Aimlux.ai Consulting Services is offering Enterprise migration services on Equitus.AI KGNN enables transforms the 2025 SAP transition from a high-risk manual overhaul into an automated, predictable technical conversion.
For a standard 2TB–5TB SAP instance, the efficiency gains are centered on the platform's ability to "understand" the semantic relationships within Oracle data and map them autonomously to SAP HANA or IBM DB2.
Per-Database Efficiency & Savings (2TB–5TB Instance)
|
Migration Phase |
Traditional Manual Effort
(Estimated Hours) |
Equitus KGNN Effort
(Automated/AI-Led) |
Efficiency Gain (%) |
Cost Displacement Note |
|
Data Discovery & Profiling |
160 - 240 hrs |
12 - 20 hrs |
92% |
Eliminates manual profiling; KGNN builds the graph
instantly. |
|
Schema Mapping (Oracle to HANA) |
320 - 450 hrs |
40 - 60 hrs |
87% |
Auto-generation of semantic mappings reduces architect
hours. |
|
Custom Code Remediation |
600 - 800 hrs |
180 - 240 hrs |
70% |
Neural network identifies logic patterns and suggests
fixes. |
|
ETL & Pipeline Setup |
200 - 300 hrs |
0 hrs (Zero-ETL) |
100% |
KGNN's "Zero-ETL" approach connects data
without staging. |
|
Validation & Reconciliation |
120 - 180 hrs |
24 - 36 hrs |
80% |
Automated semantic integrity checks ensure data parity. |
|
TOTAL |
1,400 - 1,970 hrs |
256 - 356 hrs |
~82% |
Saves ~$180k-$250k in labor per
instance. |
Why the First 12 Months? (The ROI Drivers)
The 2025 deadline creates a "bottleneck" where specialized SAP/Oracle talent will be at a premium. Equitus KGNN achieves a comprehensive ROI within the first year by:
Removing Talent Dependencies: You no longer need a massive team of high-cost consultants for manual mapping.
Infrastructure Optimization: By running natively on IBM Power10, KGNN utilizes high-bandwidth memory to process these 5TB datasets without the latency of traditional x86 clusters.
Risk Mitigation: By automating Custom Code Remediation (the #1 cause of SAP migration delays), you avoid the "hidden" 13th and 14th months of project overrun.
Strategic Transition to IBM RISE
Integrating Equitus with IBM RISE for Cloud ensures that once the data is migrated, it remains in a "Clean Core" state. The KGNN acts as a permanent semantic layer, allowing future AI applications on IBM Cloud to access SAP data without re-building complex integrations.
Would you like me to expand the "Custom Code Remediation" section into a step-by-step breakdown of how the KGNN identifies legacy Oracle PL/SQL for conversion?
Traditional migration, Custom Code Remediation is the single greatest bottleneck, often consuming 40% to 60% of the total project timeline. When moving from Oracle to SAP HANA or IBM DB2, legacy PL/SQL often contains complex logic, triggers, and proprietary extensions that don't translate 1:1.
Equitus KGNN automates this by treating code as a connected graph of intent rather than just lines of text. Here is the step-by-step breakdown:
Step-by-Step Custom Code Remediation via KGNN
1. Semantic Parsing & Node Extraction
Instead of a simple "find and replace," the KGNN ingests the Oracle PL/SQL codebase and deconstructs it into nodes.
Logic Nodes: Functions, procedures, and calculations.
Dependency Edges: How a specific trigger in Oracle affects a table that SAP HANA needs to access.
The Result: A visual "Code Map" that shows exactly which pieces of custom logic are critical and which are obsolete.
2. Pattern Matching against the "Target Ontology"
The KGNN compares the extracted Oracle patterns against a pre-built library of SAP HANA (SQLScript) and IBM DB2 (SQL PL) best practices.
Contextual Translation: It identifies if a proprietary Oracle hint (e.g.,
/*+ INDEX(...) */) has a semantic equivalent in the target database or if the target’s optimizer handles it natively.Optimization Identification: The AI recognizes "Row-based" logic in Oracle that should be converted to "Columnar-optimized" logic in HANA to take advantage of in-memory performance.
3. Impact Propagation Analysis
One change in a stored procedure can break five connected applications. The KGNN performs Change Impact Analysis:
It predicts the "downstream" effects of modifying a specific piece of custom code.
It flags "High-Centrality" code—logic that is touched by multiple business processes—requiring human-in-the-loop validation, while automating the "Leaf" nodes (isolated logic).
4. Automated "Clean Core" Synthesis
To align with SAP's Clean Core strategy (especially for RISE with SAP), the KGNN identifies custom code that can be replaced by Standard SAP Functionality.
It maps custom Oracle-side calculations to standard HANA Calculation Views.
This prevents "technical debt carry-over," ensuring the new environment is leaner than the legacy one.
5. Iterative Verification & Explainability
Unlike standard AI, the KGNN provides an audit trail. For every line of code converted:
Provenance: It shows the original Oracle source.
Reasoning: It explains why the specific target syntax was chosen.
Unit Test Generation: It automatically suggests test parameters based on the data relationships discovered in the analysis phase.
Efficiency Comparison: Custom Code Remediation: AIMLUX.AI FUSION TIMETABLE ACCELERATION
|
Task |
Traditional Manual Method |
Equitus KGNN |
|
Dead Code Detection |
Manual Audit (Weeks) |
Automated (Minutes) |
|
Syntax Conversion |
Regex/Manual Rewriting |
Semantic Transformation |
|
Dependency Mapping |
Documentation/Guesswork |
Real-time Graph Visuals |
|
Logic Validation |
Trial and Error |
Predicted Impact Analysis |
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