\Augmented Ontological-Semantic Platform 

Step 1: Initial Configuration and Data Input

The first phase of the process is driven by direct user input. Appropriate organizational representatives (business owners, C-level executives, senior managers, directors) populate a detailed compliance-oriented self-assessment questionnaire. This data input stage covers corporate structures, internal policies, key processes, risk scenarios, existing control mechanisms, and historical compliance events.

• Frontend Technology: React SPA interface (SetupPage, Questionnaire modules).

• Backend Support: FastAPI API endpoints, PostgreSQL database for session and configuration management.
  
  

Step 2: Tokenization and Fragmentation

Once data is submitted, A-OSP processes the responses through a custom tokenization engine developed in Python. The platform converts complex textual and numerical inputs into small, semantically meaningful fragments (called "231chunks"). This fragmentation process ensures both accuracy of AI analysis and complete anonymization, as these fragments lose any explicit corporate identification before leaving the local environment.

• Technology: Python-based tokenization modules, semantic decomposition algorithms.

• Output: JSON-based semantic fragments, fully anonymized and de-identified.
  
  

Step 3: Semantic Analysis via AI and Prompt Engineering

These semantic fragments ("231chunks") are sent securely via API to external AI services, typically enterprise-level Large Language Models (LLMs) such as OpenAI’s GPT models. A-OSP employs carefully engineered prompts tailored specifically for regulatory compliance tasks. These prompts guide the LLMs to produce contextually accurate, logically coherent analyses addressing compliance criteria, risk identification, control evaluations, and suggested mitigations.

Technology & Integrations:

• • Python-based LLM connectors (OpenAI API, Azure OpenAI endpoints).

  • Advanced Prompt Engineering: YAML-configured prompt templates for specific compliance scenarios.

  • Enterprise-grade API subscriptions ensuring strict confidentiality and prohibiting reuse of transmitted data for external machine learning processes.
    
    

Step 4: Local Recomposition and Synthesis

Responses obtained from the AI providers, which are contextually detailed yet initially anonymized and fragmented, are recomposed and correlated back into meaningful compliance documents exclusively within the local A-OSP environment. Semantic recomposition occurs through internal logical associations defined by the platform's proprietary knowledge base (ISO 31000, ISO 37001, ISO 27001, COSO, ANAC, DOJ standards).

• Technology: Python-based semantic correlation engine, GitPython for document versioning.

• Output: Intermediate semantic artifacts, detailed audit logs, epistemically traceable documents.
  
  

Step 5: Final Compliance Artifact Generation

Once the semantic recomposition is complete, A-OSP systematically generates final compliance documents tailored precisely to the company's specific profile and regulatory requirements. The resulting documents include, but are not limited to:

• Organization, Management and Control Model (Modello 231/01): detailing internal processes, associated criminal risks, preventive measures, and governance mechanisms.

• Risk Assessment: identifying and classifying potential compliance risks according to ISO and COSO frameworks.

• Gap Analysis: systematically comparing the current corporate compliance posture ("as-is") against desired regulatory standards ("to-be").

• Technology: Document automation via custom Docx Template Engine (Python-docx), dynamic meta-prompt generation for automated and precise document filling.
  
  

Step 6: Audit Trail and Epistemic Traceability

Every stage of the compliance analysis, from initial user inputs through to final document production, is carefully logged and version-controlled, ensuring complete auditability. Users retain detailed access to trace every decision, inference, and semantic transformation that contributed to each document’s generation.

Technology:

• • GitPython for artifact and knowledge-base versioning.

  • SQL database for session management and detailed logging.

  • WebSocket-based real-time updates for status monitoring.