This year, I’ve worked with paying clients to design and deploy AI voice agents, advanced GoHighLevel workflows, and structured automation systems using n8n. My focus is simple: build reliable systems that scale. I’m now looking to partner on additional projects. If you need strong execution on automation infrastructure, let’s connect.

Laptop acting up. Warranty? No idea. Checked tracker. 14 days remaining. Filed claim. Got replacement. Built warranty tracker. Every warranty organized. Expiration alerts automatic. Zero claims missed. THE WARRANTY FORGOTTEN PROBLEM: Buy product. Get warranty. File receipt somewhere. Product breaks. Search everywhere. Warranty expired last month. Average household has 15-20 active warranties. Average person tracks zero systematically. THE DISCOVERY: Document extraction pulls warranty terms. Code calculates expiration date. Conditional alert triggers when approaching deadline. 30-day advance warning. Every warranty. No surprises. THE WORKFLOW: Google Drive trigger watches warranty folder → Download document → Document extraction pulls product details, purchase date, warranty duration, coverage terms → Code calculates expiration date and days remaining → Sheets logs to warranty tracker → IF checks if expiring within 30 days → Alert Slack when expiring soon. 7 nodes. Warranty money protected. THE EXPIRATION LOGIC: Code calculates: Purchase date + Warranty months = Expiration date. Status determined by days remaining: - Negative: Expired - Under 30: Expiring Soon (triggers alert) - Under 90: Active - Expires Soon - Over 90: Active THE CONDITIONAL ALERT: Only Expiring Soon status triggers Slack. No spam for healthy warranties. Alert when action needed. Product name, days remaining, coverage details included. Decision-ready information. THE TRANSFORMATION: Before: Warranty documents scattered. Expirations forgotten. Claims missed after products break. After: Central tracker. Proactive alerts. Zero expired warranty surprises. THE NUMBERS: 23 warranties tracked $3,200 in claims protected 4 expiring warranties alerted 3 already-expired discovered (lessons learned) Template in n8n and All workflows in Github

Most people try to learn n8n randomly… watch tutorials, copy workflows, and still feel confused. So I created a 14-Week AI Automation Roadmap to help you go from Beginner → Advanced Automation Builder. What’s inside: → 14-Week structured AI automation learning plan → AI fundamentals (LLMs, APIs, JSON, Webhooks, RAG) → Complete n8n foundations (nodes, workflows, setup) → AI integrations (ChatGPT, Claude) → Intermediate automation logic (IF, Switch, loops, error handling) → Advanced AI systems (RAG workflows, AI chains, function calling) → Workflow best practices & scaling methods → Essential tools list (n8n, APIs, Sheets, vector DBs) → 30-Day sprint plan with daily tasks This roadmap shows exactly what to learn and in what order so you don’t waste months jumping between random tutorials. It’s 100% free. Comment ''ROADMAP'' and I’ll send it.

American Bank. 35 million documents. No organization. 275 different document types mixed together. Manual classification would take years. AI did it in 2 weeks. THE DOCUMENT NIGHTMARE: Decades of files: - Loan applications - Bank statements - Tax returns - Asset verification - Compliance filings - Historical records All in shared drives. No naming convention. No searchable index. Compliance audits = nightmare. "Find all 2018 commercial loan docs" = weeks of manual searching. THE 4-NODE WORKFLOW: 1. Scan document folders 2. Convert PDFs and images to text 3. Classify by document type (loan app, tax return, etc.) 4. Extract key metadata (date, account number, dollar amount) 5. Build searchable database THE CHALLENGE: 35 million documents = expensive if you process everything. Solution: Process in batches, prioritize by business need: - Regulatory compliance docs first (immediate audit risk) - Active account documents second - Historical archives last Total processing: 2 weeks for critical docs, 3 months for full archive. THE IMPACT: Before: - Audit request: "All 2020 mortgage applications" - Time to comply: 2-3 weeks manual search - Cost: $18,000 in staff time After: - Same request: 15 minutes database query - Cost: $0 incremental - Audit compliance time: 94% reduction THE MORTGAGE PROCESSING USE CASE: Once classified, built automated loan processing: - Applicant uploads docs - System pulls income, employment, assets - Pre-fills underwriting system - Flags missing documents Loan processing: 30 days → 3 days. THE SALES ANGLE: Don't sell "document classification." Sell "you have 35 million documents you can't find when auditors ask. I make them searchable in 2 weeks." Compliance fear > efficiency desire. 📚 All templates library in Github What business has years of documents they can't search when they need them?

While exploring the latest updates in the AI agent ecosystem, I realized many people mix up these three concepts: • Prompting • MCP Servers • Claude Skills But they actually solve very different problems when building AI systems. A simple way to think about it: Prompting → tells the AI what you want MCP → gives the AI tools to access data Skills → define the workflow or SOP So instead of relying only on prompts, modern AI agents combine all three: Prompt → triggers the task MCP → retrieves the data Skills → execute the workflow That’s when an LLM moves beyond being just a chatbot and starts acting like a real AI teammate. Curious — are you experimenting with Claude Skills or MCP servers yet? Would love to hear what you're building. 🚀