Workflow Templates

Who Is This For? This workflow is designed for educators, universities, examination departments, and EdTech institutions that need a faster, smarter, and standardized way to prepare exam question papers. What Problem Does This Solve? Creating balanced, outcome-based question papers can take hours or even days of manual effort. Faculty often struggle to: Ensure syllabus coverage across units Maintain Bloom’s Taxonomy alignment Keep a consistent difficulty balance Format papers in institution-specific templates How it works This workflow automatically generates an exam question paper based on syllabus topics submitted via a form and sends it to the entered email address. Here’s the flow in simple steps: Form Submission – A student or faculty fills out a form with subject code, syllabus topics, and their email. AI Question Generation – The workflow passes the syllabus to AI agents (Part A with 2 Marks, Part B with 13 Marks, and Part C with 14 Marks) to create question sets. The marks and the no. of question generated can be customized according to the convenience. Merging Questions – All AI-generated questions are combined into a single structured document. Format into HTML – The questions are formatted into a clean HTML exam paper (can also be extended to PDF). Send by Email– The formatted exam paper is sent to the user’s email (with option to CC/BCC). Set up steps Connect Accounts Connect your OpenAI (or LLM) credentials for AI-powered question generation. Connect your Gmail (or preferred email service) to send emails. Prepare Form Create an n8n form trigger with required fields: Subject with Code Syllabus for Unit 1, 2, 3… Email to receive the paper Customize Question Generation Modify the AI prompts for Parts A, B, and C to fit your syllabus style (e.g., 2-mark, 13-mark, 14-mark). Format the Exam Paper Adjust the HTML template to match your institution’s exam paper layout. Test & Deploy Submit a test form entry. Check the received email to ensure formatting looks good. Deploy the workflow to production for real usage. Need help customizing? ✉️ Contact Me 💼 LinkedIn

How it works This workflow runs on scheduled weekly and monthly triggers to generate unified marketing performance reports. It processes multiple websites by collecting analytics data, paid ads performance, and CRM leads, then calculates KPIs and insights automatically. The workflow sends structured reports via email and stores historical data in Google Sheets. It ensures consistent reporting without manual effort. Step-by-step Step 1: Trigger & report type detection** Schedule Trigger2 – Triggers the workflow weekly at a predefined time. Schedule Trigger3 – Triggers the workflow monthly at a predefined time. check month and week1 – Identifies whether the run is weekly or monthly and sets flags. Set Websites and Campaings1 – Defines websites, GA4 property IDs, and mapped ad campaigns. Expand Websites1 – Expands the website array into individual website items. Attach Run Flags1 – Attaches weekly or monthly flags to each website record. Step 2: Website & ads data processing** Loop Websites1 – Iterates through each website independently. Get a report – Fetches website traffic and engagement metrics from analytics. Get many campaigns – Retrieves Google Ads campaign data. Fetch Meta Ads – Fetches Meta Ads performance data via API. Filter Google Ads By Website1 – Filters Google Ads campaigns by website. Filter Meta Ads By Website1 – Filters Meta Ads campaigns by website. Merge1 – Merges analytics, Google Ads, and Meta Ads datasets. Build Website Dataset1 – Builds a unified dataset per website. Calculate KPIs & Campaign Insights1 – Calculates spend, CTR, CPA, CPL, conversions, and performance insights. Append or update row in sheet2 – Stores website-level marketing metrics in Google Sheets. Step 2.1: Marketing report generation** Prepare Report Data2 – Combines all website datasets into a unified report object. Switch – Routes execution based on weekly or monthly report type. Send Weekly Marketing report2 – Sends the weekly marketing performance email. Send Monthly Marketing Report2 – Sends the monthly marketing performance email. Step 3: HubSpot lead analysis** Fetch1 – Fetches leads from HubSpot CRM. Filter Hubspot Leads – Filters leads based on weekly or monthly time range. Summarize Hubspot Leads – Aggregates lead status and lifecycle metrics. Prepare Report Data3 – Prepares CRM summary data for reporting. Step 3.1: CRM reporting & storage** Switch3 – Routes CRM reporting by report type. Send Weekly Marketing report3 – Sends the weekly CRM summary email. Send Monthly Marketing Report3 – Sends the monthly CRM summary email. Code in JavaScript1 – Transforms CRM data for storage. Append or update row in sheet3 – Stores CRM lead performance data in Google Sheets. Switch3 – Routes CRM reporting by report type. Send Weekly Marketing report3 – Sends the weekly CRM summary email. Send Monthly Marketing Report3 – Sends the monthly CRM summary email. Code in JavaScript1 – Transforms CRM data for storage. Append or update row in sheet3 – Stores CRM lead performance data in Google Sheets. Why use this? Automates complex weekly and monthly marketing reporting. Unifies website analytics, ad platforms, and CRM data in one flow. Delivers consistent KPI calculations and insights every run. Maintains historical performance logs in Google Sheets. Scales easily across multiple websites and campaigns.

Sona-Powered AI Sales Research & Personalized Email Automation 🎯 Overview Automatically research B2B leads and generate personalized outreach emails by reading prospects from Google Sheets, enriching with company data from Sona Enrich, analyzing insights with AI, and creating custom emails — so you can scale personalized outreach to target accounts. You'll be able to automatically enrich company data for target accounts, use AI to identify pain points and opportunities, generate personalized email copy, and sync everything back to your sheet with ready-to-send Gmail compose links. ✨ What This Workflow Does Smart Lead Processing - Reads leads from Google Sheets and filters unprocessed contacts Deep Company Intelligence - Enriches each lead using Sona's API (industry, tech stack, revenue, employee count, social profiles) AI-Powered Research - GPT-4 analyzes company data to identify pain points, growth opportunities, and personalization hooks Email Generation - Creates 120-150 word personalized emails with curiosity-driven subject lines Automated Sync - Updates Google Sheets with research insights and one-click Gmail compose links 🔥 Key Features Structured AI Output** - Consistent, high-quality research and copy generation Zero Manual Work** - Processes 20-50 leads per hour completely hands-free Email Generation - Creates 120-150 word personalized emails with curiosity-driven subject lines Gmail Integration** - Pre-filled send links for instant outreach Progress Tracking** - Real-time status updates in Google Sheets 💼 Perfect For Sales teams doing cold outreach SDRs needing personalized emails at scale Agencies managing client prospecting Founders building their pipeline 📋 What You'll Need 1. Sona API Key Get yours at sonalabs.com Provides company data enrichment Add to HTTP Request node header: x-api-key: YOUR_KEY 2. OpenAI API Key Get from platform.openai.com Uses GPT-4.1-mini for research and email generation Add credentials in n8n 3. Google Sheets Setup Create a spreadsheet with these columns: Input columns:** Website Domain, Company Name, Contact Name, Email, Industry Status column:** Research Status (leave empty for new leads) Auto-populated:** Pain Points, Key Insight, Email Subject, Email Body, Send Email Link, Generated Date, Sent Status 4. Google Sheets API Enable in Google Cloud Console Set up OAuth2 with spreadsheets permission Add your spreadsheet ID to workflow nodes 🚀 Setup Instructions Import workflow into n8n Add credentials: Sona API key (HTTP Request node) OpenAI API credentials Google Sheets OAuth2 Update spreadsheet ID in all Google Sheets nodes Customize AI prompts (optional) to match your offering Test with 2-3 leads before running full list Execute workflow - it processes leads automatically in batches 📊 Expected Output Each processed lead gets: Pain points** (3-5 identified challenges) Growth opportunities** (2-3 actionable insights) Personalization hooks** (3-4 talking points) Email subject line** (max 8 words, curiosity-driven) Email body** (120-150 words, consultative tone) Gmail compose link** (one-click to send) Fit score** (High/Medium/Low) Processing time: 30-60 seconds per lead 🎓 How It Works Step 1: Data Input & Filtering Reads all leads from Google Sheets and filters out already-processed leads (those with a value in "Research Status" column). Step 2: Company Data Enrichment Updates status to "Pending" in Google Sheets Searches Sona database using domain or email 5-tier smart matching algorithm finds best company match Retrieves firmographic data and technology stack Step 3: AI Company Research GPT-4.1-mini analyzes company data to generate: Specific pain points based on industry, size, tech stack Growth opportunities and market positioning Personalization hooks from company description Recommended outreach tone and CTA One-liner insight for email opening Step 4: Personalized Email Generation AI crafts cold email following best practices: Curiosity-driven subject line (max 8 words) Opens with personalization hook showing research References ONE specific pain point Focuses on tangible outcomes (not product features) Natural CTA without being pushy Professional but conversational tone Step 5: Data Output & Loop Formats all data for Google Sheets Creates Gmail compose link with pre-filled content Updates sheet with complete results Sets status to "Completed" Waits 2 seconds, then processes next lead ⚡ Pro Tips Start small:** Test with 5-10 leads to validate personalization quality Review first emails:** Adjust AI prompts if tone needs calibration Clean your data:** Better input domains = better Sona matches Monitor fit scores:** Focus manual review on High/Medium fits Use status column:** Easily re-run workflow for new leads only Connect CRM:** Use webhooks to push data to Salesforce/HubSpot 🎯 Use Cases Sales Team Automation Process 100+ leads overnight with personalized research and emails ready by morning. Agency Client Work Deliver custom prospecting campaigns with unique emails for each client's target accounts. Founder Outreach Build pipeline systematically with AI-researched, personalized emails at scale. SDR Productivity Give SDRs pre-researched talking points and draft emails to speed up their workflow 10x. 📈 Expected Results Email personalization:** 10x better than templates Time saved:** 5-10 minutes per lead → 30 seconds automated Response rates:** 2-3x higher with AI-researched insights Scalability:** Process 50-100 leads per day hands-free 🔧 Customization Options Change AI model:** Swap GPT-4.1-mini for GPT-4 or other models Adjust email length:** Modify prompt to generate shorter/longer emails Add more enrichment:** Chain additional API calls (Clearbit, Apollo, etc.) Multi-language:** Update prompts for outreach in other languages Custom tone:** Adjust system prompts for industry-specific voice Webhook triggers:** Replace manual trigger with scheduled runs or form submissions 🐛 Troubleshooting No Sona data found? Verify API key is correct Check domain format (remove http://, trailing slashes) Fallback uses first search result if no exact match AI output not formatted correctly? Structured Output Parser ensures valid JSON Check OpenAI API key and model availability Google Sheets not updating? Verify OAuth2 credentials are connected Check spreadsheet ID matches your sheet Ensure column names match exactly (case-sensitive) Rate limits? Sona: 3 second delay between requests (built-in) OpenAI: Adjust batch size or add longer waits Google Sheets: No limit for standard usage 📝 Template Information Category:** Sales & Marketing Difficulty:** Intermediate Setup Time:** 5-10 minutes Run Time:** 30-60 seconds per lead Cost:** Pay-per-use (Sona API + OpenAI tokens) Updated:** December 2025

Customer Support AI Agent for Gmail This n8n template demonstrates how to build an AI-powered customer support workflow that automatically handles incoming Gmail messages, classifies them, finds answers from your knowledge base, and sends a personalized reply. Who’s it for SaaS founders or teams who want to automate customer support. Freelancers and solopreneurs who receive repetitive customer queries. Companies that want to reduce manual email triage and improve response times. How it works / What it does Trigger: A new email arrives in Gmail. Classification: The workflow uses a text classifier to decide whether the email is customer support-related or not. If not, it’s ignored. If yes, it proceeds. AI Agent: Queries a knowledge base (vector database with OpenAI embeddings). Retrieves the most relevant answer. Drafts a reply using AI (OpenAI or Google Gemini model). Post-processing: Labels the email in Gmail for organization. Sends a reply automatically. This ensures that your customers get timely, relevant responses without manual intervention. How to set up Import this template into your n8n account. Connect your Gmail account in the Gmail Trigger, Label, and Reply nodes. Connect your AI model provider (OpenAI or Google Gemini). Configure the knowledge base embeddings (upload your docs/FAQ into the vector database). Activate the workflow — and your AI customer support agent is live! Requirements n8n account. Gmail account (with API access enabled). OpenAI or Google Gemini account for LLM and embeddings. Knowledge base data (FAQ, documentation, or past tickets). Google Drive account for auto update your vector database(with API access enabled). How to customize the workflow Knowledge Base**: Replace or expand with your own company docs, FAQs, or past conversations. Classification Rules**: Train or adjust the classifier to handle more categories (e.g., Sales, Partnership, Technical Support). Reply Style**: Customize AI prompts for tone — professional, casual, or friendly. Labels**: Change Gmail labels to match your workflow (e.g., “Support,” “Sales,” “Priority”). Multi-language**: Add translation steps if your customers speak different languages. This template saves you hours of manual email triage and ensures your customers always get quick, accurate responses.

How it works This workflow sources news from news websites. The information is then passed to an LLM, which processes the article's content. An editor approves or rejects the article. If accepted, the article is first published on the WordPress site and then on the LinkedIn page. Setup Instructions 1. Credentials You'll need to add credentials for the following services in your n8n instance: News API**: A credential for your chosen news provider. LLM**: Your API key for the LLM you want to use. Google OAuth**: For both Gmail and Google Sheets. WordPress OAuth2**: To publish articles via the API. See the WordPress Developer Docs. LinkedIn OAuth2**: To share the post on a company page. 2. Node Configuration Don't forget to: Fetch News (HTTP Request)**: Set the query parameters (keywords, language, etc.) for your news source. Basic LLM Chain: Review and **customize the prompt to match your desired tone, language, and style. Approval request (Gmail)**: Set your email address in the Send To field. HTTP Request WP - Push article**: Replace <site_Id> in the URL with your WordPress Site ID. getImageId (Code Node)**: Update the array with your image IDs from the WordPress Media Library. Create a post (LinkedIn)**: Enter your LinkedIn Organization ID. Append row in sheet (Google Sheets)**: Select your Google Sheet file and the target sheet. All Email Nodes**: Make sure the Send To field is your email.

How it works This workflow creates a sophisticated, self-improving customer support system that automatically handles incoming emails. It's designed to answer common questions using an AI-powered knowledge base and, crucially, to learn from human experts when new or complex questions arise, continuously expanding its capabilities. Think of it like having an AI assistant with a smart memory and a human mentor. Here's the step-by-step process: New Email Received: The workflow is triggered whenever a new email arrives in your designated support inbox (via Gmail). Classify Request: An AI model (Google Gemini 2.5 Flash Lite) first classifies the incoming email to ensure it's a genuine support request, filtering out irrelevant messages. Retrieve Knowledge Base: The workflow fetches all existing Question and Answer pairs from your dedicated Google Sheet knowledge base. AI Answer Attempt: A powerful AI model (Google Gemini 2.5 Pro) analyzes the customer's email against the entire knowledge base. It attempts to find a highly relevant answer and drafts a complete HTML email response if successful. Decision Point: An IF node checks if the AI found a confident answer. If Answer Found: The AI-generated HTML response is immediately sent back to the customer via Gmail. If No Answer Found (Human-in-the-Loop): Escalate to Human: The customer's summarized question and original email are forwarded to a human expert (you or your team) via Gmail, requesting their assistance. Human Reply & AI Learning: The workflow waits for the human expert's reply. Once received, another AI model (Google Gemini 2.5 Flash) processes both the original customer question and the expert's reply to distill them into a new, generic, and reusable Question/Answer pair. Update Knowledge Base: This newly created Q&A pair is then automatically added as a new row to your Google Sheet knowledge base, ensuring the system can answer similar questions automatically in the future. Set up steps Setup time: ~10-15 minutes This workflow requires connecting your Gmail and Google Sheets accounts, and obtaining a Google AI API key. Follow these steps carefully: Connect Your Gmail Account: Select the On New Email Received node. Click the Credential dropdown and select + Create New Credential to connect your Gmail account. Grant the necessary permissions. Repeat this for the Send AI Answer and Ask Human for Help nodes, selecting the credential you just created. Connect Your Google Sheets Account: Select the Get Knowledge Base node. Click the Credential dropdown and select + Create New Credential to connect your Google account. Grant the necessary permissions. Repeat this for the Add to Knowledge Base node, selecting the credential you just created. Set up Your Google Sheet Knowledge Base: Create a new Google Sheet in your Google Drive. Rename the first sheet (tab) to QA Database. In the first row of QA Database, add two column headers: Question (in cell A1) and Answer (in cell B1). Go back to the Get Knowledge Base node in n8n. In the Document ID field, select your newly created Google Sheet. Do the same for the Add to Knowledge Base node. Get Your Google AI API Key (for Gemini Models): Visit Google AI Studio at aistudio.google.com/app/apikey. Click "Create API key in new project" and copy the key. In the workflow, go to the Google Gemini 2.5 Pro node, click the Credential dropdown, and select + Create New Credential. Paste your key into the API Key field and Save. Repeat this for the Google Gemini 2.5 Flash Lite and Google Gemini 2.5 Flash nodes, selecting the credential you just created. Configure Human Expert Email: Select the Ask Human for Help node. In the Send To field, replace the placeholder email address with the actual email address of your human expert (e.g., your own email or a team support email). Activate the Workflow: Once all credentials and configurations are set, activate the workflow using the toggle switch at the top right of your n8n canvas. Start Learning! Send a test email to the Gmail account connected to the On New Email Received node. Observe how the AI responds, or how it escalates to your expert email and then learns from the reply. Check your Google Sheet to see new Q&A pairs being added!

Automate Your Life: The Ultimate AI Assistant in Telegram (Powered by Google Gemini) Transform your Telegram messenger into a powerful, multi-modal personal or team assistant. This n8n workflow creates an intelligent agent that can understand text, voice, images, and documents, and take action by connecting to your favorite tools like Google Calendar, Gmail, Todoist, and more. At its core, a powerful Manager Agent, driven by Google Gemini, interprets your requests, orchestrates a team of specialized sub-agents, and delivers a coherent, final response, all while maintaining a persistent memory of your conversations. Key Features 🧠 Intelligent Automation: Uses Google Gemini as a central "Manager Agent" to understand complex requests and delegate tasks to the appropriate tool. 🗣️ Multi-Modal Input: Interact naturally by sending text, voice notes, photos, or documents directly into your Telegram chat. 🔌 Integrated Toolset: Comes pre-configured with agents to manage your memory, tasks, emails, calendar, research, and project sheets. 🗂️ Persistent Memory: Leverages Airtable as a knowledge base, allowing the assistant to save and recall personal details, company information, or past conversations for context-rich interactions. ⚙️ Smart Routing: Automatically detects the type of message you send and routes it through the correct processing pipeline (e.g., voice is transcribed, images are analyzed). 🔄 Conversational Context: Utilizes a window buffer to maintain short-term memory, ensuring follow-up questions and commands are understood within the current conversation. How It Works The Telegram Trigger node acts as the entry point, receiving all incoming messages (text, voice, photo, document). A Switch node intelligently routes the message based on its type: Voice**: The audio file is downloaded and transcribed into text using a voice-to-text service. Photo**: The image is downloaded, converted to a base64 string, and prepared for visual analysis. Document**: The file is routed to a document handler that extracts its text content for processing. Text**: The message is used as-is. A Merge node gathers the processed input into a unified prompt. The Manager Agent receives this prompt. It analyzes the user's intent and orchestrates one or more specialized agents/tools: memory_base (Airtable): For saving and retrieving information from your long-term knowledge base. todo_and_task_manager (Todoist): To create, assign, or check tasks. email_agent (Gmail): To compose, search, or send emails. calendar_agent (Google Calendar): To schedule events or check your agenda. research_agent (Wikipedia/Web Search): To look up information. project_management (Google Sheets): To provide updates on project trackers. After executing the required tasks, the Manager Agent formulates a final response and sends it back to you via the Telegram node. Setup Instructions Follow these steps to get your AI assistant up and running. Telegram Bot: Create a new bot using the BotFather in Telegram to get your Bot Token. In the n8n workflow, configure the Telegram Trigger node's webhook. Add your Bot Token to the credentials in all Telegram nodes. For proactive messages, replace the chatId placeholders with your personal Telegram Chat ID. Google Gemini AI: In the Google Gemini nodes, add your credentials by providing your Google Gemini API key. Airtable Knowledge Base: Set up an Airtable base to act as your assistant's long-term memory. In the memory_base nodes (Airtable nodes), configure the credentials and provide the Base ID and Table ID. Google Workspace APIs: Connect your Google account credentials for Gmail, Google Calendar, and Google Sheets. In the relevant nodes, specify the Document/Sheet IDs you want the assistant to manage. Connect Other Tools: Add your credentials for Todoist and any other integrated tool APIs. Configure Conversational Memory: This workflow is designed for multi-user support. Verify that the Session Key in the "Window Buffer Memory" nodes is correctly set to a unique user identifier from Telegram (e.g., {{ $json.chat.id }}). This ensures conversations from different users are kept separate. Review Schedule Triggers: Check any nodes designed to run on a schedule (e.g., "At a regular time"). Adjust their cron expressions, times, and timezone to fit your needs (e.g., for daily summaries). Test the Workflow: Activate the workflow. Send a text message to your bot (e.g., "Hello!"). Estimated Setup Time 30–60 minutes:** If you already have your API keys, account credentials, and service IDs (like Sheet IDs) ready. 2–3 hours:** For a complete, first-time setup, which includes creating API keys, setting up new spreadsheets or Airtable bases, and configuring detailed permissions.

This template enriches a lead list by analyzing each contact’s LinkedIn activity and auto-generating a single personalized opening line for cold outreach. Drop a spreadsheet into a Google Drive folder → the workflow parses rows, fetches LinkedIn content (recent post or profile), uses an LLM to craft a one-liner, writes the result back to Google Sheets, and sends a Telegram summary. ⸻ Good to know • Works with two paths: • Recent post found → personalize from the latest LinkedIn post. • No recent post → personalize from profile fields (headline, about, current role). • Requires valid Apify credentials for LinkedIn scrapers and LLM keys (Anthropic and/or OpenAI). • Costs depend on the LLM(s) you choose and scraping usage. • Replace all placeholders like [put your token here] and [put your Telegram Bot Chat ID here] before running. • Respect the target platform’s terms of service when scraping LinkedIn data. What this workflow does Trigger (Google Drive) – Watches a specific folder for newly uploaded lead spreadsheets. Download & Parse – Downloads the file and converts it to structured items (first name, last name, company, LinkedIn URL, email, website). Batch Loop – Processes each row individually. Fetch Activity – Calls Apify LinkedIn Profile Posts (latest post) and records current date for recency checks. Recency Check (LLM) – An OpenAI node returns true/false for “post is from the current year.” Branching • If TRUE → AI Agent (Anthropic) crafts a single, natural reference line based on the recent post. • If FALSE → Apify LinkedIn Profile → AI Agent (Anthropic) crafts a one-liner from profile data (headline/about/current role). Write Back (Google Sheets) – Updates the original sheet by matching on email and writing the personalization field. Notify (Telegram) – Sends a brief completion summary with sheet name and link. Requirements • Google Drive & Google Sheets connections • Apify account + token for LinkedIn scrapers • LLM keys: Anthropic (Claude) and/or OpenAI (you can use one or both) • Telegram bot for notifications (bot token + chat ID) How to use Connect credentials for Google, Apify, OpenAI/Anthropic, and Telegram. Set your folder in the Google Drive Trigger to the one where you’ll drop lead sheets. Map sheet columns to the expected headers (e.g., First Name, Last Name, Company Name for Emails, Person Linkedin Url, Email, Website). Replace placeholders ([put your token here], [put your Telegram Bot Chat ID here]) in the respective nodes. Upload a test spreadsheet to the watched folder and run once to validate the flow. Review results in your sheet (new personalization column) and check Telegram for the completion message. Setup Connect credentials - Google Drive/Sheets, Apify, OpenAI and/or Anthropic, Telegram. Configure the Drive trigger - Select the folder where you’ll upload your lead sheets. Map columns - Ensure your sheet has: First Name, Last Name, Company Name for Emails, Person Linkedin Url, Email, Website. Replace placeholders - In HTTP nodes: Bearer [put your token here]. In Telegram node: [put your Telegram Bot Chat ID here] (Optional) Adjust the recency rule - Current logic checks for current-year posts; change the prompt if you prefer 30-day windows. How to use Upload a test spreadsheet to the watched Drive folder. Execute the workflow once to validate. Open your Google Sheet to see the new personalization column populated. Check Telegram for the completion summary. Customizing this template • Data sources: Add company news, website content, or X/Twitter as fallback signals. • LLM choices: Use only Anthropic or only OpenAI; tweak temperature for tone. • Destinations: Write to a CRM (HubSpot/Salesforce/Airtable) instead of Sheets. • Notifications: Swap Telegram for Slack/Email/Discord. Who it’s for • Sales & SDR teams needing authentic, scalable personalization for cold outreach. • Lead gen agencies enriching spreadsheets with ready-to-use openers. • Marketing & growth teams improving reply rates by referencing real prospect activity. Limitations & compliance • LinkedIn scraping may be rate-limited or blocked; follow platform ToS and local laws. • Costs vary with scraping volume and LLM usage. Need help customizing? Contact me for consulting and support: LinkedIn

What it does: Collects cybersecurity news from trusted RSS feeds and uses OpenAI’s Retrieval-Augmented Generation (RAG) capabilities with Pinecone to filter for content that is directly relevant to your organization’s tech stack. “Relevant” means the AI looks for news items that mention your specific tools, vendors, frameworks, cloud platforms, programming languages, operating systems, or security solutions — as described in your .txt scope documents. By training on these documents, the system understands the environment you operate in and can prioritize news that could affect your security posture, compliance, or operational stability. Once filtered, summaries of the most important items are sent to your work email every day. How it works Pulls in news from multiple cybersecurity-focused RSS feeds:** The workflow automatically collects articles from trusted, high-signal security news sources. These feeds cover threat intelligence, vulnerability disclosures, vendor advisories, and industry updates. Filters articles for recency and direct connection to your documented tech stack:** Using the publish date, it removes stale or outdated content. Then, leveraging your .txt scope documents stored in Pinecone, it checks each article for references to your technologies, vendors, platforms, or security tools. Uses OpenAI to generate and review concise summaries:** For each relevant article, OpenAI creates a short, clear summary of the key points. The AI also evaluates whether the article provides actionable or critical information before passing it through. Trains on your scope using Pinecone Vector Store (free) for context-aware filtering:** Your scope documents are embedded into a vector store so the AI can “remember” your environment. This context ensures the filtering process understands indirect or non-obvious connections to your tech stack. Aggregates and sends only the most critical items to your work email:** The system compiles the highest-priority news items into one daily digest, so you can review key developments without wading through irrelevant stories. What you need to do: Setup your OpenAI and Pinecone credentials in the workflow Create and configure a Pinecone index (dimension 1536 recommended) Pinecone is free to setup. Setup Pinecone with a single free index. Use a namespace like: scope. Make sure the embedding model is the same for all of your Pinecone references. Submit .txt scope documents listing your technologies, vendors, platforms, frameworks, and security products. .txt does not need to be structured. Add as much detail as possible. Update AI prompts to accurately describe your company’s environment and priorities.

How it works: This n8n workflow automates communication with meeting invitees to decrease no-show rates by sending timely email and WhatsApp reminders, and a clarification request if more information is needed to prepare the meeting. Step-by-step: The workflow is triggered by an incoming email notification from Calendly about a newly scheduled meeting. It uses AI to extract key meeting data from the email content. It checks if the invitee didn't provide sufficient information, and, if there is a need for more information, sends a clarification request email. It calculates the waiting time required for the 24-hour and 1-hour reminders. It uses an If node to determine the correct waiting path based on the meeting time. It uses Wait nodes for timing the reminders correctly. Finally, it sends a reminder email and a WhatsApp reminder before the meeting. Customization Options: Replace Google Gemini with your preferred LLM model (though Gemini works on the free tier). Tailor email and WhatsApp messages to speak your brand's language. Replace Twillio node to WhatsApp node to be a completly free usage flow.

Gmail Lead Reply Analyzer → HubSpot Task + Slack Alert Most sales teams read every email, guess if it’s important, and tell teammates manually. This workflow does it automatically: check intent and sentiment with AI, create follow-up tasks, send Slack alerts, and save everything to Google Sheets. Benefits AI checks sentiment, intent, urgency, and priority Creates HubSpot tasks only if follow-up is needed Sends Slack message with lead summary Logs all results to Google Sheets for tracking Runs 24/7 with no manual sorting How It Works Gmail trigger watches a label for new replies Workflow extracts sender, subject, and message AI analyzes message and returns: sentiment, intent, urgency, next step Code step cleans result, adds date, and checks if follow-up is needed If follow-up = yes → create HubSpot task, send Slack alert, log to Sheets If follow-up = no → just log to Sheets Who Is This For Sales teams getting many leads by email Founders who handle leads themselves Agencies needing clear and fast lead triage Setup Connect Gmail (choose or create label) Add OpenAI API key (model: GPT-4o mini) Connect HubSpot (App Token for tasks) Connect Slack (channel for alerts) Connect Google Sheets (Spreadsheet + Tab) Optional: change how urgency/priority is scored in the code ROI & Monetization Save 3–6 hours per week on email sorting Answer faster and close more deals Sell as $1k–$3k/month “inbox automation” service Strategy Insights In the full walkthrough, I show how to: Make sure AI always returns valid JSON Adjust what counts as a follow-up lead Format Slack messages for quick reading Use Google Sheets as a simple dashboard Check Out My Channel For more AI automation systems that get real results, check out my YouTube channel where I share exactly how I build automation workflows, sell high-value services, and scale to $20k+ monthly revenue.

Introduction Automates travel planning by aggregating flights, hotels, activities, and weather via APIs, then uses AI to generate professional itineraries delivered through Gmail and Slack. How It Works Webhook receives requests, searches APIs (Skyscanner, Booking.com, Kiwi, Viator, weather), merges data, AI builds itineraries, scores options, generates HTML emails, delivers via Gmail/Slack. Workflow Template Webhook → Extract → Parallel Searches (Flights/Hotels/Activities/Weather) → Merge → Build Itinerary → AI Processing → Score → Generate HTML → Gmail → Slack → Response Workflow Steps Trigger & Extract: Receives destination, dates, preferences, extracts parameters. Data Gathering: Parallel APIs fetch flights, hotels, activities, weather, merges responses. AI Processing: Analyzes data, creates itinerary, ranks recommendations. Delivery: Generates HTML email, sends via Gmail/Slack, confirms completion. Setup Instructions API Configuration: Add keys for Skyscanner, Booking.com, Kiwi, Viator, OpenWeatherMap, OpenRouter. Communication: Connect Gmail OAuth2, Slack webhook. Customization: Adjust endpoints, AI prompts, HTML template, scoring criteria. Prerequisites API keys: Skyscanner, Booking.com, Kiwi, Viator, OpenWeatherMap, OpenRouter Gmail account Slack workspace n8n instance Use Cases Corporate travel planning Vacation itinerary generation Group trip coordination Customization Add sources (Airbnb, TripAdvisor) Filter by budget preferences Add PDF generation Customize Slack format Benefits Saves 3-5 hours per trip Real-time pricing aggregation AI-powered personalization Automated multi-channel delivery