Progenitors
As described in the intro section, a basic design choice of AzoraOne is to isolate knowledge on a company level. Progenitors is a resource used to scale value; the knowledge obtained for one company can be inherited to another company. This goes for the base data as well as the categorization.
To exemplify; let us pretend you have only one customer using your accounting software. AzoraOne has been integrated and knowledge has been accumulated for this particular company. AzoraOne automated the data entry for base data as well as categorization - in real-time, directly in your user interface, right. Now, you get a second customer: Using the resource Progenitors you could inherit all of the knowledge accumulated on the first customer - so that the second "hits the ground running" experiencing automation of data entry. Now, the knowledge for the new company is still isolated on a company level, only, the new company starts of by inheriting (hence "progenitors") the knowledge from the first. As some human oversees the data entry, she may wish to make changes, let us say the choice of a particular account for a particular purchase. This change becomes a lesson for the algorithm of AzoraOne, and the new knowledge starts to overrule the inherited knowledge.
Use case #1 - the accounting firm scaling their own previous data-entry
An accounting firm takes care of the bookkeeping for many customers in your accounting software. AzoraOne is observing in the background, learning, and delivering automated data entry right in your user interface, right. Now this firm gets a new customer, sets it up in your software. In this scenario, you could choose to present the opportunity for your end user - let us say in some settings interface - to scale the work they already have invested on other customers of theirs. In this setting, the accountant chooses a company in the same industry as the new customer - and inherits the knowledge obtained on the first customer to the new one.
As they start bookkeeping, they will receive automated suggestions for base data as well as categorization. And of course, as they (might) make changes, the algorithm will learn - and let the new knowledge overrule the old.
Use case #2 - industry specific robots
Let us say you have customers that are small business owners, not as cunning in the art of bookkeeping as a professional bookkeeper/accountant. You would want to deliver automated bookkeeping - as much as possible. A common problem would typically be the categorization; there simply is not a one-fits-all account; the choice of account differs depending on the company's use of a certain product they buy. Using the resource Progenitors, you could prepare standardized "Industry robots" that are loaded with knowledge that suits that particular industry. Offer these on a robot-as-a-subscription basis, or just let them run in the background as an included automation solution.