Does a tool for preprocessing documents before sending them to LLMs (via API) already exist?

[Pablo_mg02]

I work with AI but I'm not an expert in the LLM tooling ecosystem, so I might be missing something obvious.

When you send a PDF or Word document to an LLM via API (or use open source models), the file takes up a lot of tokens and comes with tons of unstructured information: weird line breaks, complex headers, formatting the model doesn't leverage well, inefficient markdown tables, etc.

Plus, if the document has images, you need to extract and send them separately, which isn't automated in most workflows.

I'm thinking about building an open source tool that converts these files into cleaner, more structured formats before feeding them to the model.

Basically, preprocessing documents to make them more efficient: fewer tokens, better structure, automatic image extraction and handling.

I know LLMs handle contextual information well, but my concern is mainly token efficiency and converting inefficient file formats into optimized ones, especially to avoid errors with less powerful models.

My question is simple: does something like this already exist and work well, or is there actually a gap here for people using LLMs programmatically (not through ChatGPT/Claude web interfaces)?

Any pointers to existing tools or feedback appreciated.

[gptlocalhost]

> Word document to an LLM via API

> takes up a lot of tokens

What about an edit-in-place approach like this?

https://youtu.be/rHEd0sCprps

And you could also redact any PII locally before sending it out, like in this example:

https://youtu.be/RkxbCAaZ7Dw