Insurance deadline disaster. Their OCR vendor went bankrupt overnight. API returning 404 errors. THE OVERNIGHT CRISIS Medical billing company, 15,000 insurance forms Each form worth average $1,800 in claims Total at risk: $27 million in insurance processing Deadline: 8 AM submission to insurance networks My desperation: Tried every OCR service. Nothing handled their complex forms accurately enough. 4 AM BREAKTHROUGH Stopped fighting for one perfect solution. Built the ugliest but most effective hybrid approach. THE FRANKENSTEIN n8n WORKFLOW Instead of single OCR, created specialized processing zones: Zone 1: Form header → Pattern matching (patient info) Zone 2: Checkbox sections → Binary image detection Zone 3: Printed text → Standard OCR processing Zone 4: Handwritten notes → AI-enhanced parsing Zone 5: Tables → Structured extraction Zone 6: Signatures → Presence detection only Zone 7: Stamps/dates → Template matching Node architecture (17 nodes): - Document splitting by zones - Parallel processing of each zone type - Confidence scoring per zone - Intelligent merging of results - Human review queue for conflicts under 90% THE MORNING MIRACLE 6 AM: System operational 7 AM: Processing at full speed (400 forms/hour) 8:30 AM: 12,000 forms complete and submitted 10 AM: All 15,000 processed Final accuracy: 96.8% CEO called crying. Literally. Saved their contracts, saved 47 jobs, saved company. CRISIS BECOMES BUSINESS Emergency solution worked so well: - They signed 3-year contract: $8,500/month - Referred me to 8 other medical practices - All terrified of vendor dependency Current medical form processing: - 6 medical billing companies - 75,000+ forms monthly - 97.2% accuracy average - Revenue: $31,200/month THE UNIVERSAL LESSON No single tool handles everything perfectly. But combining specialized tools for each part of the problem? Magic. My "frankenstein" workflows handle the impossible documents everyone else rejects: - Medical forms with handwriting: $18,200/month revenue

Overview: Managing multiple MSPs across the country, we needed a way to quickly review incoming resumes and decide who we really want to bring in for interviews. To solve this, we built an automated hiring pipeline in n8n that screens resumes in real-time and posts structured candidate assessments directly into Slack. How it works: - Resume Intake via Google Drive – Candidates upload their resumes into specific Drive folders (per company). The workflow automatically detects new files, downloads them, and extracts text from the PDFs. - AI-Powered Screening – The extracted resume text is analyzed by GPT against the role description (in this case, Senior MSP Systems Engineer – Tier III). The AI produces a standardized assessment including candidate name, relevant experience, technical skills, red flags, soft skills, technical level, and a fit score (1–10). - Slack Delivery – The final assessment is automatically pushed into our Slack channel for the hiring team to review, ensuring no resumes get missed and all feedback follows the same structure. Why it’s useful: - Saves hiring team hours of manual resume review. - Ensures consistent, bias-reduced evaluations. - Instantly alerts the hiring team in Slack, streamlining decision-making. - Adaptable to multiple job roles and folders.