[GTM glossary](/glossary.txt) Company embeddings

Contextual Intelligence & AI

# Company embeddings

In short

Company embeddings are lists of numbers, called vectors, that capture the meaning of a company based on its description, website, products, and other data. Companies that do similar things end up with vectors that are close together.

This lets software compare companies by meaning instead of by exact keywords or industry codes. In go-to-market work it is a common way to find lookalike accounts and to group markets.

## How company embeddings work

### Collect text and attributes

Inputs can include website copy, product descriptions, and category labels.

### Run it through an embedding model

The model converts the content into a vector. Texts with similar meaning produce similar vectors.

### Measure closeness

Similarity is calculated between vectors, often with cosine similarity. A vector database or index returns the nearest companies quickly.

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## What GTM teams use them for

* **Lookalike search:** Start from your best customers and find companies whose vectors are nearest.
* **Segmentation:** Cluster a market into groups by what companies actually do.
* **Search by description:** Type "payroll software for restaurants" and find matches without exact keywords.
* **Scoring features:** Use similarity to past customers as an input to fit scoring.

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## Example

Say a team has 40 customers in warehouse automation. It averages their vectors and searches for the nearest 500 companies. The list includes firms in robotics and fulfillment software that no industry code search would have grouped together. The team reviews the list and keeps the relevant ones.

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## Limits to know

* **Text quality:** Thin or outdated descriptions give weak vectors.
* **Similar is not the same as fit:** A company can sound alike but be too small or in the wrong region. Combine embeddings with firmographic filters.
* **Hard to explain:** Vectors do not tell a rep why two companies match, so show the supporting text.

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## Company embeddings vs. embeddings

Embeddings are the general technique and can represent text, images, or people. Company embeddings apply it specifically to firms.

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## Company embeddings FAQs

**What are company embeddings?**

Company embeddings are numerical vectors that represent what a company does, created from text and data such as website copy and product descriptions. Companies with similar activities have vectors that sit close together, which makes similarity measurable.

**How are company embeddings used to find lookalike companies?**

You take the embeddings of your best customers, combine them, and search for the companies whose vectors are nearest. The results are firms that do similar things, which you can then narrow with filters such as size and region.

**Are company embeddings better than industry codes?**

They capture meaning at a finer level, so they can group companies that codes keep apart. But they depend on text quality and are harder to explain. Many teams use both, with codes for broad filtering and embeddings for similarity.

**Do you need a vector database to use company embeddings?**

For a small set of companies you can compare vectors directly. At larger scale, a vector database or index makes nearest-neighbor search fast. The choice depends on how many companies you search and how quickly you need results.

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## More related phrases

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

## Similar phrases

* [Embeddings](/glossary/embeddings.txt)  
Embeddings are numeric representations of text, companies, or other items that place similar things close together, enabling semantic search, matching, and lookalike discovery.
* [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.
* [Lookalike companies](/glossary/lookalike-companies.txt)  
Lookalike companies are accounts that resemble your best customers across firmographic, technographic, and behavioral traits, found by comparing company data.
* [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.