I've got one! The pdf of this out-of-print book is terrible: https://archive.org/details/oneononeconversa0000simo. The text is unreadably faint, and the underlying text layer is full of errors, so copy-paste is almost useless. Can your software extract usable text?

(I'll email you a copy of the pdf for convenience since the internet archive's copy is behind their notorious lending wall)

https://news.ycombinator.com/item?id=42443022

I found that at the time no LLM was able to properly organize the text and understand footnotes structure, but non-AI OCR works very well, and restructuring (with some manual input) is largely feasible. Would be interested in what you can do with those footnotes (including, for good measure, footnotes-within-footnotes).

Regarding feeding text to LLMs, it seems they are often able to make sense of text when the layout follows the original, which means the OCR phase doesn't necessarily need to properly understand the structure of the source: rendering the text in a proper layout can be sufficient.

I worked on setting up a service that would do just that, but in the end didn't go live with it; but here's the examples page to show what I mean:

https://preview.adgent.com/#examples

This approach is very straightforward and fails rarely.

There is a section near the start where there are 4 options: Large accelerated filer, Non-accelerated filer, Accelerated filer, or Smaller reporting company.

In this option, "Large accelerated filer" is checked on the PDF, but "Non-accelerated filer" is checked on the Markdown.

I am already seeing this trend in the recent releases of the native models (such as Opus 4.5, Gemini 3, and especially Gemini 3 flash).

It's only going to get better from here.

Another thing to note is, there are over 5 startups right now in YC portfolio doing the same thing and going after a similar/overlapping target market if I remember correctly.

I guess I should thank you for saving my time? Plenty of others in this space.