Automated Document Classification System for Construction Project Management

The client faces significant difficulties in organizing and classifying tens of thousands of construction-related documents, including various formats such as Microsoft Office files, PDFs, images, and AutoCAD drawings. Manual classification is time-consuming and prone to errors, leading to workflow disruptions and inefficiencies across their cloud-based platform used globally across regions like the UK, Ireland, Australia, Qatar, and the UAE.

A large-scale construction firm responsible for managing extensive project documentation across multiple regions, aiming to automate and streamline their document management processes.

• Develop an AI-powered document classification system capable of accurately categorizing a large dataset of approximately 45,000 documents.
• Achieve at least 96% accuracy at the document level and 98% at the label level.
• Automate the process of inputting document metadata into the management system to reduce manual effort and errors.
• Ensure the solution supports various document formats including images, PDFs, Office files, and engineering drawings.
• Deploy the system securely in a cloud environment compliant with GDPR standards to safeguard client data.
• Improve overall workflow efficiency, reduce system malfunctions caused by manual data entry, and provide scalable support for extensive document management.

• Comprehensive dataset analysis capability to understand different document types and label assignments.
• Integration of OCR technology for extracting text from images and scanned documents.
• Text vectorization and natural language processing to interpret document content.
• Development of a machine learning model ensemble to enhance classification precision.
• Creation of a RESTful API for real-time document classification and metadata integration.
• Deployment on a secure cloud platform (e.g., AWS) ensuring GDPR compliance and data security.

• Construction project management systems or document repositories
• Authentication and security services for GDPR compliance
• Existing digital workflow tools for seamless data exchange

• System must handle large datasets of approximately 45,000 documents with scalable architecture.
• Achieve classification accuracy of at least 96% at document level and 98% at label level.
• Ensure secure data handling to meet GDPR and client confidentiality standards.
• Design for high availability and reliability for continuous operation across multiple regions.

Implementing the AI-driven document classification system is expected to automate manual document management processes, significantly reducing input errors and workflow disruptions. The solution aims to support extensive document types, improve classification accuracy to over 96%, and streamline operations, leading to enhanced efficiency, reduced operational costs, and scalable document support capable of accommodating growing data volumes.