Claude
22% of revenue, paying 19 days late
The owner exported 14 months of invoices as a simple file. The file held client, dates, amount, and date paid. She removed bank details and cut client names to an initial plus an industry. She uploaded the file to Claude and asked direct questions. She also asked Claude to flag any odd pattern, even one it could not explain. She ran the same check in ChatGPT.
How it wentThe work took under an hour with follow-ups. One client was 22% of invoiced revenue and paid 19 days late against 30-day terms, every time. Her day rate had not changed in 3 years, and overheads had risen about 19%. She sent a price review letter to five legacy clients and a payment-terms email to the late payer. One flat-fee add-on paid under minimum wage per hour, so she retired it. Claude also called a dip seasonal, but the dip was her 3-week holiday.
Try it · based on the source's workflow
Analyse this invoice export for [business]. The file has client, invoice date, amount, due date, and date paid; names are initials only. Answer: which clients pay late most often and by how many days on average? Has my average invoice value changed month to month, and why might that be? Flag any pattern you find, even if you cannot explain it. Show the data behind each claim. Do not give tax or compliance advice.
A starting template based on the workflow the source describes. Review it before you use it; you are responsible for what it does.
22% of revenue, paying 19 days late — from The SmallOffice Brief, a daily brief of real AI cases for small businesses: https://smalloffice.space/case-22-of-revenue-paying-19-days-late