Embeddings turn text, attributes and intent signals into vectors that can be compared by how close they are in meaning.
How to use the term
What to read alongside
This term is worth reading together with the neighbouring concepts in its section and the Gravity AI launch scenarios it belongs to.
Glossary section
The architecture patterns behind search by meaning, answers grounded in store data and the quality of the top results in the list.
What it is
Embeddings turn text, attributes and intent signals into vectors that can be compared by how close they are in meaning.
Example in ecommerce
The phrase "fragrance-free cream for sensitive skin" can surface the right products even when the product page never uses those words.
Business impact
Improve search by meaning, widen catalog coverage and raise the odds that a shopper finds a fitting product fast.
A fit for terms about search by meaning, the architecture behind the results and product discovery.
Shows how AI terms turn into a real decision flow inside the product page.
Collects the product scenarios in one place and is the starting point for a demo.
We will show which scenario to start with, how to tie it to metrics and where measurable results come fastest.