How Nuuly turned AI savings into higher pay for its support team
Questions about managing inbound conversations across channels like WhatsApp, Facebook, Instagram, X/Twitter, and other non-Messenger channels
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Hey everyone 👋I'm curious how Intercom teams are handling something that seems to get harder as support volume grows.How do you currently make sure that every message sent to a customer is safe and accurate whether it's written by a human agent or Fin?A support agent might accidentally:promise a refund they aren't authorized to givegive incorrect pricing or policy informationmake a commitment the company can't honorexpose sensitive customer informationgive inappropriate financial/medical advicesend something that creates a potential liability issueAnd with AI handling more conversations, there can be hundreds or thousands of responses that nobody manually reviews.Do you manually sample conversations, rely on CSAT/customer complaints, have a QA process, or are you monitoring them automatically?I've been building ChatShield specifically for this: it connects to Intercom and monitors workspace conversations not just AI conversations to flag potentially risky messages so teams can review
Hi everyone, We receive inbound conversations through WhatsApp, but in many cases the customer’s WhatsApp number is not associated with any existing user in our Intercom workspace. Our desired flow is:The contact starts a conversation via WhatsApp. We ask for their email address. Intercom checks whether that email already belongs to an existing user in the workspace. If it exists, we send an OTP code to verify ownership of the email address. Once the verification succeeds, the conversation is automatically associated with the existing user instead of remaining as a lead.Is this flow possible using native Intercom features only (Workflows, Fin, identity verification, etc.), or would it require implementing it through the Intercom API? Thanks in advance!
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