[GTM glossary](/glossary.txt) Embeddings

Contextual Intelligence & AI

# Embeddings

In short

Embeddings are lists of numbers (vectors) that represent the meaning or characteristics of an item such as a sentence, a company description, or a product. A model produces them so that similar items end up with similar numbers.

This lets software compare meaning mathematically. A search for "payment software for small shops" can find a page about "point-of-sale tools for retailers" even though no words match.

## How embeddings work

A model reads an input and outputs a vector, often hundreds or thousands of numbers long. To compare two items, you measure the distance or angle between their vectors. Closer means more similar. The numbers have no human-readable meaning individually; the geometry carries the meaning.

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## Where GTM teams use embeddings

* **Semantic search:** Find companies or documents by meaning, not exact keywords.
* **Lookalike accounts:** Embed company descriptions and find neighbors of your best customers.
* **Clustering:** Group accounts or call transcripts into themes without predefined labels.
* **Retrieval for AI:** Fetch the most relevant passages to ground a language model answer.
* **Deduplication hints:** Flag near-identical company descriptions for review.

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## Example

Say you embed the descriptions of your 50 best customers and of 100,000 other companies. Averaging the customer vectors gives a "center", and the companies nearest to it become a candidate list for review. It is a fast starting point, not a verdict on fit.

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## Practical tips

* **Embed good text:** Quality of the source description limits quality of the result.
* **Use one model consistently:** Vectors from different models are not comparable.
* **Store in a vector index:** A vector database or index makes nearest-neighbor lookups fast.
* **Combine with filters:** Apply hard criteria such as region or size alongside similarity.
* **Re-embed on change:** Update vectors when the underlying text changes or you switch models.

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## Common mistakes

* **Treating similarity as fit:** Similar text does not guarantee a buyer.
* **Embedding thin data:** Short, generic descriptions give vague vectors.
* **Skipping evaluation:** Spot-check results against known good and bad matches.

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## Embeddings FAQs

**What are embeddings in AI?**

Embeddings are numeric vectors that represent the meaning of text, images, or other data. A model creates them so that similar items have similar vectors, which lets software compare meaning, search by concept, and group related items.

**How are embeddings used in B2B sales and marketing?**

Teams use embeddings for semantic company search, finding lookalike accounts, clustering accounts or conversations by theme, and retrieving relevant passages for AI assistants. They help find matches that keyword search would miss.

**What is the difference between embeddings and keywords?**

Keyword search matches exact words or close variants. Embeddings match meaning, so different wording about the same idea can still match. Many systems combine both, using keywords for precision and embeddings for recall.

**Do I need a vector database to use embeddings?**

Not for small datasets, where you can compare vectors directly. For large collections, a vector index or database speeds up nearest-neighbor searches considerably and is the usual choice in production.

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## More related phrases

* [Lookalike companies](/glossary/lookalike-companies.txt)
* [Business ontology](/glossary/business-ontology.txt)
* [Natural language search](/glossary/natural-language-search.txt)

## Similar phrases

* [Company embeddings](/glossary/company-embeddings.txt)  
Company embeddings are numerical vectors that represent what a company does, learned from text and data, so software can measure how similar two companies are and find lookalikes.
* [Vector database](/glossary/vector-database.txt)  
A vector database stores embeddings, which are numeric representations of text or other data, and quickly finds the items most similar in meaning to a given query.
* [Vector search](/glossary/vector-search.txt)  
Vector search finds items by comparing numeric embeddings and returning those closest to the query, which lets systems match on meaning, similarity, or other features rather than exact keywords.
* [Semantic search](/glossary/semantic-search.txt)  
Semantic search finds results based on the meaning and intent of a query rather than exact keyword matches, so related wording and descriptions return relevant results.
* [Retrieval-augmented generation (RAG)](/glossary/retrieval-augmented-generation.txt)  
Retrieval-augmented generation (RAG) is a technique where an AI model first looks up relevant documents or data, then uses them to write an answer grounded in that source material.