Connect to IntercomBuild an AI Support Response Review Process
Deploying AI in support is not a set-and-forget decision. AI responses drift over time as your product changes, your pricing evolves, and your customers' questions shift. A lightweight review process keeps Fin accurate without requiring manual transcript review of every conversation.
The ad hoc review approach
- 1Review Fin transcripts periodically when someone has time.
- 2Update knowledge sources when you notice a specific gap.
- 3Check CSAT periodically to see if Fin quality is slipping.
The problem: Ad hoc reviews find what you happen to look for. Systematic degradation — Fin giving outdated pricing, referencing discontinued features — goes undetected until customers start complaining.
With Supportman
Supportman builds a review signal into the CSAT workflow. Every dissatisfied emoji rating (🙁 😠) on a Fin-handled conversation is a candidate for review — no proactive sampling required. On Pro and above, IQS scores on closed conversations add another quality signal.
- Route 🙁 😠 😐 ratings to a dedicated DSAT channel in the Supportman App Home (Pro and above).
- Team lead reviews the transcript from the Slack message link.
- Content fix is prioritized and applied in Intercom — process repeats signal-driven.
Review what customers actually rated — Supportman routes dissatisfied Fin CSAT to Slack in real time and scores closed conversations with IQS on Pro and above.
How many conversations should I review per week for quality?
Focus on signal-driven review rather than volume-based sampling. Review every Fin conversation with a 🙁 or 😠 rating — this is typically a manageable number for a mid-size team.
Should humans review Fin conversations before they are sent?
Fin AI operates synchronously — the response is sent in real time, so pre-send human review is not practical. Post-send quality monitoring (CSAT, IQS) is the standard approach.