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Who this is for This workflow is for digital marketing agencies or sales teams who want to automatically find business leads based on industry & location, gather their contact details, and send personalized cold emails — all from one form submission. --- What this workflow does This workflow starts every time someone submits the Lead Machine Form. It then: Scrapes business data (company name, website, phone, address, category) using Apify based on business type & location. Extracts the best email address from each business website using Google Gemini AI. Stores valid leads in Google Sheets. Generates cold email content (subject + body) with AI based on your preferred tone (Friendly, Professional, Simple). Sends the cold email via Gmail. Updates the sheet with send status & timestamp. --- Setup To set this workflow up: 1. Form Trigger – Customize the “Lead Machine” form fields if needed (Business Type, Location, Lead Number, Email Style). 2. Apify API – Add your Apify Actor Endpoint URL in the HTTP Request node. 3. Google Gemini – Add credentials for extracting email addresses. 4. Google Sheets – Connect your sheet for storing leads & email status. 5. OpenAI – Add your credentials for cold email generation. 6. Gmail – Connect your Gmail account for sending cold emails. --- How to customize this workflow to your needs Change the AI email prompt to reflect your brand’s voice and offer. Add filters to only target leads that meet specific criteria (e.g., website must exist, email must be verified). Modify the Google Sheets structure to track extra info like “Follow-up Date” or “Lead Source”. Switch Gmail to another email provider if preferred. ---
by Rakin JakariaUse the n8n Data Tables feature to store, retrieve, and analyze survey results — then let OpenAI automatically recommend the most relevant course for each respondent. --- What this workflow does This workflow demonstrates how to use n8n’s built-in Data Tables to create an internal recommendation system powered by AI. It: Collects survey responses through a Form Trigger Saves responses to a Data Table called Survey Responses Fetches a list of available courses from another Data Table called Courses Passes both Data Tables into an OpenAI Chat Agent, which selects the most relevant course Returns a structured recommendation with: course: the course title reasoning: why it was selected > Trigger: Form submission (manual or public link) --- Who it’s for Perfect for educators, training managers, or anyone wanting to use n8n Data Tables as a lightweight internal database — ideal for AI-driven recommendations, onboarding workflows, or content personalization. --- How to set it up 1 Create your n8n Data Tables This workflow uses two Data Tables — both created directly inside n8n. Table 1: Survey Responses Columns: Name Q1 — Where did you learn about n8n? Q2 — What is your experience with n8n? Q3 — What kind of automations do you need help with? To create: 1. Add a Data Table node to your workflow. 2. From the list, click “Create New Data Table.” 3. Name it Survey Responses and add the columns above. --- Table 2: Courses Columns: Course Description To create: 1. Add another Data Table node. 2. Click “Create New Data Table.” 3. Name it Courses and create the columns above. 4. Copy course data from this Google Sheet: https://docs.google.com/spreadsheets/d/1Y0Q0CnqN0w47c5nCpbA1O3sn0mQaKXPhql2Bc1UeiFY/edit?usp=sharing This Courses Data Table is where you’ll store all available learning paths or programs for the AI to compare against survey inputs. --- 2 Connect OpenAI 1. Go to OpenAI Platform 2. Create an API key 3. In n8n, open Credentials → OpenAI API and paste your key 4. The workflow uses the gpt-4.1-mini model via the LangChain integration --- Key Nodes Used | Node | Purpose | n8n Feature | |------|----------|-------------| | Form Trigger | Collect survey responses | Forms | | Data Table (Upsert) | Stores results in Survey Responses | Data Tables | | Data Table (Get) | Retrieves Courses | Data Tables | | Aggregate + Set | Combines and formats table data | Core nodes | | OpenAI Chat Model (LangChain Agent) | Analyzes responses and courses | AI | | Structured Output Parser | Returns structured JSON output | LangChain | --- Tips for customization Add more Data Table columns (e.g., email, department, experience years) Use another Data Table to store AI recommendations or performance results Modify the Agent system message to customize how AI chooses courses Send recommendations via Email, Slack, or Google Sheets --- Why Data Tables? This workflow shows how n8n’s Data Tables can act as your internal database: Create and manage tables directly inside n8n No external integrations needed Store structured data for AI prompts Share tables across multiple workflows All user data and course content are stored securely and natively in n8n Cloud or Self-Hosted environments. --- Contact Need help customizing this (e.g., expanding Data Tables, connecting multiple surveys, or automating follow-ups)? robert@ynteractive.com Robert Breen ynteractive.com
by Robert BreenScrape physician profiles from BrowserAct to Google Sheets This workflow automates the process of building a targeted database of healthcare providers by scraping physician details for a specific location and syncing them to your records. It leverages BrowserAct to extract data from healthcare directories and ensures your database stays clean by preventing duplicate entries. Target Audience Medical recruiters, pharmaceutical sales representatives, lead generation specialists, and healthcare data analysts. How it works 1. Define Location: The workflow starts by setting the target Location and State in a Set node. 2. Scrape Data: A BrowserAct node executes a task (using the "Physician Profile Enricher" template) to search a healthcare directory (e.g., Healow) for doctors matching the criteria. 3. Parse JSON: A Code node takes the raw string output from the scraper and parses it into individual JSON objects. 4. Update Database: The workflow uses a Google Sheets node to append new records or update existing ones based on the physician's name, preventing duplicates. 5. Notify Team: A Slack node sends a message to a specific channel to confirm the batch job has finished successfully. How to set up 1. Configure Credentials: Connect your BrowserAct, Google Sheets, and Slack accounts in n8n. 2. Prepare BrowserAct: Ensure the Physician Profile Enricher template is saved in your BrowserAct account. 3. Setup Google Sheet: Create a new Google Sheet with the required headers (listed below). 4. Select Spreadsheet: Open the Google Sheets node and select your newly created file and sheet. 5. Set Variables: Open the Define Location node and input your target Location (City) and State. 6. Configure Notification: Open the Slack node and select the channel where you want to receive alerts. Google Sheet Headers To use this workflow, create a Google Sheet with the following headers: Name Specialty Address Requirements BrowserAct account with the Physician Profile Enricher template. Google Sheets account. Slack account. How to customize the workflow 1. Change the Data Source: Modify the BrowserAct template to scrape a different directory (e.g., Zocdoc or WebMD) and update the Google Sheet columns accordingly. 2. Switch Notifications: Replace the Slack node with a Microsoft Teams, Discord, or Email node to suit your team's communication preferences. 3. Enrich Data: Add an AI Agent node after the Code node to format addresses or research the specific clinics listed. Need Help? How to Find Your BrowserAct API Key & Workflow ID How to Connect n8n to BrowserAct How to Use & Customize BrowserAct Templates --- Workflow Guidance and Showcase Video #### Automate Medical Lead Gen: Scrape Healow to Google Sheets & Slack
by Madame AI Team | Kai