Finance client. 320 vendor invoices monthly. Manual processing consuming 240 hours. Built automated invoice processor. THE CLIENT'S MANUAL PROCESS: Invoices arriving daily. Finance downloads PDFs. Types invoice number, vendor, date, amount. Checks vendor approval manually. Opens database. Searches by name. Reviews previous invoices for duplicates. Validates purchase orders. Cross-references procurement. Flags missing POs when remembered. Categories line items one by one. Assigns office supplies, software, marketing. Checks budgets. Notes warnings inconsistently. Inspects for fraud manually. Round numbers, rushed terms, missing info. Detection varies by experience. Calculates discounts when time permits. ROI estimated informally. Payment timing without analysis. Routes for approval informally. Manager for unknowns. Director for limits. Auto-approve inconsistent. 45 minutes per invoice. 320 monthly. 240 hours weekly. Audit problems - 67 duplicates ($47,200). 142 missed discounts ($28,400). 89 budget overruns discovered late. 23 fraud cases missed. 84% accuracy. THE AUTOMATION: 11-node workflow: Gmail Trigger → Get Invoice → Prepare Binary → PDF Vector Extract → Duplicate Check → Vendor Validation → Auto-Categorize → Fraud Detection → Payment Optimization → Sheets Log → Approval Router → [Auto] OR [Manager/Director] EXTRACTION: Invoice number, vendor details (name, address, tax ID), dates, PO number, line items (description, quantity, price, amount, category), totals, terms DUPLICATE DETECTION: Multi-method fingerprinting - primary (vendor + invoice#), secondary (vendor + amount + date), fuzzy (vendor substring + numeric invoice). Cross-checks complete historical database. VENDOR VALIDATION: Approved vendor database with spending limits, risk scores, auto-approve thresholds. Automatic lookup, limit verification, approval routing. AUTO-CATEGORIZATION: Keyword-based category assignment for line items. Automatic budget aggregation. Real-time overspend warnings.

Built the same automation 8 times last month. Total time: 3.2 hours. Total revenue: $6,200. THE OPPORTUNITY: Every business receives invoices Most process them manually Same pain, different industries Perfect template opportunity THE TECH STACK: Make.com - Workflow automation ($9-29/month) PDF Vector - Invoice data extraction (free-$25/month) Google Sheets - Temporary staging Client's accounting software - Final destination Total cost per client: $9-34/month THE TEMPLATE FLOW: STEP 1: Invoice arrives via email Gmail watches specific folder Triggers when new invoice arrives Passes to document parser STEP 2: Data extraction PDF Vector extracts all invoice fields Vendor name, invoice number, date Line items, subtotal, tax, total Output: Clean structured data STEP 3: Validation Check for required fields Flag if anything missing Send to client for review if incomplete STEP 4: Integration Push to QuickBooks/Xero/their system Create vendor if new Match to purchase order if exists Mark as pending approval STEP 5: Notification Slack message: "Invoice processed" Include vendor, amount, status Client approves or rejects Total automation time: 90 seconds per invoice THE 8 CLIENTS LAST MONTH: Restaurant (42 invoices/month) - $1,800 setup Construction (65 invoices/month) - $2,200 setup Retail store (28 invoices/month) - $1,400 setup Consulting firm (18 invoices/month) - $1,200 setup Law office (35 invoices/month) - $1,800 setup Real estate (22 invoices/month) - $1,400 setup Gym (15 invoices/month) - $1,200 setup Dental practice (31 invoices/month) - $2,200 setup Total setup revenue: $13,200 Template deployment time per client: 24 minutes average THE PRICING FORMULA: Base price: $1,200 Add $200 for 30+ invoices monthly Add $300 for multiple locations Add $400 for custom approval workflows Add $500 for multiple accounting systems Monthly maintenance: $120-180 THE VALUE PROPOSITION: For 30 invoices monthly at 15 minutes each:

When I started building document automation workflows, I needed to stay current on research. Academic papers on AI extraction methods. New models for document understanding. Research on accuracy improvements. Problem: Papers publish constantly. ArXiv, Google Scholar, PubMed, Semantic Scholar. Checking manually meant 2-3 hours daily just to stay current. I was spending 15 hours weekly on research DISCOVERY. Not reading papers. Just FINDING them. THE SOLUTION: Built n8n workflow that monitors academic databases automatically: WHAT IT DOES: Runs every morning at 8 AM. Searches ArXiv, Google Scholar, and Semantic Scholar for papers on "AI automation document processing" published in last 24 hours. Filters and validates each paper (must have abstract, PDF, author info). Limits to top 5 papers by citation count. Creates Notion database entry with full metadata. Sends Slack morning digest before I start work. THE TECHNICAL SETUP: Uses schedule trigger (daily). Academic search API that queries multiple databases simultaneously (PDF Vector has this built-in, but you can also use individual database APIs). Date filter code node to ensure papers are genuinely from yesterday. Validation filter to remove incomplete entries. Notion integration for knowledge base. Slack for morning notifications. Took 45 minutes to build. Most time spent on validation logic to handle messy database responses. THE RESULTS: From 15 hours weekly search time to ZERO search time. Wake up each morning, check Slack, see curated list of 5 papers. Takes 5 minutes to scan abstracts. If something's relevant, full PDF is linked in Notion. Been running 4 months. Notion database now has 480+ papers with full searchable metadata. Found 3 papers that directly improved client workflows. Actually reading MORE papers now because discovery friction removed. TEMPLATES: If you're building research monitoring workflows, PDF Vector has pre-built academic search functionality that works with n8n, Make, and Zapier.

Goal: Determine outreach/marketing schedule within current multi-focus opportunity. Blocker: Nothing at the moment. Need: Nothing at the moment.

I’m sure I’m not the first one realizing this, but the more different types of people you work with, the more you start to see who is a good fit and who isn’t. The perfect-fit client will be so happy with the results that they’ll come back for more. So a $100 client turns into $1000 for me instead. Also, offering things for free is a must. I call it the pizza effect. If you’ve ever been to a pizzeria in Sweden, the guys working behind the counter will joke with you and take care of you in a way that makes you want to go back. At the end of the day, I’ve noticed that some of my older clients come back because they enjoy working with me and because, at times, I’ll offer free services and handle things for them. I know most people hate offering free things, but it’s a huge unlock if you can do it. I worked for free for 3 months and got a $1500 contract afterward, plus shares in a company. If you’re the type of person who can overdeliver while working for free, and you’ve done it before, keep doing it. Besides that I'm in a perfect loop of do more > get better > client gets happy > clients come back and order more