An Interpretable AI Architecture for System-Level Reasoning from Engineering Documentation

Abstract

Engineering systems are increasingly characterized by large, heterogeneous collections of technical documentation, including specifications, interface descriptions, and contribution records. While artificial intelligence techniques have been applied to document analysis, many existing approaches rely on opaque models that limit transparency and human trust. This paper presents a structured AI-based approach for deriving systemlevel understanding from engineering documentation by combining semantic abstraction, modular reasoning, confidence-aware outputs, and analyst validation. The approach emphasizes transparency and evidence-linked reasoning, enabling users to inspect intermediate representations and validate inferred relationships. A case study using a large-scale wireless systems documentation corpus and a focused Wi-Fi Aware worked example demonstrates how source-anchored reasoning can scale across extensive document sets while preserving human oversight.