The Problem
Venture Partners' accounting team spent the last 3 days of every month downloading PDFs, manually keying invoice data into QuickBooks, cross-referencing bank statements, and hunting for discrepancies. It required weekend work, junior accountants hated it, and the firm was paying for it twice - in salary and in errors that needed correction later.
The Solution
We built a Python-based RPA script with OCR that ingests PDF invoices from a monitored email folder, extracts structured data (vendor, amount, date, category), reconciles it against the QuickBooks GL via API, and flags discrepancies in a review spreadsheet. The entire month-end close now runs overnight as a scheduled job. Accountants review a clean exception report in the morning.
How We Did It
Document Intelligence
Trained an OCR pipeline (Tesseract + custom post-processing) on 18 months of historical invoices to extract vendor name, invoice number, line items, and totals with 99.2% accuracy.
QuickBooks Integration
Connected to QuickBooks Online via OAuth API to read the chart of accounts, create journal entries programmatically, and write reconciliation status back to each transaction.
Reconciliation Engine
Built a rule-based matching engine that pairs extracted invoice data to bank transactions using fuzzy amount matching, date windows, and vendor name normalization.
Scheduled Automation
Deployed as a nightly cron job that processes all invoices received since the last run, generates a reconciliation report, and emails the exception list to the partner on duty.
Cost Reduction
Month-End Close Time
OCR Accuracy
Tools & Technologies Used
“Our auditors reviewed the output and asked if we had hired a Big Four firm. We hadn't - we'd hired Loopzillalabs.”
Thomas W., Managing Partner - Venture Partners
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