Heads up, on Saturday at midnight, the price of AI Automation Society Plus is increasing. The good news? If you join before then, you’ll lock in the current $94/month rate forever. Here’s what you’ll get immediate access to: ⚡ Agent Zero — a crash course for absolute beginners where you learn the basics in a simple, step-by-step format ⚡ 10 Hours to 10 Seconds — how to decide what to automate so you save yourself (and others) massive amounts of time ⚡n8n Masterclass — Projects, puzzles, and course material to turn you into an n8n wizard ready to sell your solutions ⚡ Unlimited tech support — working on automation and get stuck? Our team will jump in and give you advice on how to fix the problem Plus, you’ll be eligible for our live vibe coding workshops. Every month, we are going to host live workshops where you’ll learn vibe coding with Claude Code, build AI agents, and create real apps, all in real time with expert guidance. And if you go annual? You’ll unlock our premium training: The One Person AI Agency, the exact blueprint to build a lean, high-profit automation business. This alone is worth $1,000. Bottom line: You get instant access to everything above… And your price is locked in forever. Others will pay more while your price never increases. 👉 Click here to lock in your price  I’ll see you inside. Nate

Overview: Managing multiple MSPs across the country, we needed a way to quickly review incoming resumes and decide who we really want to bring in for interviews. To solve this, we built an automated hiring pipeline in n8n that screens resumes in real-time and posts structured candidate assessments directly into Slack. How it works: - Resume Intake via Google Drive – Candidates upload their resumes into specific Drive folders (per company). The workflow automatically detects new files, downloads them, and extracts text from the PDFs. - AI-Powered Screening – The extracted resume text is analyzed by GPT against the role description (in this case, Senior MSP Systems Engineer – Tier III). The AI produces a standardized assessment including candidate name, relevant experience, technical skills, red flags, soft skills, technical level, and a fit score (1–10). - Slack Delivery – The final assessment is automatically pushed into our Slack channel for the hiring team to review, ensuring no resumes get missed and all feedback follows the same structure. Why it’s useful: - Saves hiring team hours of manual resume review. - Ensures consistent, bias-reduced evaluations. - Instantly alerts the hiring team in Slack, streamlining decision-making. - Adaptable to multiple job roles and folders.

In this video, I walk you through a live build of a smart AI-powered resume screening system, all built in n8n with zero code. This system takes in a candidate’s resume, analyzes their strengths and weaknesses, evaluates their risk vs. reward factor, and determines their overall fit for the role. It doesn’t just give you a score, it justifies each ranking with clear reasoning so you understand why the candidate received that rating. Whether you're building internal hiring tools, working with recruiting clients, or just want to see what’s possible with no-code AI, this walkthrough will give you practical insights into combining automation and intelligence for real-world use cases. Watch the full build from start to finish and learn how you can create your own smart screening workflows using nothing but n8n and a few powerful AI nodes.

Hi everyone, I’m building an enterprise-grade AI Resume Screening Agent and could use some advice on streamlining the architecture. I’m looking to solve three specific challenges: 1. Unified Trigger Strategy: - Enterprises receive resumes from fragmented sources: Google Drive uploads, LinkedIn/hiring platforms (API), website career forms (Webhooks), and direct inbound emails (IMAP). - The Challenge: Instead of having 5 different starting points, what is the best practice for "normalizing" these inputs into a single trigger for the AI Agent? 2. Idempotency & De-duplication: - How do you best ensure a resume isn't screened multiple times if it’s submitted through different channels? - The Challenge: I’m considering hashing the file or using a "processed" flag in a vector database. Does anyone have experience with a robust way to handle this at scale without increasing latency? 3. Multi-JD Matching Logic: The enterprise has multiple active job postings. The agent needs to pick the correct Job Description (JD) to screen against. - The Challenge: Should the agent first perform a "Classification" step to assign the resume to a Job ID, or should I be looking at a multi-agent setup where a "Router" sends the resume to a specific sub-agent dedicated to a specific JD? I'd love to hear how you guys are handling the orchestration of these "pre-screening" steps. Thanks!

I would like to start a conversation in the comment section about your experience and best approach to business for AI Agents and Automation selling that work in 2025 that help you make the most money. What kind of work or business niche did you target, how much did they pay you, how's the work going on? Whatever you are open to share :)