Platform Workflow Overview

Purpose: Explain how users move from project setup and source ingestion to AI-assisted generation, review, traceability, and export. Standard Workflow Pattern Create or select a project workspace with the correct industry profile. Upload relevant project documents such as item definitions, requirements, system descriptions, prior analyses, and evidence files. Ingest sources into the project-scoped knowledge base using parsing, chunking, embedding, and vector storage. Select the workflow to run, such as HARA, TARA, Safety Goals, or requirements extraction. Generate first-pass structured suggestions using AI and project-scoped retrieval context. Store outputs as AI Suggested artifacts rather than approved engineering records. Review reasoning, source references, field values, assumptions, and traceability links. Edit, reject, mark for review, or approve artifacts through the engineering lifecycle. Link downstream artifacts such as Safety Goals, requirements, controls, and evidence. Export structured tables and evidence packages for existing engineering and review processes. Functional Safety Workflow Example

Cybersecurity Workflow Example Define assets and system context. Upload cybersecurity-relevant documents and requirements. Generate or structure TARA artifacts such as threats, attack paths, risks, goals, controls, and response planning artifacts. Review AI-generated suggestions and source references. Approve valid items and link them downstream to requirements or controls. Export TARA and cybersecurity evidence views for partner review. Requirements and Evidence Workflow Example Upload requirements documents or source text. Extract candidate requirements. Structure HLR/LLR records. Link requirements to upstream safety or cybersecurity artifacts. Review and approve candidate requirements. Export requirements and evidence package structures.