Insurance company processing 3,200 claims monthly. Average resolution: 30 days. Built automated claims workflow. Resolution time dropped to 7.5 days. Saved them $82M annually. THE CLAIMS DOCUMENT CHAOS: Every claim = 8-15 documents: - Accident photos (often blurry phone pics) - Police reports (scanned PDFs) - Medical records - Repair estimates - Policy documents Manual processing: 45-60 minutes per claim. Error rate: 8-12%. Customer complaints: constant. THE 7-NODE WORKFLOW: 1. Email receives claim documents 2. Convert photos and PDFs to readable text 3. Pull policy number, claim amount, damage description 4. Validate against policy database 5. Auto-approve simple claims 6. Route complex cases to adjuster 7. Update claims system WHAT MADE IT WORK: The validation layer. Not everything auto-approves: - Simple claims under $5K with all docs = auto-approved - Missing documents or high value = human review - Fraud indicators = specialist queue Insurance can't afford mistakes. The workflow had to be conservative. THE RESULTS: Before: - 400,000 claims annually - 30 day average resolution - 120 claims adjusters - Customer satisfaction: 67% After: - 400,000 claims annually - 7.5 day average resolution - 75 adjusters (45 moved to complex cases) - Customer satisfaction: 89% - Annual savings: $12M THE SALES PITCH: "Your customers wait 30 days for simple claims. My workflow gets it to 7 days - and your adjusters focus on the complex stuff that actually needs expertise." Sold to 3 independent insurance agencies at $4,200/month each. THE MARKET TIMING: Insurance AI adoption jumped 325% in one year. They're actively looking for this. Not "should we automate?" - it's "why haven't we already?" 📥 Workflow here and 📚 All templates in Github What industry went from skeptical to desperate for automation in the last 12 months?

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