Workflow Templates

Description Automate your AI-powered outreach and follow-up pipeline end-to-end with GPT-4o, Gmail, and Google Sheets. 🤖📬 This workflow personalizes emails for each lead, manages follow-ups automatically, tracks client replies, and updates CRM records in real time — all from a single Google Sheet. Ideal for sales and growth teams looking to convert leads faster without manual effort. ⚙️🚀 What This Template Does 1️⃣ Starts manually when you click “Execute workflow.” 🕹️ 2️⃣ Fetches all leads from the Google Sheet (sample_leads_50). 📊 3️⃣ Validates email format and filters only active (unbooked) leads. 🔍 4️⃣ Uses Azure OpenAI GPT-4o to generate short, personalized outreach emails in HTML. ✉️ 5️⃣ Cleans and parses the AI output (subject + HTML body). 🧠 6️⃣ Sends the first outreach email via Gmail and stores its thread ID. 📤 7️⃣ Waits 24 hours, then checks for a client reply in the Gmail thread. ⏱️ 8️⃣ If a positive reply is found → marks lead as BOOKED and updates in Sheets. ✅ 9️⃣ If no reply → triggers a polite follow-up email, waits again 24 hours, and checks the thread a second time. 🔁 🔟 If a second reply is found → marks BOOKED and logs the client message. 1️⃣1️⃣ If still no response → updates status to Declined in Google Sheets. ❌ 1️⃣2️⃣ Logs invalid or incomplete leads to a separate sheet for data cleanup. 🧾 Key Benefits ✅ Eliminates manual outreach and follow-up effort. ✅ Produces personalized, context-aware AI emails for every lead. ✅ Auto-tracks replies and updates CRM status with zero input. ✅ Prevents duplicate or repeated contact with booked clients. ✅ Keeps lead database synchronized and audit-ready. Features Google Sheets integration for dynamic lead retrieval and updates. Regex-based email validation for clean data pipelines. Azure OpenAI GPT-4o for contextual email writing. Two-stage Gmail automation (initial + follow-up). JavaScript parsing for AI output and Gmail thread analysis. Automated 24-hour wait and recheck logic. Conditional branches for Booked / Declined / Invalid outcomes. End-to-end CRM synchronization without manual review. Requirements Google Sheets OAuth2 credentials with read/write access. Azure OpenAI API key for GPT-4o model access. Gmail OAuth2 credentials with send, read, modify permissions. Environment Variables GOOGLE_SHEET_LEADS_ID GOOGLE_SHEET_OUTREACH_TAB_ID AZURE_OPENAI_API_KEY GMAIL_OAUTH_CLIENT_ID GMAIL_OAUTH_SECRET Target Audience 💼 Sales and Business Development teams automating outreach. 📈 Marketing and Growth teams running re-engagement campaigns. 🤖 Automation and RevOps teams integrating AI lead workflows. 💬 Freelancers and agencies managing large prospect lists. 📊 Operations teams maintaining CRM cleanliness and tracking. Step-by-Step Setup Instructions 1️⃣ Connect your Google Sheets, Azure OpenAI, and Gmail credentials. 2️⃣ Set your Google Sheet ID and tab name (outreach automation). 3️⃣ Update the GPT-4o system prompt to match your tone and signature. 4️⃣ Verify column headers (Company Name, Email, Booking Status, etc.). 5️⃣ Test the email validation branch with sample data. 6️⃣ Run once manually to confirm Gmail thread creation and reply detection. 7️⃣ Confirm successful CRM updates in Google Sheets. 8️⃣ Activate for continuous lead outreach and follow-up automation. ✅

This n8n template demonstrates a “Human-in-the-Loop” workflow where AI automatically drafts replies to inbound emails, which are then reviewed and approved by a human before being sent. This powerful pattern ensures both the efficiency of AI and the quality assurance of human oversight. Use cases are many: Streamline sales inquiry responses, manage first-level customer support, handle initial recruitment communications, or any business process that requires personalized yet consistent email replies. Good to know At the time of writing, the cost per execution depends on your OpenAI API usage. This workflow uses a cost-effective model like gpt-4o-mini. See OpenAI Pricing for updated info. The AI’s knowledge base and persona are fully customizable within the Basic LLM Chain node’s prompt. How it works The Gmail Trigger node starts the workflow whenever a new email arrives in the specified inbox. The Classify Potential Leads node uses AI to determine if the incoming email is a potential lead. If not, the workflow stops. The Basic LLM Chain, powered by an OpenAI Chat Model, generates a draft reply based on a detailed system prompt and your internal knowledge base. A Structured Output Parser is crucially used to force the AI’s output into a reliable JSON format ({"subject": "...", "body": "..."}), preventing errors in subsequent steps. The Send for Review Gmail node sends the AI-generated draft to a human reviewer and pauses the workflow, waiting for a reply. The IF node checks the reviewer’s reply for approval keywords (e.g., “approve”, “承認”). If approved, the ✅ Send to Customer Gmail node sends the final email to the original customer. If not approved, the reviewer’s feedback is treated as a revision request, and the workflow loops back to the Basic LLM Chain to generate a new draft incorporating the feedback. How to use Gmail Trigger** node: Configure with your own Gmail account credentials. Send for Review** node: Replace the placeholder email reviewer@example.com with the actual reviewer's email address. IF** node: You can customize the approval keywords to match your team’s vocabulary. OpenAI Nodes**: Ensure your OpenAI credentials are set up. You can select a different model if needed, but the prompt is optimized for models like GPT-4o mini. Requirements An OpenAI account for the LLM. A Gmail account for receiving customer emails and for the review process. Customising this workflow By modifying the prompt and knowledge base in the Basic LLM Chain, you can adapt this agent for various departments, such as technical support, HR, or public relations. The approval channel is not limited to Gmail. You can easily replace the review nodes with Slack or Microsoft Teams nodes to fit your internal communication tools.

Description Automate Jira backlog management with intelligent cleanup, prioritization, and AI-powered reporting. This workflow scans daily to identify stale issues, missing priorities, and overdue tasks — auto-updates Jira with corrective labels, logs everything into Google Sheets for tracking, and notifies teams via Slack. Every Friday, it sends an AI-generated backlog summary email to project leads for visibility and planning. 🚀📅 What This Template Does Step 1: Triggers automatically every weekday at 7:00 AM to fetch backlog issues from Jira. ⏰ Step 2: Filters issues missing estimates, assignees, or priority values for cleanup. 🧹 Step 3: Applies corrective labels (e.g., “Needs Estimation,” “Unassigned,” “Overdue”). 🏷️ Step 4: Logs all flagged issues into Google Sheets with timestamps for audit tracking. 📊 Step 5: Sends real-time Slack alerts summarizing key backlog insights. 💬 Step 6: Every Friday, uses GPT-4 to generate a summarized backlog health report. 🤖 Step 7: Delivers weekly summary emails to leads and project managers via Gmail. 📧 Key Benefits ✅ Eliminates manual backlog reviews and prioritization. ✅ Ensures consistent Jira hygiene and task visibility. ✅ Provides centralized backlog tracking via Google Sheets. ✅ Sends real-time alerts for overdue and unassigned tasks. ✅ Offers AI-driven insights for better sprint planning. Features Automated daily trigger (Mon–Fri, 7 AM) Jira issue fetching and filtering by priority and assignment Smart labeling for hygiene tracking Slack alerts for backlog anomalies Weekly GPT-4 generated summary reporting Google Sheets integration for historical logging Gmail integration for summary email delivery Requirements Jira API credentials with read/write issue permissions Google Sheets OAuth2 credentials for data logging Slack Bot token with chat:write permissions Gmail OAuth2 credentials for email delivery OpenAI or Azure OpenAI API key for GPT-4 summarization Target Audience Agile and Scrum teams maintaining large backlogs 🧩 Product managers ensuring backlog quality and consistency 📋 Engineering leads seeking proactive backlog hygiene 🛠️ Organizations needing visibility across project tasks 🏢 Remote teams using Slack for daily syncs 🌐 Step-by-Step Setup Instructions Connect Jira credentials and specify your project key(s). 🔑 Link your Google Sheet and replace YOUR_SHEET_ID for backlog tracking. 📊 Configure Slack and replace YOUR_CHANNEL_ID for alert delivery. 💬 Add Gmail credentials and define recipient emails for weekly reports. 📧 Add your GPT-4 API key (OpenAI or Azure) for AI summarization. 🤖 Adjust cron expression (0 7 * * 1-5) to match your local timezone. ⏰ Run manually once to validate all connections, then enable automation. ✅

Who's it for Customer support teams, SaaS companies, and service businesses that need to quickly identify and respond to urgent customer issues. Perfect for organizations handling high ticket volumes where manual prioritization creates delays and missed critical issues. How it works This workflow automatically analyzes incoming Zendesk tickets using OpenAI's GPT-4 to determine urgency levels and routes high-priority issues to your team via Slack notifications. The system monitors new Zendesk tickets via webhook, extracts key information (subject, description, customer details), and sends this data to OpenAI for intelligent analysis. The AI considers factors like emotional language, business impact keywords, technical severity indicators, and customer context to assign an urgency score from 1-5. Based on the AI analysis, the workflow automatically updates the ticket priority in Zendesk, adds detailed reasoning as a private note, and sends formatted Slack notifications for high-priority issues (score 4+). The Slack alert includes ticket details, urgency reasoning, key indicators found, and direct links to the ticket for immediate action. How to set up Prerequisites: Zendesk account with API access OpenAI API key (GPT-4 access recommended) Slack workspace with webhook permissions n8n instance (cloud or self-hosted) Setup steps: Configure credentials in n8n: Add OpenAI API credential with your API key Add Zendesk API credential (email + API token) Add Slack API credential (bot token with chat:write permissions) Update Configuration Variables node: Set your Zendesk subdomain (e.g., "yourcompany" for yourcompany.zendesk.com) Configure Slack channel for urgent alerts (e.g., "#support-urgent") Adjust urgency threshold (1-5, default is 4) Set default assignee email for fallback scenarios Set up Zendesk webhook: Copy the webhook URL from the trigger node In Zendesk Admin, go to Settings > Extensions > Add target Create HTTP target with the copied URL and POST method Create a trigger for "Ticket is created" that sends to this target Test the workflow: Create a test ticket with urgent language ("system is down", "critical issue") Verify the AI analysis runs and priority is updated Check that Slack notifications appear for high-priority tickets Confirm ticket updates include AI reasoning in private notes Requirements Zendesk** account with API access and admin permissions for webhook setup OpenAI API key** with GPT-4 access (estimated cost: $0.01-0.05 per ticket analysis) Slack workspace** with bot creation permissions and access to notification channels n8n instance** (cloud subscription or self-hosted installation) How to customize the workflow Adjust AI analysis parameters: Modify the system prompt in the OpenAI node to focus on industry-specific urgency indicators Add custom keywords or phrases relevant to your business in the prompt Adjust the temperature setting (0.1-0.5) for more consistent vs creative analysis Configure priority mapping: Edit the Code node to change how urgency scores map to Zendesk priorities Add custom business logic based on customer tiers or product types Implement time-based urgency (e.g., higher priority during business hours) Enhance Slack notifications: Customize the Slack message blocks with additional fields (product, customer tier, SLA deadline) Add action buttons for common responses ("Acknowledge", "Escalate", "Assign to me") Route different urgency levels to different Slack channels Extend integrations: Add email notifications using the Email node for critical issues Integrate with PagerDuty or Opsgenie for after-hours escalation Connect to your CRM to enrich customer context before AI analysis Add Teams or Discord notifications as alternatives to Slack Advanced customizations: Implement machine learning feedback loops by tracking resolution times vs AI scores Add sentiment analysis as a separate factor in priority calculation Create daily/weekly summary reports of AI analysis accuracy Build approval workflows for certain priority changes before auto-updating

This workflow automatically sends timely medication reminders to patients after a prescription is marked as sent in the system. It reads the medication schedule from prescription data, schedules reminders for each dosage time, and delivers notifications via WhatsApp, SMS, or email. All reminders are tracked and logged, ensuring patients stay on track with their treatment while providing healthcare providers with acknowledgment records. 📋 Simple Google Sheets Structure "Prescriptions" Sheet - Required columns: prescription_id patient_name patient_phone patient_email medication dosage times_per_day (1, 2, 3, or 4) duration_days (7, 14, 30, etc.) start_date (YYYY-MM-DD) prescription_status (set to "sent") reminders_created (auto-updated to "yes") "Reminders" Sheet (auto-created): Simple tracking of all scheduled reminders 🔧 Workflow Components (Only 10 Nodes!) Part 1: Schedule Creation Watch Sheet → Monitors for "sent" prescriptions Filter New → Only processes unscheduled prescriptions Create Schedule → Generates reminder times automatically Save Reminders → Stores schedule in sheet Mark Processed → Prevents duplicate scheduling Part 2: Send Reminders Cron Timer → Checks every 10 minutes Get Reminders → Retrieves all scheduled reminders Find Due → Identifies reminders due now Send Messages → WhatsApp + Email simultaneously Mark Sent → Updates status to prevent duplicates ⚙️ Simple Setup Replace these values: YOUR_GOOGLE_SHEET_ID YOUR_WHATSAPP_PHONE_NUMBER_ID Email sender address Add credentials: Google Sheets API WhatsApp API SMTP for email Sample data: prescription_id: RX001 patient_name: John Doe patient_phone: +1234567890 patient_email: john@email.com medication: Amoxicillin 500mg dosage: 1 tablet times_per_day: 3 duration_days: 7 start_date: 2025-01-15 prescription_status: sent reminders_created: no 📱 Default Schedule 1x daily:** 9:00 AM 2x daily:** 9:00 AM, 9:00 PM 3x daily:** 8:00 AM, 2:00 PM, 8:00 PM 4x daily:** 8:00 AM, 12:00 PM, 4:00 PM, 8:00 PM

How it works This workflow automatically generates a weekly sales performance report from Google Sheets data. It runs on a schedule, cleans and filters the dataset, and calculates week-over-week performance metrics. An AI model generates a concise business summary, while a chart visualizes category performance. Finally, the report is formatted into a professional HTML email and sent via Gmail. Step-by-step Trigger & fetch data** Schedule Trigger1 – Runs the workflow weekly at a defined time. Get row(s) in sheet1 – Fetches raw sales data from Google Sheets. Clean & prepare data** Removing Empty Rows1 – Cleans dataset by removing empty rows and formatting numbers. Filter Latest Week1 – Filters data to include only the relevant weekly range. Sales Analytics Engine – Calculates totals, growth %, category performance, and prepares structured output. Generate insights & visuals** Message a model – Uses AI to generate a short performance summary and recommendations. HTTP Request1 – Generates a bar chart comparing category performance using QuickChart. Format & send report** Format Email1 – Builds a styled HTML email with KPIs, insights, and tables. Send Email1 – Sends the final report via Gmail to the configured recipient. Why use this? Automates weekly reporting with zero manual effort Provides AI-driven insights for better decision-making Visualizes performance trends clearly with charts Improves communication with clean, professional email reports Saves time on data cleaning, analysis, and formatting

🚀 What this template does Automatically finds and enriches key contacts in a deal’s buying group by combining the company domain from the HubSpot deal with the buying group criteria you define (departments, seniorities, countries, job titles). It then pushes these contacts into HubSpot and emails your team a clean summary with direct HubSpot links—so no decision-maker falls through the cracks. Before starting, make sure you have: Buying Group Criteria Excel – Contains two sheets: Buying group reference values (reference list) Your Buying Group Criterias (where you define your filters) You’ll import the Excel file into Google Sheets during setup. ❓ What Problem Does This Solve? When a new opportunity/deal is created, sales teams often miss adjacent decision-makers (e.g., VP Sales, Head of Marketing). This template searches for those people, enriches their contact data, adds/updates them in HubSpot, and notifies your team with a one-glance table. 🧰 Prerequisites: To use this template, you’ll need: A self-hosted or cloud instance of n8n A Surfe API Key (Bearer token for People Search & Bulk Enrich) A Google Sheets account (OAuth2 or service account) with access to your criteria sheet A HubSpot developer account (for the HubSpot Deal Trigger) A HubSpot normal account (where there is your deals, contacts, companies) A Gmail account to send the enrichment summary email The workflow JSON file (included with this tutorial) Buying Group Criteria Excel (included with this tutorial) 📌 Your input (Google Sheets) This workflow uses a Google Sheet with two tabs: Buying group reference values – A read-only reference list of all available options for the departments and seniorities columns. You’ll use this list to choose your search filters. Your Buying Group Criterias – The sheet where you define the actual filters used in the workflow. ⚠️ Before you start: Import the provided Excel file into your Google Sheets account so both tabs appear exactly as in the template. How to fill it the tab “Your Buying Group Criterias”: departments (Column A) → Select one or more values from the reference tab. Only rows containing a value will be used in the search. seniorities (Column B) → Select one or more values from the reference tab. Only rows containing a value will be used in the search. countries (Column C) → Enter any ISO Alpha-2 country codes (e.g., fr, gb, de). This is a free-text filter. jobTitles (Column D) → Enter any job title keywords you want to search for (e.g., CTO, Head of Marketing). This is also a free-text filter. The workflow will read the filled cells from each column, clean duplicates, and pass them to the Surfe People Search API. ⚙️ Setup Instructions 4.1 🔐 Create Your Credentials in n8n 4.1.1 📊 Google Sheets OAuth2 API Go to n8n → Credentials Create new credentials: Type: Google Sheets OAuth2 API Here a pop-up will open where you can login to your Google account from where you will read the Google Sheets When it’s done you should see this on n8n 4.1.2 📧 Gmail OAuth2 API Go to n8n → Credentials Create new credentials: Type: Gmail OAuth2 API A pop-up window will appear where you can log in with your Google account that is linked to Gmail Make sure you grant email send permissions when prompted 4.1.3 🚀 Surfe API In your Surfe dashboard → Use Surfe Api → copy your API key Go to n8n → Credentials → Create Credential Choose Credential Type: Bearer Auth Name it something like SURFE API Key Paste your API key into the Bearer Token Save 4.1.4 🎯 HubSpot OAuth2 API Go to n8n → Credentials → Create Credential → HubSpot Oauth2 API Here make sure to select your normal hubspot account where your companies, deals and contacts are and not the hubspot-developers-xxx.com Done ✅ 4.1.5 🔓 HubSpot Private App Token Go to HubSpot → Settings → Integrations → Private Apps Create an app with scopes: crm.objects.contacts.read crm.objects.contacts.write crm.schemas.contacts.read Save the App token Go to n8n → Credentials → Create Credential → HubSpot App Token Paste your App Token 4.1.6 🎯 HubSpot Developer Api: In order to Use the HubSpot Trigger node, you need to setup HubSpot Developer Api To configure this credential, you'll need a HubSpot developer account and: A Client ID: Generated once you create a public app. A Client Secret: Generated once you create a public app. A Developer API Key: Generated from your Developer Apps dashboard. An App ID: Generated once you create a public app. To create the public app and set up the credential: Log into your HubSpot app developer account. Select Apps from the main navigation bar. Select Get HubSpot API key. You may need to select the option to Show key. Copy the key and enter it in n8n as the Developer API Key. Still on the HubSpot Apps page, select Create app. On the App Info tab, add an App name, Description, Logo, and any support contact info you want to provide. Anyone encountering the app would see these. Open the Auth tab. Copy the App ID and enter it in n8n. Copy the Client ID and enter it in n8n. Copy the Client Secret and enter it in n8n. In the Scopes section, select Add new scope. Add all the scopes listed in Required scopes for HubSpot Trigger node to your app. Select Update. Copy the n8n OAuth Redirect URL and enter it as the Redirect URL in your HubSpot app. Select Create app to finish creating the HubSpot app. Refer to the HubSpot Public Apps documentation for more detailed instructions. You should see this on n8n at the end. ✅ You are now all set for the credentials 4.2 📥 Import and Configure the N8N Workflow Import the provided JSON workflow into N8N Create a New Blank Workflow click the … on the top left Import from File 4.2.1 🔗 Link Nodes to Your Credentials In the workflow, link your newly created credentials to each node of this list : Google Sheets -> Credentials to connect with → Google Sheets Account Gmail Node Credentials to connect with → Gmail account HubSpot: Create or Update →Credentials to connect with → Huspot App Token Account HubSpot Get Company →Credentials to connect with → Huspot App Token Account HubSpot get deal →Credentials to connect with → Huspot App Token Account HubSpot Trigger →Credentials to connect with → Huspot Developer account HTTP Node GET deal associated companies from HUBSPOT → Credential Type → Hubspot OAuth2 API Surfe HTTP nodes: Authentication → Generic Credential Type Generic Auth Type → Bearer Auth Bearer Auth → Select the credentials you created before Surfe HTTP nodes 4.2.2 🔧 Additional Setup for the node Google Sheets READ CRITERIAS: Paste the url of your google sheets in Document → By URL Select the sheet Your Buying Group Criterias 🔄 How This N8N Workflow Works A new deal is created in HubSpot, which triggers the workflow. The workflow retrieves the company domain linked to that deal. It reads the buying group criteria from your Google Sheet (departments, seniorities, countries, job titles). These criteria are combined with the company domain to create a search payload for Surfe’s People Search API (limited to 200 people per run). Matching contacts are then sent to Surfe’s Bulk Enrichment API to retrieve emails, phone numbers, and other details. n8n polls Surfe until the enrichment job is complete. Enriched contact data is extracted and filtered so that only contacts with at least one valid email or phone number remain. These contacts are created or updated in HubSpot. Finally, a Gmail summary email is sent to your team with a clean table of the new or updated contacts and direct links to view them in HubSpot. 🧩 Use Cases Net-new deal created → instantly surface the rest of the buying group and enrich contacts. 🛠 Customization Ideas 🔁 Add retry logic for failed Surfe enrichment jobs 📤 Log enriched contacts into a Google Sheet or Airtable for auditing 📊 Extend the flow to generate a basic summary report of enriched vs rejected contacts ⏳ Trigger the enrichment not only on deal creation but also at a specific deal stage change 📧 Send the summary email to multiple recipients or a team mailing list ✅ Summary This template automates buying-group discovery and enrichment off a new HubSpot deal, writes enriched contacts back to HubSpot, and emails a neat table to your team—so reps focus on outreach, not admin. Import it, connect credentials, point at your criteria sheet, and Let Surfe do the rest.

Track your daily mood in one tap and receive automated AI summaries of your emotional trends every week and month. Perfect for self-reflection, wellness tracking, or personal analytics. This workflow logs moods sent through a webhook (/mood) into Data Tables, analyzes them weekly and monthly with OpenAI (GPT-4o), and emails you clear summaries and actionable recommendations via Gmail. ⚙️ How It Works Webhook – Mood → Collects new entries (🙂, 😐, or 😩) plus an optional note. Set Mood Data → Adds date, hour, and note fields automatically. Insert Mood Row → Stores each record in a Data Table. Weekly Schedule (Sunday 20:00) → Aggregates the last 7 days and sends a summarized report. Monthly Schedule (Day 1 at 08:00) → Aggregates the last 30 days for a deeper AI analysis. OpenAI Analysis → Generates insights, patterns, and 3 actionable recommendations. Gmail → Sends the full report (chart + AI text) to your inbox. 📊 Example Auto-Email Weekly Mood Summary (last 7 days) 🙂 5 ██████████ 😐 2 ████ 😩 0 Average: 1.7 (Positive 🙂) AI Insights: You’re trending upward this week — notes show that exercise days improved mood. Try keeping short walks mid-week to stabilize energy. 🧩 Requirements n8n Data Tables enabled OpenAI credential (GPT-4o or GPT-4 Turbo) Gmail OAuth2 credential to send summaries 🔧 Setup Instructions Connect your credentials: Add your own OpenAI and Gmail OAuth2 credentials. Set your Data Table ID: Open the Insert Mood Row node and enter your own Data Table ID. Without this, new moods won’t be stored. Replace the email placeholder: In the Gmail nodes, replace your.email@example.com with your actual address. Deploy and run: Send a test POST request to /mood (e.g. { "mood": "🙂", "note": "productive day" }) to log your first entry. ⚠️ Before activating the workflow, ensure you have configured the Data Table ID in the “Insert Mood Row” node. 🧠 AI Analysis Interprets mood patterns using GPT-4o. Highlights trends, potential triggers, and suggests 3 specific actions. Runs automatically every week and month. 🔒 Security No personal data is exposed outside your n8n instance. Always remove or anonymize credential references before sharing publicly. 💡 Ideal For Personal mood journaling and AI feedback Therapists tracking client progress Productivity or self-quantification projects 🗒️ Sticky Notes Guide 🟡 Mood Logging Webhook POST /mood receives mood + optional note. ⚠️ Configure your own Data Table ID in the “Insert Mood Row” node before running. 🟢 Weekly Summary Runs every Sunday 20:00 → aggregates last 7 days → generates AI insights + emails report. 🔵 Monthly Summary Runs on Day 1 at 08:00 → aggregates last 30 days → creates monthly reflection. 🟣 AI Analysis Uses OpenAI GPT-4o to interpret trends and recommend actions. 🟠 Email Delivery Sends formatted summaries to your inbox automatically.

This n8n template automates targeted lead discovery, AI-driven data structuring, and personalized cold-email sending at controlled intervals. It’s ideal for sales teams, founders, and agencies that want to scale outreach without losing personalization. Good to know Can run on an interval (e.g., every 10 minutes) to fetch and process new leads. Requires API keys for OpenAI (content + parsing) and Apify (lead discovery). Emails are sent one-by-one with delays (the Wait node) to reduce spam risk. Lead data is written to Google Sheets—we recommend separate sheets for leads with and without emails. Works with Gmail, Outlook, or your own SMTP—just plug in your credentials. How it works Form Trigger (START) A form collects: Job Title, Company Size, Keywords, Location. Apollo URL Generator (GPT) The model turns the form fields into a precise Apollo search URL. Run Apify (Actor) Apify fetches contacts/companies that match your preferences for downstream processing. Limit Caps how many records are prepared per run (e.g., max 5). Parse Lead Data (GPT) Extracts key fields (full name, email, title, LinkedIn, company, company links). Synthesizes a short 2–3 sentence sales-ready summary for each lead. Sorting (If) Splits leads into with email vs. without email. With email → main sheet + email pathway Without email → a separate sheet for later enrichment Email Magic (GPT) Uses the parsed data to personalize your fixed email template for each lead (keeps structure/intent, swaps in the right details). Sending Emails (Loop + Wait + Sender) Loop Over sends messages individually. Wait inserts a pause between sends (fully configurable). Delivery via Gmail or SMTP (custom domain / Outlook). Confirmation After the loop finishes, a Gmail node sends a “campaign complete” confirmation. How to use Enable the workflow and open the start form. Enter preferences: job title, company size, keywords, location. Add credentials: OpenAI (for parsing + email generation) Apify (Bearer token in Run Apify) Google (Sheets + optionally Gmail) SMTP/Outlook (if not using Gmail) Set limits (the Limit node) and send interval (the Wait node). Choose sheets for leads with/without email. Run—the workflow will fetch leads, prepare emails, and send them with spacing. Requirements OpenAI API key Apify API token (access to the chosen Actor) Google Sheets for storage Gmail or SMTP/Outlook credentials for sending An operational n8n instance Customising this workflow Email template: Edit the text in “Creating a email” while preserving placeholders. Segmentation: Add more conditions (role, industry, country) and route to different templates/sheets. Follow-ups: Add a second loop that reads statuses and sends timed reminders. Data enrichment: Insert additional APIs before “Parse Lead Data.” Anti-spam: Increase Wait duration, rotate senders, vary subject lines. Reporting: Add a “send status” sheet and an error log. Security & compliance tips Store API keys in n8n Credentials, not plain-text nodes. Respect GDPR/opt-out—track source and first-contact date in your sheet. Start with a small batch, validate deliverability, then scale up. In short Automated lead capture → AI cleaning + summary → personalized emails → spaced sending → completion notice. Scalable, customizable, and ready to plug into your preferred sender and template.

This automated n8n workflow enables an AI-powered movie recommendation system on WhatsApp. Users send messages like "I want to watch a horror movie" or "Where can I watch the Jumanji movie?" The workflow uses AI to interpret the request, searches relevant APIs (e.g., TMDb, JustWatch), and replies with movie recommendations or streaming platform availability via WhatsApp. Fundamental Aspects WhatsApp Webhook Trigger**: Initiates the workflow when a WhatsApp message is received. Analyze WhatsApp Message**: Uses AI (e.g., Ollama Model) to interpret the user's intent and extract request type. Check Request Type**: Determines if the request is for a movie genre or a specific movie title. Check Where Request**: Identifies if the request includes a "where to watch" query. Extract Movie Title**: Extracts the movie title from the message if specified. Extract Genre**: Identifies the movie genre from the message if specified. Search Specific Movie Title**: Queries an API (e.g., TMDb) for details about a specific movie. Search Movies by Genre**: Queries an API (e.g., TMDb) for movies matching the genre. Get Streaming Availability**: Queries an API (e.g., JustWatch) for streaming platforms. Format Streaming Response**: Prepares the response with streaming platform details. Format Genre Recommendations**: Prepares the response with genre-based movie recommendations. Prepare WhatsApp Message**: Formats the final response for WhatsApp. Send WhatsApp Response**: Sends the recommendation or streaming info back to the user via WhatsApp. Setup Instructions Import the Workflow into n8n: Download the workflow JSON and import it via the n8n interface. Configure API Credentials: Set up WhatsApp Business API credentials with a valid phone number and token. Configure TMDb API key (e.g., https://api.themoviedb.org). Configure JustWatch API key (e.g., https://api.watchmode.com). Set up AI model credentials (e.g., Ollama Model). Run the Workflow: Activate the webhook trigger and test with a WhatsApp message. Verify Responses: Check WhatsApp for accurate movie recommendations or streaming info. Adjust Parameters: Fine-tune API endpoints or AI model as needed. Features AI Interpretation**: Uses AI to analyze user intents (genre or movie title). API Integration**: Searches TMDb for movie details and JustWatch for streaming availability. Real-Time Responses**: Sends instant replies via WhatsApp. Custom Recommendations**: Provides genre-based or specific movie recommendations. Technical Dependencies WhatsApp Business API**: For receiving and sending messages. TMDb API**: For movie details and genre searches. JustWatch API**: For streaming availability. Ollama Model**: For AI-based message analysis. n8n**: For workflow automation and integration. Customization Possibilities Add More APIs**: Integrate additional movie databases (e.g., IMDb). Enhance AI**: Train the Ollama Model for better intent recognition. Support More Languages**: Add multilingual support for WhatsApp responses. Add Email Alerts**: Include email notifications for admin monitoring. Customize Responses**: Adjust the format of recommendations or streaming info.

Who’s it for This template is perfect for SEO writers, niche bloggers, and content marketers who want to generate high-quality blog posts from a single keyword without spending hours on research and writing. If you often find yourself stuck at the research stage or manually drafting blog content, this workflow automates the entire process from topic discovery to publication. What it does The workflow takes a keyword, performs a Google search using Dumpling AI, analyzes the top-ranking pages and People Also Ask (PAA) questions, and then uses GPT-4 to generate a detailed blog post based on the most valuable question. The blog draft is sent for approval via email, and once approved, it’s automatically published to WordPress. Here’s what happens step by step: Receives a keyword through a simple form Uses Dumpling AI to perform a Google search and extract: Top 2 organic search results People Also Ask (PAA) questions and answers Top related searches Filters for insightful PAA questions Sends the data to GPT-4 to generate a blog post in JSON format Emails the draft blog post for manual review and approval If approved, publishes the post automatically to WordPress How it works Form Trigger: Captures the keyword input Dumpling AI: Searches Google and extracts SEO data including top results, PAA, and related searches Code Node: Processes the raw search data into a structured format for GPT-4 Filter Node: Checks if PAA questions are available GPT-4: Chooses a strong PAA question and writes the blog post Gmail: Sends the draft blog post to your inbox for review Approval Node: Waits for manual approval WordPress: Publishes the approved post automatically Requirements ✅ Dumpling AI API key stored securely as credentials ✅ OpenAI GPT-4 credentials ✅ Gmail account with OAuth2 connected to n8n ✅ WordPress account with API credentials configured How to customize Edit the GPT-4 prompt to control the blog structure, tone, or style Add extra filters to select specific types of PAA questions (e.g., how-to, guides) Change the review recipient email in the Gmail node Add additional formatting or SEO optimization steps before publishing Integrate with Notion, Airtable, or Slack to log or notify team members after publication > This workflow turns a single keyword into a fully researched, GPT-4 generated, and auto-published blog post — helping you scale content creation efficiently while maintaining quality.

Chat-Based AWS IAM Policy Generator with OpenAI Agent > Chat-driven workflow that lets IT and DevOps teams generate custom AWS IAM policies via AI, automatically apply them to AWS, and send an email notification with policy details. 👤 Who’s it for This workflow is designed for: Cloud Engineers / DevOps* who need to quickly generate and apply *custom IAM policies** in AWS. IT Support / Security teams* who want to create IAM policies through a *chat-based interface** without manually writing JSON. Teams that want automatic notifications (via email) once new policies are created. ⚙️ How it works / What it does Trigger → Workflow starts when a chat message is received. IAM Policy Creator Agent → Uses OpenAI to: Interpret user requirements (e.g., service, actions, region). Generate a valid IAM policy JSON following AWS best practices. IAM Policy HTTP Request → Sends the generated policy to AWS IAM CreatePolicy API. Email Notification → Once AWS responds with a CreatePolicyResponse, an email is sent with policy details (name, ARN, ID, timestamps, etc.) using n8n mapping. Result: The user can chat with the AI agent, create a policy, and receive an email confirmation with full details. 🛠 How to set up Chat Trigger Node Configure the When chat message received node to connect your preferred chat channel (Slack, MS Teams, Telegram, etc.). IAM Policy Creator Agent Add OpenAI Chat Model as the LLM. Use a system prompt that enforces AWS IAM JSON best practices (least privilege, correct JSON structure). Connect Memory (Simple Memory) and Structured Output Parser to ensure consistent JSON output. IAM Policy HTTP Request Set method: POST URL: https://iam.amazonaws.com/ Add authentication using AWS Signature v4 (Access Key + Secret Key). Body: Action=CreatePolicy PolicyName={{ $json.CreatePolicyResponse.CreatePolicyResult.Policy.PolicyName }} PolicyDocument={{ $json.policyDocument }} Version=2010-05-08 Email for tracking 📋 Requirements n8n instance (self-hosted or cloud). AWS IAM user/role with permission to iam:CreatePolicy. AWS Access Key + Secret Key (for SigV4 signing in HTTP request). OpenAI API key (for the Chat Model). Email server credentials (SMTP or provider integration). 🎨 How to customize the workflow Restrict services/actions** → Adjust the IAM Policy Creator Agent system prompt to limit what services/policies can be generated. Notification channels** → Replace the email node with Slack, MS Teams, or PagerDuty to alert other teams. Tagging policies** → Modify the HTTP request to include Tags when creating policies in AWS. Human-readable timestamps** → Add a Function or Set node to convert CreateDate and UpdateDate from Unix epoch to ISO datetime before sending emails. Approval step** → Insert a manual approval node before sending the policy to AWS for compliance workflows.