(untitled) (Community Engagement and Emergence Journal (CEEJ), 2026)
Official correspondence in professional associations often relies on copied files and separate approval records, producing inconsistent structure and weak traceability. General-purpose generative artificial intelligence can accelerate drafting but may fabricate factual content and vary mandatory document elements. This study designs and functionally evaluates the Smart Correspondence Assistant (SCA), a web-based artifact that uses PermenPANRB 21/2021 as a structural reference while separating deterministic document assembly from model-generated narrative. Following design science research, seven design requirements were derived from practitioner experience, organisational letter examples, and the reference standard. The artifact supports seven letter types, configurable multi-level approval, deterministic numbering, role-based access control, audit logging, and PDF generation. Functional evaluation comprised fifty-four scenarios across six iterative runs in an isolated PHP 7.4.33 environment. Eight defects were identified and all were remediated and retested. In the final cumulative state, all fifty-four scenarios passed: forty-six were decided through objective functional checks and eight semantic or visual scenarios were verified manually by the first author using preserved evidence packages. Because the manual reviewer was part of the development team, the result represents developer-executed functional conformance rather than independent validation. The study contributes a transferable pattern for constraining generative artificial intelligence in official correspondence by keeping structural and authority controls at the application layer, while treating generated narrative as reviewable content rather than a final document.