Built document extraction system. 99.4% accuracy on test data. Client lost $42,000 in first month. The 0.6% errors hit their highest-value transactions. WHAT WENT WRONG: System accuracy breakdown: - 99.9% accurate on standard invoices - 96% accurate on invoices $5,000-$10,000 - 91% accurate on invoices $10,000-$50,000 - 87% accurate on invoices over $50,000 The bigger the transaction, the worse the accuracy. And that's where the money was. THE $42,000 INCIDENT: Invoice: $420,000 construction vendor payment Extracted amount: $42,000 (missed a zero) Payment processed: $42,000 Vendor called: "Where's the other $378,000?" Bank reversal fees: $850 Staff time to fix: 12 hours Client trust damage: Severe WHY HIGH-VALUE INVOICES FAIL: - Custom contract formats - Handwritten notes and exceptions - Multi-page complex layouts - Non-standard payment terms - Executive approval signatures All the edge cases. All on the expensive invoices. THE FIX - THREE VALIDATION LAYERS: LAYER 1: Format Validation - Extracted amount matches currency format? - Dates within valid business ranges? - Line items actually sum to total? - All required fields present? LAYER 2: Business Rule Validation - Amount within vendor's 6-month history range? - Payment terms match vendor profile? - Missing PO number for amounts over $5,000? - Unusual discounts or adjustments flagged? LAYER 3: Confidence-Based Routing - High confidence (>95%) + Low value (<$5,000) = Auto-process - Medium confidence (85-95%) OR Medium value ($5-20K) = Review queue - Low confidence (<85%) OR High value (>$20K) = Always human approval RESULTS AFTER IMPLEMENTING LAYERS: - Zero high-value errors: 8 months and counting - Human review required: Only 8% of documents (was 100%) - Processing speed: Still 82% faster than full manual - Errors caught before processing: 23 incidents - Estimated damage prevented: $187,000 THE LESSON: Accuracy percentage means nothing without context. What matters: What happens when it fails? Does it fail safely?

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