Safe Space Labs AI — Custom Code Intelligence
Discover what your custom developments do, where they matter, what they depend on and which migration path deserves investigation before committing resources to remediation.
Safe Space Labs AI transforms custom-code evidence into a structured catalog that architecture, functional and engineering teams can use throughout an S/4HANA program.
A mature SAP environment may contain developments created across many years, projects and implementation partners.
Some remain business critical. Some duplicate functionality that has since become available elsewhere. Some have dependencies nobody wants to disturb. Some may no longer be used.
The first modernization decision should therefore not be: How quickly can we rewrite this?
It should be: What is this development doing, who depends on it, and what is the right future-state treatment?
Safe Space Labs AI Custom Code Intelligence is designed to organize the evidence needed to answer that question.
Capabilities
Discover
Normalize available information about custom SAP objects into a searchable catalog rather than leaving teams to work from disconnected spreadsheets and individual knowledge.
Output: Custom Development Catalog
Understand
A program name alone rarely tells an S/4HANA team enough. Custom Code Intelligence should help identify likely functional domains, related process steps, impacted applications, dependencies and relevant business context.
Output: Business Process Impact Map
Document
Where reliable source information is available, AI-assisted analysis can help create a first-draft explanation of custom development. Each generated object record should be capable of containing purpose, functional summary, technical summary, inputs, outputs, key logic, tables accessed, interfaces, dependencies, enhancement points, authorization considerations, batch dependencies, error handling, potential business owner, related processes and migration considerations.
Output: AI-Assisted Functional and Technical Documentation
Generated documentation should be validated against source code, system behavior and qualified business or technical review before being treated as authoritative.
Connect
Retirement decisions require more than understanding an object's primary purpose. The analysis model should support relationships between programs, transactions, tables, classes, function modules, interfaces, batch jobs, enhancements, APIs, business processes, downstream systems and upstream systems.
Output: Technical Dependency Map
Decide
The platform should support a structured assessment of future-state treatment using Safe Space Labs categories: RETIRE, STANDARDIZE, CONFIGURE, EXTEND, MODERNIZE and INVESTIGATE when evidence is insufficient for a defensible decision.
Output: Clean-Core Decision Record
Prioritize
Create an analytical model capable of considering usage frequency, business criticality, technical complexity, change frequency, dependency count, support effort, upgrade impact, security considerations, data sensitivity, availability of alternatives and migration complexity. When usage data is unavailable, clearly show: Usage data not provided.
Output: Custom Code Modernization Scorecard
Execute
Analysis becomes valuable when a program team can turn it into work. Allow conceptual grouping by retire, replace with standard, configuration change, in-app extension, developer extensibility, BTP side-by-side extension, integration redesign, ABAP modernization and further assessment activities.
Output: S/4HANA Custom Code Backlog
A searchable workspace containing one record per custom object — with functional and technical descriptions, business-process mapping, dependencies, usage evidence, clean-core assessment and linked project decisions.
Interface
Example data only
Additional evidence is required to determine whether the future S/4HANA architecture eliminates, replaces or modernizes the requirement.
Import approved source-code metadata, extracts or other available technical evidence.
Generate classifications, summaries, relationships and candidate business-process mappings.
Combine technical evidence, usage information and functional context to assess modernization paths.
SAP architects, developers and process owners review AI-generated findings.
Convert approved conclusions into the S/4HANA modernization backlog.
Maintain validated findings as reusable transformation knowledge.
Approved source-code exports and associated metadata are securely provided for analysis.
An authorized extraction mechanism collects approved technical information using controlled access.
| From | To | Type |
|---|---|---|
| ZSSL_VENDOR_SYNC | LFA1 | reads |
| ZSSL_VENDOR_SYNC | ZSSL_VENDOR_OUT | calls |
| ZSSL_VENDOR_SYNC | Procure-to-Pay | supports |
| ZSSL_ORDER_REVIEW | VBAK | reads |
| ZSSL_FIN_RECON | BKPF | reads |
AI-generated analysis, specifications and code are decision-support materials and should be reviewed by appropriately qualified SAP, security, architecture and engineering professionals before implementation.
See how Safe Space Labs AI can organize legacy custom-development evidence into a reviewable modernization backlog for your architecture and engineering teams.
