Multimodal AI
AI that processes and understands multiple types of data like text, images, and drawings simultaneously.
Definition
Multimodal AI refers to artificial intelligence systems that can process, understand, and connect information across different data modalities—including text documents, images, CAD drawings, PDFs, and tables. In the AEC context, this means AI that can simultaneously analyze a drawing (visual) and its associated specification (text) to understand the complete design intent, enabling more sophisticated analysis and search capabilities than text-only or image-only AI systems.
In Depth
Multimodal AI processes multiple types of information simultaneously — text, images, drawings, tables, and structured data. In AEC, this is essential because project information is never just text. A single answer might require reading a specification paragraph, looking at a drawing detail, and cross-referencing a product data table.
Consider a submittal review. The submitted product data sheet contains a combination of marketing text, technical specifications in a table format, test report references, and product photographs. The project specification contains text requirements organized in CSI format. The AI needs to parse both document types — extracting tabular data from one and structured requirements from the other — then compare them to identify compliance or discrepancies. A text-only AI cannot do this.
Drawing analysis is inherently multimodal. A floor plan communicates through spatial relationships (room adjacencies), graphic conventions (wall types, door swings), text annotations (room names, keynotes), and dimensional information (room sizes, corridor widths). Understanding a drawing requires processing all of these modalities together, just as a human reviewer does when they scan a sheet.
Examples
Searching for details by uploading a photo or sketch
Understanding both the visual elements of a drawing and the text annotations
Connecting specification requirements to relevant drawing details
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
Multimodal AI refers to artificial intelligence systems that can process, understand, and connect information across different data modalities—including text documents, images, CAD drawings, PDFs, and tables. In the AEC context, this means AI that can simultaneously analyze a drawing (visual) and its associated specification (text) to understand the complete design intent, enabling more sophisticated analysis and search capabilities than text-only or image-only AI systems.
Searching for details by uploading a photo or sketch. Understanding both the visual elements of a drawing and the text annotations. Connecting specification requirements to relevant drawing details.
Automated Drawing Review: Automatically review drawings against building codes, internal standards, and client requirements. Firm-Wide Detail Search: Give designers instant access to every detail your firm has ever drawn.