Recruiting company. 480 resume submissions annually. Manual screening consuming 200 hours. Built automated parser. Eliminated manual review. THE CLIENT'S MANUAL PROCESS: Every resume manual. Recruiter downloads document. Opens spreadsheet. Reads entire resume carefully. Types candidate name, email, phone, location manually. Reads work history. Counts years of experience manually. Notes each job with dates. Calculates total years. Reads education section. Determines degree level. Scans skills section. Lists technical skills. Assigns score subjectively based on qualifications. Determines candidate status - strong candidate, potential fit, or not a match. Types assessment notes manually. 25 minutes per resume. 480 annually. 200 hours total. Audit problems - 67 candidates scored inconsistently. Same qualifications rated differently by different recruiters or at different times. 89 resumes with incorrect experience calculations from manual counting. 123 strong candidates marked potential due to rushed screening. 45 overlooked candidates with matching skills. THE AUTOMATION SOLUTION: 8-node workflow with scoring automation: Google Drive Trigger → Download → Parse Resume → Score Candidate → Merge Binary → AI Assessment → Log Database → Notify Team Monitors folder. Resume uploaded. Triggers processing. EXTRACTION: Personal - name, email, phone, location, LinkedIn profile Experience - company, position, start date, end date, achievements Education - institution, degree, field of study, graduation date Skills - technical skills list, soft skills, languages, certifications SCORING CALCULATIONS: Experience score (40 points max) - 5+ years = 40pts, 2-5 years = 25pts, 0-2 years = 10pts Education score (30 points max) - PhD = 30pts, Master's = 25pts, Bachelor's = 20pts Skills match score (30 points max) - 3 points per matched required skill, maximum 30pts Total score out of 100. Auto status - 75+ = Strong Candidate, 50-74 = Potential, <50 = Not Match. AI ASSESSMENT:

Sent 47 cold emails using generic template. Got ghosted by 46. One reply: "Not interested." Changed my entire approach. Sent 12 emails using new 3-message sequence. Got 2 clients. $3,000 total. THE OLD WAY (DIDN'T WORK): Subject: "AI Automation Services" Body: Long explanation of what I do Call to action: "Let me know if interested" Problem: Too vague. No urgency. Easy to ignore. THE NEW 3-EMAIL SEQUENCE: EMAIL 1 - THE SPECIFIC PROBLEM: Subject: "Processing 80 invoices monthly?" Body: "Hi [Name], Noticed [Company] handles client billing. Quick question - roughly how many invoices do you process monthly? I ask because I've automated invoice processing for 3 accounting firms. Cuts manual entry time by 85%. Worth a 10-minute conversation? [Your name]" Short. Specific. Question-focused. EMAIL 2 (3 DAYS LATER IF NO RESPONSE) - THE SOCIAL PROOF: Subject: "Re: Processing 80 invoices monthly?" Body: "Hi [Name], Following up on my question about invoice processing. Just finished similar project for [Similar Company]. Their bookkeeper was spending 12 hours weekly on data entry. Now it's automated. Here's what it looks like: [Screenshot of workflow] 10 minutes to show you how this could work for [Their Company]? [Your name]" Visual proof. Similar company mentioned. Low commitment ask. EMAIL 3 (5 DAYS LATER IF STILL NO RESPONSE) - THE BREAKUP: Subject: "Closing the loop" Body: "Hi [Name], Haven't heard back so I'll assume invoice automation isn't a priority right now. If things change, here's my calendar link: [Link] Good luck with [Their Company]! [Your name]" Respectful exit. Keeps door open. Surprisingly, this one gets responses. THE RESULTS: 12 emails sent using this sequence 4 responses (33% response rate vs 2% before) 2 booked calls 2 signed deals ($1,500 and $1,500) Both clients from Email 3 (the breakup). They replied "Wait, let's talk." THE LESSON: Specific problems beat generic services. Questions beat pitches. Persistence with respect works.

Client called me Monday morning. Frustrated tone. "We keep approving purchase orders that break our company rules. Last month alone: three POs over our $10K threshold got approved anyway." Their approval process: someone eyeballs the PO email, decides subjectively if it seems expensive, routes accordingly based on feeling. The problem: "seems expensive" changes by person reviewing and their current mood. THE AUTOMATED VALIDATOR Built n8n workflow enforcing purely objective business rules. Email triggers automatically when PO arrives. Downloads attachment file. Extracts complete vendor details, line items with quantities and prices, financial totals, delivery dates. Validates all calculations with precision. Checks budget threshold enforcement. Routes based solely on data not feelings. Over $10K threshold: Slack approval request with complete financial breakdown Under $10K threshold: auto-approved with confirmation notification Validation fails: rejected with specific errors for correction Every single PO logged to Google Sheets: complete details, validation results, submitter identification, processing timestamp. THE VALIDATION RULES Mathematical precision checking: Line item calculations accurate to one cent? Subtotal equals exact sum of all items? Total equals subtotal plus tax precisely? Any calculation error over $0.01 flagged? Budget enforcement system: Compare final total to $10K threshold Clear binary routing decision Zero subjective human judgment Consistent application every time Date validation preventing problems: Delivery date chronologically after order date? Timeline realistic for delivery logistics? No scheduling conflicts created? THE TRANSFORMATION Before automation implementation: - 15 minutes checking each PO manually - 10 hours monthly across 40 POs processed - Subjective approval decisions varying by person - Three over-budget approvals in one month - No audit trail or tracking system After automation deployment: - 30 seconds automated validation per PO - 20 minutes monthly oversight reviewing exceptions only - Objective threshold enforcement every single time - Zero over-budget approvals in 4 months running - Complete Google Sheets historical tracking

Spent 12 hours building perfect document classification workflow. Multiple AI models. Advanced logic. Posted about it. Demoed it. Zero clients interested. Not one. WHY IT FAILED: I built what I thought was cool. Not what clients actually needed. Document classification = technical problem I enjoyed solving. Invoice processing = boring problem clients desperately want fixed. THE MISTAKE: Got excited about technical complexity instead of business pain. Built for Reddit upvotes. Not for client checkbooks. Client doesn't care about: AI algorithms, multi-model architecture, technical sophistication. Client cares about: "Will this save me 10 hours weekly? How much? When can we start?" THE REALITY CHECK: Week building classification system: 0 interested clients Week building invoice processor: 2 signed clients ($3,300 total) Market speaks clearly. WHAT CLIENTS ACTUALLY PAY FOR: TIME SAVINGS - invoice processing, form extraction, receipt tracking COST REDUCTION - avoiding new hires, reducing errors GROWTH ENABLERS - scaling without proportional headcount FRUSTRATION RELIEF - eliminating tasks they hate Not "impressive technology." THE BORING TRUTH: Invoice processing = boring, simple, sells immediately Document classification = interesting, complex, nobody buys Your ego wants complex. Your bank account needs simple. THE REDIRECT: Took that classification system. Reframed it completely. Before: "AI-powered document classification system with multi-model architecture" After: "Multi-vendor invoice router - automatically sorts invoices by vendor and posts to correct categories. Saves 3 hours weekly." Same technology. Different positioning. Sold to 2 clients within a week. $1,500 each. THE PATTERN: Features nobody asked for = features nobody pays for. Problems people complain about daily = problems people pay to fix. YOUR ACTION PLAN: Stop building cool technology demos. Start solving boring, repetitive problems people openly complain about. Invoice entry. Form processing. Receipt categorization. Document filing.

client sent 20 test invoices. my extraction got 6 right. SIX. tried 3 different approaches: - built-in pdf node: worked on 4 invoices - regex patterns: broke on every layout change - ocr + manual parsing: took 2 hours per invoice format was about to refund the client then someone here mentioned pdf vector. tested it expecting nothing. 18 out of 20 extracted correctly first try. the llm mode just figured out different layouts automatically. same invoices. same workflow. different extraction tool. completely different results. client has no idea how close i came to quitting lol what tool swap saved your project?