An Evidence Bounded Workflow Architecture for Integrated Goal, Study, and Venture Execution in ForgeanOS: A Context-Aware Multi-Module AI Platform for Academic and Career Support

Abstract

Fragmented goal, academic, career, and venture tools can leave planned work disconnected from recorded outcomes. This paper presents a source-based case study of ForgeanOS, a Next.js application that links goal structures to study and founder execution while separating deterministic recommendations from optional generative AI. The architecture uses authenticated route handlers, owner-scoped PostgreSQL records, transactional mutations, version checks, and metered provider routing. A repository guide reports 621 passing tests and a local production build on 27 September 2026, plus disposable-account FounderPilot checks; the underlying test logs and deployed runtime were not supplied for independent replication. No user study, comparative benchmark, or production performance measurement was available. The contribution is an explicit workflow and evidence model, with its implementation boundaries and validation gaps stated rather than inferred benefits.