Client: "We have 50,000 mixed documents in one folder. Need them sorted by type. Budget: Whatever it takes." THE DIGITAL HOARDER SITUATION - 15 years of accumulated documents - Insurance claims, medical records, contracts, invoices, receipts - No naming convention - No folder structure - Previous attempts failed: "Too complex for interns" THE CHAOS CLASSIFICATION n8n WORKFLOW (12 nodes) Document ingestion (Nodes 1-2): - Batch folder processing - File format detection and validation Content analysis (Nodes 3-6): - Full document text extraction - Keyword density analysis - Layout pattern recognition - Financial data detection Classification engine (Nodes 7-9): - Machine learning document categorization - Confidence scoring per category - Multi-label classification (document can be multiple types) Organization system (Nodes 10-12): - Automated folder creation - File naming standardization - Duplicate detection and handling - Processing log and reporting BUILD AND DEPLOYMENT Development time: 6 hours Testing with sample chaos: 4 hours Full deployment: 2 hours Client training: 1 hour THE SORTING RESULTS 50,000 documents processed in 8 hours: - Medical records: 12,400 documents - Insurance claims: 8,700 documents - Contracts: 6,200 documents - Invoices: 9,800 documents - Receipts: 7,300 documents - Miscellaneous: 5,600 documents Classification accuracy: 94.7% Manual review needed: 2,650 documents (5.3%) Client satisfaction: "Life-changing" THE RECURRING OPPORTUNITY Monthly document volume: 2,000+ new mixed documents Ongoing sorting service: $2,100/month Processing time: 30 minutes monthly Profit margin: 96% THE TEMPLATE EXPANSION "Document Chaos Classifier" deployed for: - Law firms with discovery document dumps - Accounting firms with client record archives - Real estate companies with property files - Medical practices with patient record conversions Deployment variations: - Legal: Contract vs discovery vs correspondence - Accounting: Receipts vs invoices vs tax documents

Hey folks 👋 just wanna share a small lifehack — haven’t seen anyone post this combo here yet. I’ve been using **Serper.dev** (Google Search API) with **n8n** to automate Google dorks, and it’s super useful for **enriching company or key people profiles** — without having to do manual searching. 💡 The cool part is it’s **not limited to LinkedIn or profiles only** — this is just a sample use case. You can point it to any type of public data indexed by Google (like GitHub, company sites, job pages, etc.). How it works: - Run your Google dork → Serper.dev returns clean results - n8n grabs those links → fetches public fields you want - Save it to Google Sheets or CSV Example dorks: - `site:linkedin.com/in "software engineer" "Manila"` - `site:github.com "data analyst" "Philippines"` - `site:crunchbase.com "Your Company Name"` Mini n8n flow: - HTTP Request → Serper.dev (`q` = your dork + `serper_api_key`) - Set/Function → extract `organic_results[].link` - SplitInBatches (size 1) → HTTP Request (fetch HTML) - HTML (Extract) → pick your fields (title, headline, location, etc.) - Output → Google Sheets / CSV ⚡ Pros: - Easy setup, no scraping headaches - Works with any public Google-indexed data - Great for enrichment or research tasks ⚠️ Cons: - Limited to **public** indexed pages only - HTML structure may change → extraction needs maintenance - Add delays and respect ToS / robots.txt to stay safe Links: - Serper.dev → https://serper.dev/ - n8n → https://n8n.io/ If anyone’s interested, I can drop a ready-to-import n8n JSON for this setup.