Automating Financial Reconciliation with Python and Plaid

If you run a small business, you know the drill. Every month you sit down with your bank statements, open QuickBooks, and start the tedious process of matching transactions. Did that $47.99 charge from "SQ *COFFEE HOUSE LLC" match the office supplies entry or the client meeting expense? Multiply that ambiguity by hundreds of transactions and you have a full afternoon gone. I built a pipeline that handles this automatically. It pulls transactions from Plaid, categorizes them with AI, reconciles them against expected journal entries, and flags anything it is not confident about. The whole thing runs on Python with Supabase as the data store. This article walks through the architecture and the code. I am Parker Gawne, founder of Syntora , and this came out of a real project we built for accounting automation. Architecture Overview The pipeline has four stages: Pull - Plaid API syncs bank transactions into Supabase Categorize - Each transaction gets classified. Ambiguous ones go through Cl