Building a Self-Updating AI Pipeline for Expense Extraction

Billino describes a self-updating expense-extraction pipeline in which user corrections are captured as labeled cases, distilled by an LLM into candidate one-line rules, validated through shadow runs and replay against existing tests, then activated per tenant and vendor so later extractions re-run with the new rules. The post walks through the full loop from upload to promotion and discusses pitfalls including personal data in invoices, narrow rules, limited bug coverage, scope, and setup, API, and stability costs.