Executive Summary
S/4HANA transformations rarely fail because teams do not understand the Clean Core principle. They fail because legacy systems contain years of custom ABAP, Z tables, direct database reads, unreleased APIs, batch inputs, update tasks, Dynpro logic, RFC dependencies and business-critical exceptions that are difficult to classify.
Clean-Core.io helps teams turn this uncertainty into a structured modernization backlog. The platform analyzes legacy custom code, identifies technical and compliance risks, recommends a Clean-Core target pattern and generates reviewable implementation drafts.
Not automatic production migration — but a faster, more transparent path from unknown legacy custom code to architect-reviewed modernization decisions.
A prototyping, assessment and governance accelerator — fast enough for exploration, structured enough for governance, honest enough for enterprise review.
A replacement for enterprise-architecture approval, SAP release checks, privacy review, penetration testing or production migration governance.
What Clean-Core.io Produces
Clean-Core.io is an accelerator for governed modernization work. Code generation is valuable — but the decision evidence around it is what enterprise teams trust.
A modernization assessment before transformation starts.
A custom-code and integration-risk inventory.
A Clean-Core decision tree with recommended target architecture.
Draft implementations for RAP, CAP or integration patterns.
Test stubs and validation checklists.
BPMN / SOP documentation for business review.
A compliance and audit pack per project.
Modernization Assessment Before Transformation
Before any refactoring starts, the platform creates a project assessment report — a shared view of what exists, how risky it is, and which target architecture fits.
Programs, classes, function modules, includes, exits, enhancements, forms, tables and generated artifacts.
Direct SELECT / UPDATE / INSERT / DELETE usage, joins on SAP standard tables, Z-table dependencies and data-ownership risks.
Released APIs, unreleased APIs, private classes, obsolete function modules and compatibility concerns.
Dynpro, RFC, update task, batch input, BAPI, user exits, implicit enhancements and background jobs.
Size, coupling, number of dependencies, critical table access, testability and modernization effort.
Process area, user volume, frequency, financial impact, regulatory relevance and operational fallback.
Key User Extensibility, Developer Extensibility / RAP, Side-by-Side CAP, Integration Suite, Event Mesh or Retire.
Clean-Core Decision Tree
A decision tree explains why a recommendation was made — and which alternatives were rejected.
Compliance & Audit Pack Per Project
Enterprise customers need evidence. Every project export includes an audit pack documenting what the platform saw, what it decided, and what limitations remain.
Timestamp, uploaded file hash, project ID, user / tenant context and processing configuration.
Source patterns, matched rules, confidence levels, rejected alternatives and manual override notes.
Model / provider, prompt-template version, policy version, settings and generation timestamp.
Architecture, security, privacy, SAP release check, test evidence and owner sign-off.
Unsupported ABAP constructs, incomplete metadata, assumptions and low-confidence mappings.
Processing region, storage scope, credential handling, retention / deletion status and BYOK status.
Downloadable with the implementation ZIP — the bridge between prototype speed and enterprise governance.
Security & Data Handling
The security model is described as implemented controls, intended scope and review boundaries — not as unconditional guarantees. This wording fits enterprise procurement expectations.
Use non-production or representative code for the public pilot, unless a customer-specific agreement is in place.
Keep AI-provider keys, platform secrets and tenant credentials separated by responsibility and storage boundary.
Store only what the workflow requires, and define retention and deletion behavior clearly.
Document which data is sent to AI providers, which region processes it, and which contractual terms apply.
Make tenant connectivity read-scoped by design where possible, admin-approved and auditable.
Treat generated output as draft material that requires expert review before production use.
Reference Workflow
Upload a legacy ABAP package, representative extract or code sample.
Generate the modernization assessment and risk inventory.
Review findings by object, process area and risk category.
Use the Clean-Core decision tree to choose the target pattern.
Generate a draft implementation and test scaffold.
Validate APIs, tenant metadata and business assumptions.
Export the delivery package and compliance / audit pack.
Complete expert review before productive implementation.
Example Findings
Quality Engineering
Modernization is not complete when code compiles. The platform generates test and review evidence:
Unit-test stubs for generated RAP / CAP drafts.
API contract checks and schema validation.
Security review checklist for credentials, authorization and data flow.
Business SOP and BPMN export for process owners.
Clean-Core score and remediation backlog.
Manual-review gates for low-confidence mappings.
Start with a non-production sample
Use Clean-Core.io to create the first modernization assessment, review the decision tree with your SAP architect and export a governed delivery package for the next implementation step.
Clean-Core.io is a practical accelerator for expert teams: fast enough for exploration, structured enough for governance and honest enough for enterprise review.