The shared mailbox stops being a pile of unknowns and becomes a sorted work list

The classification layer is trained on your own mail. We analyse a sample of mailbox history, agree a label set with the supervisors who sort it today, and have your team label training examples in UiPath Communications Mining: meter reading submitted, invoice copy requested, payment plan, tariff change, move-in, supply interruption, damage claim, complaint, and mail that needs no action. The model also extracts the fields routing depends on and returns a confidence score for every prediction. This is supervised machine learning on your correspondence: it recognises and extracts, it does not write.

What follows is deterministic and auditable. A robot checks whether the thread already belongs to an open case, matches the customer in the billing system, then creates or updates one case carrying the intent, priority, due date and the original message. Routing follows a rule table the service manager owns, not the model. A defined set of standard requests is fulfilled from approved templates without an agent: an invoice copy, an account balance, the status of a case, confirmation that a meter reading was recorded.

People meet the flow in Microsoft Teams. Urgent intents are posted to a channel with a link to the case as soon as they are recognised. Messages below the confidence threshold arrive as Action Center tasks in Teams, where a team leader confirms or corrects the label in seconds and the correction returns to the training set. One boundary is worth stating plainly: this solution resolves nothing. It decides what each message is, how urgent it is and who owns it, then hands complaints to the process that resolves complaints.