So I was drowning in unqualified leads (classic B2B SaaS problem) and built this workflow that's been an absolute game-changer. Sharing because I wish someone had shown me this 6 months ago. The Problem: We were spending HOURS researching every lead that filled out our form, only to find out 70% weren't even in our target industries. Total waste of time. The Solution: A fully automated lead qualifier using n8n that does all the research and qualification in under 2 minutes. Here's How It Works: 1. Lead fills out Typeform → Basic info + LinkedIn URL 2. Apify scrapes their LinkedIn profile → Gets their experience and current company 3. Scrapes their company's LinkedIn page → Industry, company size, etc. 4. Crawls their website → Extracts business description 5. AI analyzes everything → Determines if they're in Lead Gen/Sales/Marketing SaaS 6. Auto-updates Google Sheets → With qualification status + reasoning. The Magic: I'm using Google Gemini with a super specific prompt that ONLY qualifies leads in our target industries. No more "maybe" leads - it's either YES (1) or NO (0). Tech Stack (all free/cheap): - n8n (self-hosted) - Typeform - Apify (3 different scrapers) - Google Gemini API - Google Sheets - LangChain for the AI agent Results After 1 Month: - ⏱️ 20+ hours saved weekly - 🎯 100% of qualified leads are actually relevant - 📈 Sales team closing rate up 35% (they only talk to qualified leads now) - 😴 Runs 24/7 while I sleep

This advanced lead generation workflow monitors Reddit's r/n8n subreddit for potential business opportunities. It retrieves the latest posts and uses a sophisticated AI-powered lead qualification system to identify high-value prospects for AI automation services. The workflow analyzes each post against a detailed target persona (AI Automation Specialists) and evaluates factors like persona alignment, opportunity type, and engagement potential. Posts are classified into categories such as Support Requests, Solution Inquiries, Tool Comparisons, and Feature Requests. The AI assigns priority levels and extracts relevant resource links. All analyzed data is automatically appended to a Google Sheets spreadsheet for lead management, including the author information, post content, lead classification, priority, reasoning, and direct links to the Reddit posts. Key Components: - Reddit API integration with OAuth2 - AI-powered lead qualification engine - Complex JSON parsing and error handling - Google Sheets integration for lead tracking - Batch processing for multiple posts - Multi-criteria evaluation framework - Resource extraction capabilities Use Cases: - Automated lead generation for freelancers - Business development for service providers - Community engagement monitoring - Competitive intelligence gathering - Market research automation - Sales pipeline feeding - Consultant opportunity identification - SaaS customer acquisition

This workflow creates a custom conversational AI chatbot called "Mora AI" that can be integrated into any application via webhook. The chatbot is built on Google's Gemini model but is branded as being developed by "Mohan at MagicTeams." The workflow accepts POST requests containing chat messages and maintains conversation history through a memory buffer system that keeps the last 10 exchanges. It processes incoming messages, manages conversation context, and generates intelligent responses while maintaining a specific personality and following strict guidelines about not revealing its underlying AI model. The chatbot can handle various topics including technology, science, personal advice, and code-related queries. Key Components: - Webhook endpoint for chat integration - Conversation history management - Memory buffer for context retention - Custom AI personality configuration - Response generation with system prompts - Session management capabilities Use Cases: - Customer support chatbots - Website virtual assistants - Internal company help bots - Educational tutoring systems - Technical documentation assistants - Interactive FAQ systems - White-label AI solutions for businesses

This workflow automates the creation of personalized LinkedIn icebreaker messages for HR professionals and recruiters. It reads data from a Google Sheets spreadsheet containing recruiter information (name, company, role, email). The workflow processes each row individually using a batch processing loop. For each recruiter, it cleans the company name by removing common suffixes (Ltd., Inc., etc.) and then uses an AI Agent to generate a personalized icebreaker message. The AI is instructed to research the company online and create an engaging, professional message that references specific company details, expresses interest in IT/software opportunities, and ends with "Figured I'd reach out." The generated messages are written back to the spreadsheet, updating each row with its corresponding icebreaker. Key Components: - Google Sheets integration for data management - Batch processing with loop functionality - Company name normalization - AI-powered message generation with web research capability - Structured output parsing - Spreadsheet update functionality Use Cases: - Job search automation - Professional networking at scale - Recruitment outreach campaigns - Business development messaging - Sales prospecting - Conference attendee outreach - Alumni networking initiatives

This sophisticated email automation workflow monitors a Gmail inbox for unread emails and automatically classifies them into five categories: Course Requests, Consulting, Payments, Miscellaneous, and College Mails. The workflow polls Gmail every minute for new unread messages. It first extracts the sender's name using an AI-powered information extractor, then determines whether to use a personalized greeting (if the name is found) or a generic greeting. The email content is analyzed by a text classifier that categorizes the email based on its content. Depending on the classification, the workflow applies appropriate Gmail labels and sends tailored auto-responses. Each category has its own response template that maintains professionalism while addressing the specific inquiry type. Key Components: - Gmail trigger with polling mechanism - AI-powered sender name extraction - Conditional logic for personalization - Multi-category text classification - Automated label application - Category-specific auto-responses - Integration with Google Gemini for AI processing Use Cases: - Customer service automation - Lead qualification and routing - Educational institution communication management - Freelancer inquiry management - Support ticket pre-classification - Business development automation - Reducing response time for common inquiries