Construction company CFO: "We spend $78,000 yearly on data entry. Can automation help?" Their chaos: Quotes via fax, estimates on napkins, invoices by email, photos from job sites. THE DATA ENTRY NIGHTMARE - 2.5 full-time data entry clerks - 40 hours weekly manual processing - Sources: Fax, email, photos, scanned documents, handwritten notes - Error rate: 12% - Processing delay: 3-4 days average THE 4-HOUR AUTOMATION BUILD Used my construction template library: Input automation (Nodes 1-4): - Fax server monitoring (yes, they still use fax) - Email attachment processing - Dropbox photo sync from field teams - Manual upload portal for edge cases Document intelligence (Nodes 5-9): - Source type detection (fax, scan, photo, digital) - Quality assessment and enhancement - Content classification (quote, invoice, estimate, permit) - Handwriting detection and routing Processing engine (Nodes 10-14): - Multi-path extraction based on document type - Vendor pattern recognition (learns common formats) - Financial data validation and formatting - Project code assignment automation Integration layer (Nodes 15-16): - Direct construction management software sync - Real-time Slack notifications for exceptions Build time: 4 hours Testing: 2 hours Deployment: 1 hour Training: 1 hour THE IMMEDIATE IMPACT Week 1 results: - Processing time: 3-4 days → 12 minutes average - Accuracy: 88% → 97.3% - Data entry cost: $1,500/week → $0 - Exception handling: 8% require human review Month 1 savings calculation: - Eliminated 2.5 positions: $78,000 annually - Reduced processing errors: $12,000 annually - Faster project invoicing: $23,000 additional revenue - Total impact: $113,000 annually My fee: $8,500 setup + $1,200/month maintenance THE SCALING SUCCESS Construction template portfolio: - General contractors: 7 clients - Electrical contractors: 3 clients - Plumbing companies: 4 clients - Specialty trades: 6 clients Industry adaptations: - Permit processing automation

Has anybody here build an AI automated workflow in N8N and scrapes websites for content and builds its own knowledge based to train an AI model? I know Firecrawl offers this option but I am more interested to know if you have implemented this using Crawl4AI. Cheers!!