# 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.

- Stages: Preconstruction, Estimating, Business Development
- Concepts: Retrieval (RAG), Data
- Published: 2026-08-28
- Canonical: https://buildr.com/library/embeddings-and-semantic-search

## 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

1. **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.
2. **Store the vectors.** The vectors go into an index alongside the original text.
3. **Embed the query.** The system sends the user's question through the same model.
4. **Compare.** It finds stored vectors closest to the query vector, which indicates similar meaning.
5. **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.

## Go deeper

- [How to Build a Subcontractor Database That Actually Gets Used Across Your Team](/blog/how-to-build-subcontractor-database.md)
- [Construction CRM: The Comprehensive Guide](/blog/construction-crm.md)

## Related terms

- [Retrieval-Augmented Generation (RAG)](/library/retrieval-augmented-generation.md): Retrieval-Augmented Generation (RAG) finds relevant pages in a company's own documents and gives them to an AI language model before it answers. It grounds a construction team's responses in its actual specs, proposals, and project history instead of the model's general knowledge.
- [AI Subcontractor Matching](/library/ai-subcontractor-matching.md): AI subcontractor matching uses a general contractor's sub database and bid history to recommend which subcontractors to invite for each trade package on a construction project, based on trade, location, capacity, past performance, and qualification status.
- [Natural Language Querying](/library/natural-language-querying.md): Natural language querying lets a construction team ask questions about business data in plain English, such as 'what is our weighted pipeline for Q4' or 'which superintendents are free in March,' and get answers from the CRM, backlog, and workforce plan without building reports or formulas.
- [Tokens](/library/tokens.md): Tokens are the small chunks of text that AI language models read and write, roughly three-quarters of a word each. They determine both the cost of a request and how much of a construction document fits in a model's context window.
- [Data Readiness](/library/data-readiness.md): Data readiness is how complete, consistent, and accessible a construction company's project, pipeline, cost, and staffing records are. It determines whether AI tools can produce useful forecasts and comparisons or confident answers built on gaps.

## Referenced by

- [Context Window](/library/context-window.md): A context window is the amount of text, measured in tokens, that an AI language model can consider in one request. It limits how much of a construction spec book or proposal set the model can see at once.

## FAQ

### 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.