Built an AI document triage workflow for dental clinics happy to share what I learned

Built with n8n
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I recently built an n8n workflow that automates something every small clinic deals with: incoming documents by email (referrals, consent forms, insurance paperwork) that someone has to manually read, classify, file, and log.

The flow: Gmail Trigger → extract PDF text → AI Agent (structured output) classifies the document type and pulls admin metadata only (no clinical data) → routes to the right Google Drive folder → logs it in Sheets → flags anything uncertain for manual review by email.

A few things I learned building it that might help others working on similar triage/classification flows:

  • Skip the raw n8n.io template marketplace for anything niche, it’s flooded with free generic templates, hard to stand out.
  • Keeping the AI step to metadata only (not full content) sidesteps a lot of data-sensitivity headaches when the documents involve personal data.
  • Always wire a fallback branch on your Switch node undetected/low-confidence items should never silently disappear.

I packaged it as a ready-to-import kit with a setup guide if anyone wants the finished version instead of building from scratch: LINK. Happy to answer questions about the setup either way.

إعجابَين (2)
4

Welcome @altortxuh! The metadata-only extraction approach is the right call - extracting full PDF content into the AI context is where a lot of document workflows run into data sensitivity issues. The Switch node fallback for undetected items is also a pattern I rely on, and it’s the one that prevents the most silent data loss when edge-case documents come in. One addition that could strengthen the triage: add a confidence_score field to your structured output schema, then route anything below a threshold (0.7 or whatever fits your use case) directly to the manual review queue instead of letting the agent make a low-confidence classification decision.

إعجابَين (2)
5

Great catch, you’re right that category only routing leaves a gap, implemented the confidence threshold (0.7) on the Switch node exactly as you describe, anything below that now falls to manual review regardless of the predicted category. Appreciate the feedback!

إعجاب واحد (1)