AI for document transformation

1. Analyze legacy sources and business rules

Use AI to extract variables, conditions, formatting rules and inconsistencies from legacy documents, specifications and source programs.

• Legacy document analysis
• Rule and variable extraction
• Gap identification
• Initial documentation generation

2. Extract and structure CSF data

Use AI to assist the analysis and extraction of data from CSF in the mainframe environment: identify structures, variables and business rules, then prepare mappings to target formats, particularly XML, under human validation.

• CSF source analysis in the mainframe environment
• Structure and variable identification
• Business-rule extraction
• Preparation of XML mappings

3. Assist data mapping

Propose mappings between source data, target XML structures and document components while keeping review and approval under human control.

• Source-to-target mapping proposals
• XML and schema alignment
• Unmapped data detection
• Traceable validation workflow

4. Accelerate document design

Generate an initial design dossier, structure reusable components and maintain documentation as implementation decisions evolve.

• Design dossier drafting
• Reusable component identification
• Design consistency checks
• Documentation updates during development

5. Support development teams

Provide contextual assistance for naming, component reuse, transformation rules and technical documentation without replacing expert implementation decisions.

• Development copilot use cases
• Reusable pattern suggestions
• Rule consistency checks
• Knowledge retrieval

6. Strengthen tests and comparison

Generate test scenarios, compare legacy and target outputs, classify differences and prioritize anomalies for human review.

• Test case generation
• PDF and data comparison
• Difference classification
• Regression analysis and reporting

Frequently asked questions

Does document AI replace business analysts or developers?

No. The objective is to accelerate analysis, preparation, documentation and testing while keeping expert validation and accountability.

Where should a document AI pilot start?

A focused pilot should target a measurable pain point such as rule extraction, mapping assistance, test generation or output comparison.

How is risk controlled?

Through approved data access, traceability, human validation, quality thresholds, restricted use cases and clear governance rules.