Model-Based Quantity Extraction
AI extraction of accurate quantities from BIM models for estimating and procurement.
Definition
Model-Based Quantity Extraction AI automatically extracts accurate quantities from BIM models for cost estimating, bidding, and procurement. It handles various quantity types, accounts for waste factors, and formats output for cost databases and procurement systems.
In Depth
Extracting quantities from BIM models produces more accurate takeoffs than measuring from 2D drawings because the model contains precise three-dimensional geometry. AI enhances this extraction by understanding which model elements correspond to which cost items and handling the edge cases where model data needs interpretation.
The mapping between model elements and cost line items is not always one-to-one. A Revit wall type includes the studs, insulation, gypsum board, and paint — but the cost estimate prices each of these components separately. AI decomposes composite model elements into their component materials and quantities, producing a takeoff that maps directly to the cost estimate structure.
Examples
Extracting concrete quantities
Calculating material takeoffs
Generating bid quantities
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
Model-Based Quantity Extraction AI automatically extracts accurate quantities from BIM models for cost estimating, bidding, and procurement. It handles various quantity types, accounts for waste factors, and formats output for cost databases and procurement systems.
Extracting concrete quantities. Calculating material takeoffs. Generating bid quantities.
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