As an accountant in a prior life, I can tell you that this approach won't provide anything near "the most pertinent facts". That's because public earnings reports are written specifically to circumvent automated analysis. Wall Street firms have tools in place to scrape the data tables from those PDFs, and even then the technology isn't perfect, because layouts aren't standardized (merged cells, table built in InDesign, etc).
At best, it will get the headline numbers right, which are meaningless without context. What does a quarterly profit of $3.3m for MSTR mean without benchmarking against its competitors, or taking the macroeconomic environment (interest rates, etc) into account?
The headline numbers on the balance sheet, P&L and cash flow statement don't say nearly as much as the notes to those statements, which often contain minute details that are extremely relevant for investors and analysts. Trained accountants and analysts can miss details when reading through those, so how is AI going to parse it any better?
Unfortunately, this smacks of "quantity of articles" over quality of analysis, the latter of which represents why journalism is valuable and necessary.