Most back-office processes follow the same shape: something arrives, a human reads it, a human decides what it is, a human puts it somewhere. The process works at low volume. It doesn't scale.
I built an end-to-end invoice processing workflow. Email arrives. Attachment is extracted. Claude reads it and categorizes — vendor, amount, due date, IRS tax category. Structured record written to the database. Any invoice that can't be resolved cleanly goes to an exception queue for human review. The exception queue is not a fallback — it's a design decision. Graceful degradation is part of the architecture.
What used to take 10 minutes per invoice runs in 20 seconds as a batch, regardless of volume. One invoice or fifty, the process shape is the same.
The financial operations layer runs alongside it. Income and expenses tracked against a 50/30/20 allocation model. Alerts when any category hits 80% of budget. 0ドル AI spending authority — the AI categorizes and tracks, a human approves any money movement. Every dollar still has a human in the loop.
The pattern scales to any back-office process with a defined input shape. Start with the process that's eating the most manual time. Instrument it properly. Build it to handle what comes next.
- Email-to-database invoice processing pipeline
- AI classification (vendor, amount, date, IRS category)
- Exception queue with graceful error handling
- Budget tracking with 50/30/20 allocation model
- Threshold alerting at 80% of category budget
- 0ドル AI spending authority enforcement
n8n · Claude (Anthropic) · Supabase · Gmail API · Slack API
Jordan Waxman — jordanwaxman.com · LinkedIn