🧾 Invoice & Receipt Processing

The problem

Monthly books close slowly because someone has to manually enter 200 receipts. Accountants charge for manual data entry that a computer can do. Mistakes happen at 4pm on the 30th of the month. Receipts get lost; expenses go uncategorized.

The outcome

Every receipt or invoice hitting a shared inbox (or dropped into a shared folder) gets parsed: vendor, date, amount, category, tax, currency. Clean rows go to QuickBooks/Xero/Notion. Anomalies (amount > threshold, unknown vendor, duplicate) get flagged for human review before auto-posting.

Why IronClaw

Financial data deserves a sandbox: IronClaw keeps credentials encrypted and out of its tools and limits each tool to allowlisted hosts. It does not provide an accounting audit trail for you, so log the source file, extracted values, model and confidence for every record in your bookkeeping tool or a database.

Alternatives worth considering

• OpenClaw — For small businesses with lighter audit needs: faster to set up, with its own hardening guide
• Hermes — If you want the agent to also remember vendor patterns over time and improve categorization accuracy

Setup steps

1. Step 1: Set up the inbound channel

   A dedicated email (receipts@your-domain) or a shared Dropbox folder. Don't pipe your regular inbox — agents and financial data need isolation.

2. Step 2: Configure the extraction schema

   Vendor, date, total, line items, tax, currency, category. Categories match your chart of accounts. Explicit schema means the output plugs straight into bookkeeping without reformatting.

3. Step 3: Add the anomaly rules

   Flag: amount over [threshold], vendor never seen, duplicate (same vendor + amount + date), foreign currency, missing tax field. Flagged items go to a human queue.

4. Step 4: Integrate with your bookkeeping tool

   QuickBooks/Xero/FreshBooks/Notion all have APIs. Clean rows auto-post; flagged rows sit in a queue until approved.

5. Step 5: Add monthly reconciliation

   End of month, the agent pulls the bank statement, matches each transaction to a processed receipt, flags any unmatched. You only look at the exceptions.

Example prompt

Extract from this receipt: vendor, date, total, line items with amounts, tax, currency, suggested category from [chart of accounts]. Output a confidence score 0–1. If confidence < 0.8, flag for human review with a specific reason.

Pitfalls to avoid

• Trusting OCR on low-quality photos. A crumpled receipt photo at 2pm after lunch yields garbage. Have the agent output a confidence score; below threshold → human review.
• Auto-categorizing everything. Categories matter for tax time. A misclassified 'meals' vs 'entertainment' can cause audit issues. Auto-categorize confident matches; defer ambiguous ones.
• Not testing the recovery path. If the agent fails for a week, can you catch up manually? Build in a weekly 'show me what hasn't been processed' report.

Cost breakdown (monthly)

Cost: Model usage plus bookkeeping tool. Model rates change often; check the model pricing table or the cost calculator for current figures.

Related guides

• Security architecture
• Configuration
• Model API pricing