Semantic Search
Search that understands meaning and context, not just keyword matching.
Definition
Semantic Search uses AI embeddings and natural language processing to understand the meaning behind search queries, not just match keywords. In AEC applications, this allows users to search using natural language like 'show me waterproofing details for below-grade walls' and get relevant results even if those exact words don't appear in the documents. The search understands synonyms, related concepts, and domain-specific terminology to return the most relevant results from drawings, specifications, and project files.
In Depth
Traditional search in AEC software is frustrating because it only finds exact keyword matches. Search for "waterproofing" and you miss every document that says "moisture barrier" or "damp-proofing" or "below-grade protection." Semantic search fixes this by understanding meaning rather than matching strings.
Under the hood, semantic search converts documents and queries into mathematical representations called embeddings. Documents with similar meanings end up near each other in this mathematical space, regardless of the specific words used. When you search for "fire-rated wall assembly between occupancy types," the system finds relevant results even if the documents use phrases like "occupancy separation," "fire barrier," or "2-hour rated partition."
For AEC firms, this is transformative for institutional knowledge. A firm with 20 years of projects has solved thousands of design problems, but that knowledge is scattered across old project files. Semantic search lets a designer working on a hospital project find relevant details from every past healthcare project the firm has done — even if those projects used different naming conventions, different spec formats, or different CAD standards. The knowledge was always there; semantic search makes it accessible.
Examples
Finding 'exterior insulation systems' when searching for 'EIFS'
Retrieving relevant details when searching with a description instead of exact product name
Understanding that 'CMU' and 'concrete masonry unit' refer to the same thing
Nomic Use Cases
See how Nomic applies this in production AEC workflows:
Frequently Asked Questions
Semantic Search uses AI embeddings and natural language processing to understand the meaning behind search queries, not just match keywords. In AEC applications, this allows users to search using natural language like 'show me waterproofing details for below-grade walls' and get relevant results even if those exact words don't appear in the documents. The search understands synonyms, related concepts, and domain-specific terminology to return the most relevant results from drawings, specifications, and project files.
Finding 'exterior insulation systems' when searching for 'EIFS'. Retrieving relevant details when searching with a description instead of exact product name. Understanding that 'CMU' and 'concrete masonry unit' refer to the same thing.
Firm-Wide Detail Search: Give designers instant access to every detail your firm has ever drawn. Project Research: Get AI-drafted responses to RFIs using your project documentation.