of just flagging an obsolete Function Module, KGNN can map the entire semantic dependency of that code. It identifies every Z-report, interface, and external system that relies on that specific logic. This allows architects to design CDS views and OData services that aren't just one-to-one replacements but are optimized for how the business actually uses the data.

• Decoupling for "Clean Core": KGNN excels at data unification without movement. For a Clean Core strategy, it helps identify which customizations can be entirely moved to SAP BTP as "side-by-side" extensions. It provides the "semantic glue" to ensure that custom logic running in CAP or RAP still has full contextual awareness of the S/4HANA core data without creating "spaghetti" integrations.

• Transitioning to a Cloud Mindset: A major hurdle in cloud migration is moving from file-based I/O to APIs. KGNN automates the discovery of these "hidden" integration points. By creating a digital twin of your data ecosystem, it helps architects visualize the stateless flow required for BTP’s Integration Suite, reducing the risk of broken pipelines during the cutover.

• Overcoming the "Learning Curve": KGNN provides built-in explainability and traceability. For teams new to RAP or AMDP, the platform can serve as a knowledge repository that translates legacy business rules into modern architectural patterns, effectively acting as an "AI Co-pilot" for the architectural design phase.

Challenge		How Equitus Fusion (KGNN) Mitigates It
Vendor Lock-In		By creating a semantic layer independent of the underlying database, it makes data more portable and less dependent on specific hyperscaler features.
Customization Limits		It identifies "dead code" vs. "critical logic" more accurately than standard tools, helping teams decide what to standardize and what to rebuild on BTP.
Performance		It optimizes heavy calculations by identifying the most efficient data paths, supporting the architect’s goal of leveraging HANA’s in-memory power via AMDP.