Similar.ai demand-based content helps you reduce duplicate content by increasing the uniqueness, freshness and depth of your category pages:
- We create a universe of search intents: all the search engine keywords where the searcher has the intent to transact in a certain domain, such as automotive, clothing, electronics or homeware.
- We cluster keywords into topic groups using our knowledge graph and we pair the universe of transactional unbranded intents with information about your pages and product listings.
- At a page level the platform can see both the main keywords a page targets and all of the longer tail variations.
- We use our knowledge graph to find the common user needs for which users search and turn these into language, or what we call content entities.
For instance, the
color_top_3
content entity finds the three colours with the greatest total search volume from amongst all the keywords with the page needs. 
These types of entities form the heart of our demand-based content. However, until recently, it was sometimes hard to know why the platform chose certain content. Not any more. Now you can quickly check which keywords and volume were used for each entity on any page. Just click on the inspect icon from the main content page to see zoom in to each entity.
For instance, how would the platform reach a decision to create the answer "The most popular colours for an Abarth are grey, yellow and black" for the
/cars/uk/abarth
page?
Just click on the "inspect" icon 
Here we can see that the platform found a total demand of
- 1,490 for grey across 39 keywords
- 1,240 for yellow across 20 keywords
- and 1,140 for black Abarths across 35 keywords
It used this data to create the answer "The most popular colours for an Abarth are grey, yellow and black".
This new inspection functionality lets you into how the content is created for any of your entities on any of your category pages.