# Why use cognee?

LLMs don't have a semantic layer, and they don't have a way to understand the data they are processing. This is where cognee comes in. 
We let you define logical structures for your data and then use these structures to guide the LLMs to process the data in a way that makes sense to you.



??? note "Why use cognee?"

    Its hard to answer the question of why use cognee without answering why you need thin LLM frameworks in the first place.:
 - **Cost effective** — cognee extends the capabilities of your LLMs without the need for expensive data processing tools.
 - **Self contained** — cognee runs as a library and is simple to use
 - **Easy to use** — cognee is simple to use and can be used by anyone with a basic understanding of Python
 - **Flexible** — cognee can be used to structure data in any way you want, and can be used to structure data in any way you want. We rely on the work done by Pydantic and are inspired by the Instructor library, which is a simple way to structure data for LLMs.



## Bring your own data model

If you are building an AI vertical, most of the time you will have a specific data model that you want to use. Cognee lets you bring your own data model and use it to structure your data in a way that makes sense to you.


## Data processing

With dlt you can avoid all the boilerplate code that comes with data processing. We let you define logical structures for your data and then use these structures, deduplicated, incremental and replayable



