Our sales people request invoices from a potential customer. On those invoices are our competitor's services and price. Invoices can come in PDF, png, jpeg, excel, csv, email formats. Content formatting can come in random forms. Pricing breakdowns are also non-standard across invoices. We have matching services and our own prices.
The goal is to find similar services where we charge less. In the past, our sales people would spend hours combing through those invoices. We wrote a prompt for GPT4, fed in our services and prices, and asked it to find services we could potentially replace as well as our profit margin. It took us a day to write this prompt. The results were outstanding and GPT4 gave accurate results. We even asked it to package it up in a PDF for us to send to the potential customers. On a Monday morning, we started on the prompt. By Tuesday morning, we got it working well enough that we were confident ship it to a few of our sales people to test.
This will save our company hundreds of thousands each year and we can get back to the potential customer much faster than before - increasing the likelihood of a sale.
If we had to program this like normal software, it'd probably take months to get it right with dedicated engineering resources to account for new invoice edge cases. Chances are, engineering would never even prioritize this feature for our sales people because there is simply no economical way to account for so many different invoice edge cases.
I believe we're just getting started. If we get GPT6 in two years and massive improvement in inference cost and context size, it's going to change everything we do. Heck, even GPT4 with 100x context size and 100x lower cost per inference would be transformative.
If this is a bubble, I'd like to live in it. I believe that many businesses have found use cases similar to the impact of ours. But they're just not broadcasting them to the internet in order to keep it a business advantage.