Document AI
AI systems that extract, understand, and organize information from documents.
Definition
Document AI encompasses technologies that automatically parse, extract structured data from, and understand unstructured documents. For AEC firms, this includes processing complex PDFs like specifications, submittals, contracts, and reports to extract key information, organize it into searchable databases, and make it accessible for AI-powered workflows. Document AI handles the unique challenges of AEC documents including complex formatting, tables, technical drawings embedded in PDFs, and domain-specific terminology.
In Depth
Every AEC project produces thousands of documents — specifications, submittals, RFIs, shop drawings, daily reports, meeting minutes, contracts. Most of this information is trapped in PDFs where it cannot be searched, compared, or analyzed at scale. Document AI is the layer that makes all of it machine-readable and queryable.
The technical challenge is that AEC documents are not simple text. A specification has a rigid structure (CSI divisions, sections, parts) that conveys meaning through its hierarchy. A submittal package contains product data sheets with tables of performance values. A drawing sheet has a title block, dimensions, keynotes, and graphic symbols that all carry information. Document AI for AEC needs to understand all of these formats and preserve the structure, not just extract raw text.
In practice, Document AI powers everything else. RAG systems need it to retrieve accurate information. Compliance checking needs it to parse code requirements. Submittal review needs it to extract product data. When a firm connects their document management system to an AI platform, Document AI is what transforms a folder full of PDFs into a searchable knowledge base that the entire team can query in seconds.
Examples
Extracting structured data from product submittal PDFs
Converting specification documents into searchable, queryable databases
Automatically categorizing and tagging uploaded project documents
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Compatible Platforms
Nomic integrates with these platforms so you can use document ai across your existing project data:
Frequently Asked Questions
Document AI encompasses technologies that automatically parse, extract structured data from, and understand unstructured documents. For AEC firms, this includes processing complex PDFs like specifications, submittals, contracts, and reports to extract key information, organize it into searchable databases, and make it accessible for AI-powered workflows. Document AI handles the unique challenges of AEC documents including complex formatting, tables, technical drawings embedded in PDFs, and domain-specific terminology.
Extracting structured data from product submittal PDFs. Converting specification documents into searchable, queryable databases. Automatically categorizing and tagging uploaded project documents.
Automated Submittal Review: Let AI do the first-pass review of submittal packages against your drawings and specs. Project Research: Instantly access all project-critical information from a single search interface.