I dont know who the target is for this. But if it's big enterprises, then this might not be as appealing as it sounds. I used to work for Datacert, a legal apps vendor for large companies(walmart, jpmc, novartis etc). They use e-billing with a well defined format(ledes) all items on an invoice is code with a specific code(UTBMS). We used a rules engine(Drools) to ensure proper billing. A proper use for machine learning in legal space would be simulations for different pricing models(Alternate fee arrangements(AFA) in legal parlance). If machine learning could evaluate different pricing models for a given case and provide inputs, that would be a big selling point.

You'll get 80% of the benefit just by looking at word frequency, highlighting outliers and then a weight based on factors such as length and secret-sauce weighting.

Bonus points if you're using multiple categorizations (using different weights for different industries).

NLP / statistical stuff is fun ;)

Are you scanning / OCRing the documents? I never managed to get the OCR to be good enough for invoicing, there always had to be a manual process to fix the (machine-learning-flagged) errors.

Or don't you need accurate-to-the-cent invoices?