AI-Driven Intelligent Design and Business Intelligence System for Automated Technical Drawings in the Roofing Industry

The roofing industry faces increasing demands to meet evolving legislative requirements and market expectations for efficiency, scalability, and sustainability. Addressing these challenges requires innovative solutions that blend advanced automation with predictive business intelligence. This research introduces the TaperedPlus Intelligent Design System (TPID), an AI-driven framework that leverages cutting-edge Deep Learning, Natural Language Processing (NLP), and Large Language Models (LLMs) to revolutionise technical drawing processes. The proposed system automates the extraction of critical design parameters from diverse unstructured data sources, such as customer emails and technical documentation, while integrating predictive analytics to enhance decision-making. At the core of TPID is a novel methodology that applies transformer-based architectures for precise semantic under- standing and multi-modal learning to handle both textual and visual data. This enables automated parameter extraction, error detection, and intelligent adaptation of technical drawings in real-time, significantly reducing manual intervention and improving accuracy. A proactive business intelligence component further enhances the framework by utilising predictive analytics to forecast customer behavior and identify market trends, enabling strategic decision-making. Preliminary results demonstrate that TPID achieves over a 30% reduction in manual design time, with the potential to scale its impact across the construction industry. Beyond its practical applications, this research contributes to the AI field by introducing domain-specific adaptations of LLMs and advancements in multi-modal learning for technical design automation. This work out- lines the development of the TPID framework, its initial results, and its future potential to drive innovation, sustainability, and growth within the roofing and construction sectors.