Billino shares self-updating AI expense extraction pipeline
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Billino has disclosed the self-updating loop of its AI expense extraction pipeline: user corrections to extraction results are recorded as cases, which the LLM distills into a one-line rule; a rule must first pass a shadow run replaying historical tests and be confirmed to cause no regressions before it is activated. Once activated, rules are bound by vendor and tenant, and are appended to the prompt for the next extraction and re-run. Billino says automatic expense reading is one of its core features.
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- Hacker News AIBuilding 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.
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