Medical clinic. Patient fills intake form. Someone types it all into system. Allergy information buried on page 3. Built intake processor. Critical allergies trigger instant alert. Different Slack channel. Staff knows immediately. THE HEALTHCARE DOCUMENTATION RISK: Penicillin allergy on form. Nurse doesn't see it until patient is in room. Doctor almost prescribes it. Near miss. Documentation existed. Wasn't surfaced fast enough. THE DISCOVERY: Document extraction pulls complete patient information. Code checks allergies against critical list. Different routing based on severity. Critical allergies go to urgent channel. Everyone sees it. THE WORKFLOW: Google Drive trigger watches intake forms → Download PDF → Document extraction pulls patient info, insurance, medical history, medications, allergies → Code processes and checks for critical allergens → Sheets creates patient record → IF checks critical allergy → TRUE: Slack urgent channel alert → FALSE: Slack routine intake notification. 8 nodes. HIPAA-aware design. THE CRITICAL ALLERGY DETECTION: Code maintains critical allergen array: penicillin, sulfa, latex, iodine, aspirin, nsaids. Patient allergy list checked against array. Any match triggers urgent routing. Different Slack channels: #critical-alerts vs #patient-intake. Staff knows the difference. THE DATA EXTRACTION: JSON Schema captures: Patient info (name, DOB, contact), insurance details, medical history, current medications with dosages, allergies with reaction severity, emergency contact, reason for visit. Code calculates age from DOB. Formats medications list. Flags "NKDA" if no allergies. THE TRANSFORMATION: Before: Manual data entry. Allergy information buried. Near misses happen. After: Automatic extraction. Critical allergies surfaced instantly. Staff prepared. THE NUMBERS: 200 intake forms monthly 6 critical allergens monitored Instant alert on match Data entry: 15 minutes → 2 minutes review HIPAA NOTE: Self-hosted n8n recommended for PHI.

Industry research shows 50%+ of B2B companies will have automated invoice processing by 2025. You're either positioning early or scrambling late. No "on time" anymore. THE ADOPTION CURVE: 2020: 12% of B2B automated 2022: 23% automated 2024: 38% automated 2025: 50%+ projected We crossed the chasm. Early majority is implementing NOW. THE CLIENT WHO WAITED TOO LONG: Met them in 2023: "We'll consider automation next year. Not a priority right now." Their competitors automated in 2023. By late 2024: - Lost 2 major accounts (competitors processed orders 3X faster) - Couldn't hire qualified AP staff (nobody wants manual data entry jobs) - Vendor relationships damaged (consistent late payments) - Scaling impossible (hiring couldn't keep up with growth) Finally implemented December 2024. 18 months behind competitors. Market share already lost to faster processors. THE POSITIONING THAT WINS: Before: "Would you like to automate your invoice processing?" Response: "We'll think about it." After: "50% of your industry will have automated invoices by end of 2025. Your competitors are implementing now. Want to be ahead of the curve or behind it?" Response: "Let's discuss implementation timeline." USING INEVITABILITY IN SALES: Don't sell automation as optional upgrade. Position as inevitable industry shift they must navigate. "The market is moving to automated invoice processing. I help companies implement before their competitors close the gap, not after." CURRENT PIPELINE: - Prospects actively evaluating: 8 - Primary buying trigger: Competitor pressure (5), Staff retention crisis (2), Scaling requirements (1) - Average deal: $5,800 setup + $480/month - Close rate with inevitability positioning: 62% - Close rate with features/benefits positioning: 38% WHAT HAPPENS TO LATE ADOPTERS: Competitive disadvantage: - Slower order processing - Higher error rates - Can't scale without hiring Talent retention crisis: - Can't attract qualified staff - High turnover in AP roles

Job posting goes live. 127 resumes arrive. HR has 3 hours to review. Built resume scorer. Weighted algorithm. 127 ranked automatically. HR reviewed top 20 only. Hired candidate #3. THE HIRING BOTTLENECK: Every resume opened manually. Read through. Gut feeling score. Next one. Inconsistent. Slow. Biased. Good candidates buried at resume #87. Never seen. Lost to competitor. THE DISCOVERY: Document extraction pulls structured candidate data. Code applies weighted scoring algorithm. Candidates ranked objectively. Top scores get interviews. Others get polite auto-reply. THE WORKFLOW: Gmail trigger catches applications → Get message with resume → Code renames binary → Document extraction pulls name, experience, skills, education → Code applies scoring algorithm → Sheets adds to ATS tracker → Gmail sends auto-reply → Slack notifies HR with score and status. 8 nodes. Objective candidate ranking. THE SCORING ALGORITHM: 100 points total, weighted: Experience (40 pts): 7+ years = 40, 5+ = 35, 3+ = 25, 1+ = 15 Education (30 pts): PhD = 30, Masters = 25, Bachelors = 20 Skills match (30 pts): 3 pts per matched skill (max 10 checked) Customizable. Required skills array in code node. Change for each position. THE STATUS ROUTING: Score 75+ → Schedule Interview Score 50-74 → Review Further Score <50 → Pass HR sees status immediately. Focuses time on promising candidates. THE TRANSFORMATION: Before: 3 hours reviewing 127 resumes. Fatigue affects later reviews. Inconsistent criteria. After: 20 minutes reviewing top 20. Objective scoring. Better candidates identified. THE NUMBERS: 127 candidates scored Top 20 reviewed by HR Hired candidate ranked #3 Review time: 3 hours → 20 minutes Template in n8n and All workflows in Github What skills would you weight highest for your next hire?

Commercial real estate firm. 340 lease agreements. Manual attorney review. Review time: 12-24 hours per lease at $280/hour. Missed one clause on page 47. Legal dispute cost: $180,000. THE INCIDENT: Retail lease. 62 pages of standard commercial terms. Buried clause, page 47, section 8.3.c: "Landlord responsible for HVAC system maintenance and replacement." Attorney missed it during 18-hour manual review. HVAC failed 8 months later. Tenant claimed breach of landlord obligations. Legal dispute: - Attorney fees: $94,000 - Settlement payment: $72,000 - Lost rent during dispute: $14,000 - Total damage: $180,000 The clause was there. Standard language. Just buried. Missed. THE AUTOMATION SOLUTION: Lease compliance analysis system checking: - Insurance requirements (limits, providers, expiration dates) - Maintenance obligations (who's responsible for what systems) - Critical dates (renewal options, termination windows) - Financial terms (rent escalations, CAM charges) - Non-standard clauses (compare to company policy baseline) Processing: 2-4 minutes per 60-page lease THE RESULTS: Review time: 12-24 hours → 2-4 minutes Cost per review: $3,360-$6,720 → $15 Annual attorney time saved: $1.2M+ Compliance issues caught: 34 in first 6 months REAL EXAMPLE FROM FIRST 90 DAYS: New retail lease. 62 pages. Automation flagged: - Insurance limits $1M (company minimum: $2M) - Non-standard CAM reconciliation (tenant-favorable language) - Maintenance ambiguity on exterior signage responsibility Caught before signing. Renegotiated all three. Attorney reaction: "Would have taken me 18 hours to find these. System did it in 3 minutes." Prevented dispute estimate: $40,000+ THE 6 COMPLIANCE CHECKS: 1. Insurance coverage matches requirements 2. Maintenance responsibilities clearly defined 3. Financial terms align with company policy 4. Critical dates properly structured 5. Termination clauses protect landlord 6. Non-standard language flagged for review CURRENT REAL ESTATE VERTICAL:

PhD student problem. Papers pile up. Read one. Forget where you put it. Forget what it said. Built analyzer workflow. Papers go in folder. Come out tagged, summarized, with related work already found. THE ACADEMIC CHAOS: Download paper. Read abstract. Save somewhere. Forget filename. Search folder. Can't find it. Need citation? Which paper had that methodology? Manual search through 400 PDFs. THE DISCOVERY: Document extraction pulls complete paper metadata. Academic search finds related work automatically. Notion page created with summary. Literature database builds itself. THE WORKFLOW: Google Drive trigger watches papers folder → Download paper → Document extraction pulls title, authors, abstract, methodology, findings, keywords → Academic search finds related papers using keywords → Code compiles analysis and generates citation → Sheets logs to literature database → Notion creates summary page. 7 nodes. Automatic literature organization. THE COMPREHENSIVE EXTRACTION: JSON Schema captures everything: Title, authors array, journal, year, DOI, abstract, keywords, study type (Experimental, Review, Meta-analysis, etc.), methodology, main findings, limitations, future research suggestions. Code generates APA citation automatically. Ready to paste into paper. THE RELATED WORK MAGIC: Academic search uses extracted keywords. Queries Semantic Scholar and PubMed. Returns related papers with citation counts. You uploaded one paper. Got back five related ones you didn't know existed. THE TRANSFORMATION: Before: 20 minutes per paper just organizing. Related work search takes hours. Citation formatting manual. After: 30 seconds per paper processed. Related work automatic. Citations generated. THE NUMBERS: 400+ papers in database APA citations auto-generated Related work found automatically Notion summary under 2000 chars (platform limit) Template in n8n and All workflows in Github