THE PROBLEM Pre-trained models break on new formats Template approaches need constant updates Hardcoded extraction fails on layout changes Custom ML requires training data and time THE BETTER APPROACH Stop trying to match templates. Define what fields you need. Let AI figure out the layout. Use extraction with JSON Schema: You define structure once (invoice number, date, items, totals) API extracts those fields regardless of layout Returns consistent JSON every time REAL EXAMPLE Client with 12 vendor invoice formats: Single column layouts Two column layouts Tables with merged cells Handwritten notes mixed in Multi-page with scattered totals Different languages Old approach: Build template for each format (12 templates to maintain) New approach: One schema, works on all formats THE SCHEMA ```json { "invoice_number": "string", "date": "string", "vendor": "string", "line_items": [ { "description": "string", "quantity": "number", "price": "number" } ], "subtotal": "number", "tax": "number", "total": "number" } ``` Send any invoice + this schema. Get back consistent JSON. RESULTS Handles any invoice format: Yes Accuracy: 96.8% Maintenance: Zero (no templates to update) Setup time: Define schema once (10 minutes) WORKS WITH n8n, Make, Zapier HTTP Request node or community node Send PDF + schema Get structured JSON back Route to your system Templates: n8n | Make | Zapier Current clients: 8 using this for invoices 4 for contracts 3 for medical forms 2 for receipts Average accuracy: 96.2% Template maintenance: $0 The key: Don't train on layouts. Define what fields you need. What variable format extraction are you fighting with templates?

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