Embeddings and Semantic Search
Embeddings are numeric representations of text that place similar ideas near each other even when the wording differs. Semantic search uses them to find construction documents, subs, or past projects by meaning rather than exact keywords, so acoustical ceiling can match ACT and lay-in tile.
Why it matters in construction
A GC’s institutional knowledge is spread across thousands of documents that nobody labeled consistently. One estimator writes “ACT,” another writes “acoustical ceilings,” a sub writes “lay-in 2x4 tile.” A past project’s lessons learned say “owner was slow on pay apps” and nobody searches for that phrase when vetting the same owner three years later.
Keyword search often misses those differences in wording. Semantic search can find related records, making it useful for questions such as “which of our subs have done clean-room work” or “show me proposals similar to this one.”
How it works
- Embed the content. The system breaks each document, proposal, project record, or sub profile into chunks and sends each chunk through an embedding model. The output is a vector, a list of numbers that represents the chunk’s meaning.
- Store the vectors. The vectors go into an index alongside the original text.
- Embed the query. The system sends the user’s question through the same model.
- Compare. It finds stored vectors closest to the query vector, which indicates similar meaning.
- Return the source chunks. The matching text goes to the user or supplies context for a language-model answer. This is the retrieval part of retrieval-augmented generation.
Chunk size matters. A whole 300-page spec as one vector loses too much detail. Metadata filters still matter, so a team might restrict results to mechanical subs in Texas before ranking them by similarity. Hybrid search, which combines keyword and semantic scoring, can help with exact part numbers and section references that embeddings may blur.
Example in practice
Consider a commercial GC with a sub database of about 2,400 firms built up over 15 years, with trade tags entered by whoever happened to be at the keyboard. Precon is bidding a $45M biotech lab and needs subs with real cleanroom and process piping experience.
A keyword search on “cleanroom” returns 11 subs, mostly because someone remembered to add the tag. A semantic search across sub profiles, past proposals, and project closeout records returns 38, including a mechanical sub whose profile says nothing about cleanrooms but whose 2022 pharma proposal describes ISO 7 environments and orbital-welded stainless process lines. The team adds that sub to the bid list.
Frequently asked questions
How is semantic search different from the search bar we already have?
Keyword search matches the exact words you typed. Semantic search matches the idea, so a query for 'hospital projects with ICP ductwork' finds a project record that says 'infection control pressurization' even though neither phrase appears in the other.
Do embeddings understand construction terminology?
General-purpose embedding models know a surprising amount of construction vocabulary, including CSI divisions and common abbreviations. They can still miss firm-specific shorthand, which is why some tools let you add a glossary or fine-tune on your documents.
Is our data sent to a third party to create embeddings?
Usually yes, to the embedding model provider, unless the vendor runs the model on their own infrastructure. Ask where embeddings are computed and stored, and whether the provider retains your text.
Go deeper
- From the blog How to Build a Subcontractor Database That Actually Gets Used Across Your Team Most subcontractor databases die within months. Learn how to build a sub database your estimators will actually use by connecting it directly to your bidding workflow.
- From the blog Construction CRM: The Comprehensive Guide What a construction CRM actually does, why your spreadsheet isn't cutting it, and how GCs use CRM to track relationships and win more work.