How does ServiceBrief AI handle messy or incomplete job information from real customer calls?

In real service workflows, most job requests come in unstructured formats like phone calls, WhatsApp messages, or quick voice notes — often missing key details (materials, urgency, exact scope, etc.).

I’m curious how ServiceBrief AI deals with these situations:

• Does it infer missing details or leave fields blank?

• How does it avoid incorrect assumptions when information is unclear?

• Can users customize how strict or “safe” the structuring process is?

• What happens when conflicting info appears in the same conversation?

This seems like a critical part of making the tool reliable in real-world HVAC/plumbing/electrical workflows where data is rarely clean from the start.

Would love to understand how the system is designed to handle ambiguity and prevent costly misunderstandings.