Embedding
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An embedding is a numerical representation of text, images, or other data as a vector of floating-point numbers, allowing a machine learning model to measure semantic similarity between pieces of content.
Why Embeddings Matter for Proposal Content
In AEC pursuit work, embeddings are the mechanism behind "find content like this" searches across past proposals, project descriptions, and resumes. Rather than matching keywords, an embedding model encodes meaning: a search for "adaptive reuse of historic structures" can surface a project narrative that never uses those exact words but describes the same scope. This is why modern proposal libraries built on embedding search outperform legacy keyword systems on long-tail pursuit queries. The vector distance between two embeddings is the mathematical answer to the question "how relevant is this past project to this RFP?"
How Embeddings Fit the Pursuit Workflow
Embedding-based retrieval becomes most valuable at the shortlisting stage, when a pursuit team is pulling relevant project experience and staff qualifications under deadline pressure. A proposal manager can paste an RFP scope paragraph and retrieve the ten most semantically similar project write-ups from a corpus of hundreds, without manually tagging or categorizing anything in advance. The quality of results depends heavily on what was embedded: clean, consistently structured content yields better vector representations than raw, inconsistently formatted source documents. Garbage-in still applies, even when the retrieval mechanism is sophisticated.
The Non-Obvious Constraint: Embedding Model Choice Is a Long-Term Decision
Most BD teams treat embedding search as a feature they turn on; the more consequential decision is which embedding model generated the vectors stored in the database. Switching models later requires re-embedding every document in the library, because vectors from different models occupy incompatible mathematical spaces and cannot be compared across models. Firms that embed their pursuit archive using a general-purpose model may later find that a domain-specific model trained on construction or government procurement language produces materially better retrieval results for AEC content. Kantiv uses embeddings as the retrieval layer connecting RFP requirements to your firm's historical pursuit data, so that relevant experience surfaces by meaning rather than by metadata tag.
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