by Oneclick AI Squad
This n8n workflow automatically creates and sends regular performance summaries to parents using data from a Learning Management System (LMS). It pulls student grades and attendance, formats them into easy-to-read reports, and emails them without any manual work. Good to Know Fully Automated**: Generates reports and sends emails using LMS data. Regular Updates**: Sends summaries on a set schedule (e.g., every Monday at 9 AM). Clear Reports**: Includes student grades, attendance, and progress notes. Error Alerts**: Notifies admins via email if something goes wrong. Scalable**: Works for multiple students across different classes. How It Works Report Generation Flow Weekly Trigger: Starts the process every Monday at 9 AM. Fetch LMS Data: Pulls grades, attendance, and progress from the LMS. Process Data: Organizes the data into a clear report format. Generate HTML Report: Creates a readable report with student details. Send Email to Parents: Emails the report to parents’ addresses. Log Report Delivery: Records the sent reports in a log. Example Sheet Columns Student ID**: Unique identifier for each student. Name**: Full name of the student. Grade**: Current academic grade or score. Attendance**: Percentage of classes attended. Progress Notes**: Brief comments on performance. Report Date**: Date the report was generated. How to Use Import Workflow: Add the workflow to n8n using the “Import Workflow” option. Set Up LMS Access: Configure n8n with LMS credentials to fetch data. Configure Email: Add parent email addresses and set up an email service (e.g., Gmail). Activate Workflow: Save and turn on the workflow in n8n. Check Logs: Verify reports are sent and logs are updated. Requirements n8n Instance**: Self-hosted or cloud-based n8n setup. LMS Access**: API or credentials to connect to the LMS. Email Service**: SMTP setup (e.g., Gmail) for sending reports. Admin Oversight**: Someone to monitor and fix any errors. Customizing This Workflow Change Schedule**: Adjust the trigger to send reports weekly or monthly. Add More Data**: Include extra LMS fields like behavior notes. Custom Email**: Change the email template for a personalized touch.
by Baptiste Fort
Telegram Voice Message → Automatic Email Imagine: What if you could turn a simple Telegram voice message into a professional email—without typing, copying, pasting, or even opening Gmail? This workflow does it all for you: just record a voice note, and it will transcribe, format, and write a clean HTML email, then send it to the right person—all by itself. Prerequisites Create a Telegram bot** (via BotFather) and get the token. Have an OpenAI account** (API key for Whisper and GPT-4). Set up a Gmail account with OAuth2.** Import the JSON template** into your automation platform. 🧩 Detailed Flow Architecture 1. Telegram Trigger Node: Telegram Trigger This node listens to all Message events received by the specified bot (e.g., “BOT OFFICIEL BAPTISTE”). Whenever a user sends a voice message, the trigger fires automatically. > ⚠️ Only one Telegram trigger per bot is possible (API limitation). Key parameter: Trigger On: Message 2. Wait Node: Wait Used to buffer or smooth out calls to avoid collisions if you receive several voice messages in a row. 3. Retrieve the Audio File Node: Get a file Type:** Telegram (resource: file) Parameter:** fileId = {{$json"message"["file_id"]}} This node fetches the voice file from Telegram received in step 1 4. Automatic Transcription (Whisper) Node: Transcribe a recording Resource:** audio Operation:** transcribe API Key:** Your OpenAI account The audio file is sent to OpenAI Whisper: the output is clean, accurate text ready to be processed. 5. Optional Wait (Wait1) Node: Wait1 Same purpose as step 2: useful if you want to buffer or add a delay to absorb processing time. 6. Structured Email Generation (GPT-4 + Output Parser) Node: AI Agent This is the core of the flow: The transcribed text is sent to GPT-4 (or GPT-4.1-mini here, via OpenAI Chat Model) Prompt used:** You are an assistant specialized in writing professional emails. Based on the text below, you must: {{ $json.text }} Detect if there is a recipient's email address in the text (or something similar like "send to fort.baptiste.pro") If it’s not a complete address, complete it by assuming it ends with @gmail.com. Understand the user's intent (resignation, refusal, application, excuse, request, etc.) Generate a relevant and concise email subject, faithful to the content Write a professional message, structured in HTML: With a polite tone, adapted to the situation Formatted with HTML tags (`, `, etc.) No spelling mistakes in French My first name is jeremy and if the text says he is not happy, specify the wish to resign ⚠️ You must always return your answer in the following strict JSON format, with no extra text: { "email": "adresse@gmail.com", "subject": "Objet de l’email", "body": "Contenu HTML de l’email" } Everything is strictly validated and formatted with the Structured Output Parser node. 7. Automatic Email Sending (Gmail) Node: Send a message To:** {{$json.output.email}} Subject:** {{$json.output.subject}} HTML Body:** {{$json.output.body}} This node takes the JSON structure returned by the AI and sends the email via Gmail, to the right recipient, with the correct subject and full HTML formatting.
by Mohamed Abubakkar
How it Works. The workflow runs automatically every day and collects analytics data for both today and yesterday. It cleans and standardizes both datasets in the same way so they are easy to compare. After that, it measures how performance has changed from one day to the next and interprets those changes to understand trends and context. Once all calculations are finished, the AI creates a clear, easy-to-read summary of what happened. This summary is then formatted and sent through the required communication channels, while the final data is saved for tracking over time and for creating follow-up tasks if needed. Key Features: Trigger runs once per day recommended (11: 57 PM). Fetch seperate data for today and yesterday in node. Compare the two days data and highlight if traffic is less Add the trend lowTraffic for low traffic identification. Using GPT-4 Mini for human readable summary to suit different communication channels. Sending report to WhatsApp / Email Stored the final structure data to Google Sheet for future analytics and historical record Setup Steps 1. Connect Required Credentials You must connect the following credentials: Google Analytics API Google Sheets OpenAi API Email SMTP WhatsApp API ClickUp API 2. Replace Defalut Values Update the workflow with: Your Google Analytics Id's Your Google Sheet Tabs Replace SMTP Credentials, sender and recipients Change with your OpenAi API key Create your WhatApi API Credentials ClickUp API Key's 3. Customize Email Template Modify subject, message body, or formatting style based on your reporting standards. 4. Adjust Trigger You may choose: Manual Trigger Cron Trigger for daily/weekly report Webhook Trigger integrated with your system Detailed Process Flow Schedule Trigger Node Type: Trigger Node Purpose: Automates the start of the workflow. Details: Runs every day (or every hour if real-time monitoring is needed). Eliminates manual data collection and ensures consistent reporting. Analytics Reports Node Type :- Google Analytics Node Purpose: Fetch website performance data Metrics includes: users, sessions, page views Combine the Data Node Type: Merge Node (Append) Purpose: Combines today’s and yesterday’s datasets into a single item for comparison. Details: Prepares data for calculating percentage changes. Maintains proper structure for further nodes. Calculate Percent Changes Node Type: Function Node Purpose: Computes day-over-day percentage changes for users, sessions, and page views. Details: Formula: ((today - yesterday) / yesterday) * 100 Handles increases and decreases correctly. Outputs values used for trend indicators and alerts. Generate AI Summary Node Type: OpenAI Node Purpose: Produces human-readable, professional insights about the daily analytics. Details: Summarizes key changes, trends, and recommendations. Provides context such as low-traffic warnings. Text output is used in emails, WhatsApp, and ClickUp tasks. Send Email or Whatsapp to Dedicated Person / Marketing Team Node Type: Email Node / Whatspp Purpose: Sends daily alert or report emails or whatsapp. Details: Includes formatted metrics and AI summary. Email subject and body clearly indicate trends and recommendations. Workflow Benefits Fully automated daily GA reporting AI-generated summaries for clear insights Alerts only triggered when necessary Historical logging for trends and dashboards Actionable tasks automatically created in ClickUp Multi-channel delivery via Email and WhatsApp Handles low-traffic scenarios gracefully
by Jitesh Dugar
Customer Support Ticket Documentation Automation Automatically transform resolved support tickets into professional, AI-powered PDF documentation with complete tracking and team notifications. Overview This workflow automates the entire process of documenting resolved support tickets — from receiving ticket data to generating professional PDF case studies, storing them in Google Drive, tracking in spreadsheets, and notifying your team. Powered by AI, it creates consistent, high-quality documentation that can be reused for knowledge base articles, training materials, and compliance records. What This Workflow Does Receives resolved support tickets via webhook from your support platform Extracts and normalizes ticket data (works with Zendesk, Freshdesk, and custom formats) Generates AI-powered summaries using OpenAI GPT-4, creating structured case studies with: Problem description Troubleshooting steps taken Final resolution Key takeaways and prevention tips Creates professional PDF documents with branded HTML templates Uploads PDFs to organized Google Drive folders Tracks all tickets in a Google Sheets database for reporting and analytics Sends Slack notifications to your team with links to completed documentation Handles errors gracefully with automatic alerts when issues occur Key Features Fully Automated:** Zero manual intervention after setup AI-Powered Documentation:** Intelligent summaries that extract insights from raw ticket data Professional Output:** Branded, print-ready PDFs with modern styling Multi-Platform Support:** Works with any support tool that can send webhooks Centralized Tracking:** All tickets logged in Google Sheets for easy reporting Error Handling:** Built-in failure detection with Slack alerts Customizable:** Easy to brand with your company colors, logo, and styling Scalable:** Handles hundreds of tickets per day Use Cases Knowledge Base Building:** Automatically create searchable documentation from real support cases Team Training:** Build a library of resolved issues for onboarding new support agents Compliance & Audit:** Maintain complete records of all customer interactions Performance Analytics:** Track resolution times, common issues, and agent performance Customer Success:** Share professional case studies with stakeholders Process Improvement:** Identify recurring issues and optimize workflows Prerequisites Required Services & Accounts n8n** (self-hosted or cloud) OpenAI Account** with API access PDFMunk Account** (for HTML → PDF conversion) Google Workspace** (for Drive & Sheets) Slack Workspace** (optional but recommended) Support Platform** that can send webhooks (Zendesk, Freshdesk, Intercom, etc.) Required Credentials OpenAI API Key PDFMunk API Key Google Drive OAuth2 credentials Google Sheets OAuth2 credentials Slack Bot Token (OAuth2) Setup Instructions 1. Import the Workflow Copy the workflow JSON. In n8n, click “Import from File” or “Import from Clipboard.” Paste and import. 2. Configure Credentials OpenAI API Get API key from OpenAI Add in n8n: Credentials → OpenAI API → Paste key PDFMunk API Sign up at pdfmunk.com Copy API key → Add in Credentials → HtmlcsstopdfApi Google Drive OAuth2 Create project at Google Cloud Console Enable Drive API Create OAuth 2.0 credentials Add in n8n: Credentials → Google Drive OAuth2 → Connect Google Sheets OAuth2 Enable Google Sheets API in the same project Add in n8n: Credentials → Google Sheets OAuth2 → Connect Slack OAuth2 Create app at Slack API Add scopes: chat:write, chat:write.public Install to workspace Add bot token in Credentials → Slack OAuth2 API 3. Configure Node Settings Google Drive Folder ID Create a folder in Drive for PDFs Copy folder ID from the URL → https://drive.google.com/drive/folders/FOLDER_ID_HERE Paste in the “Upload to Google Drive” node Google Sheets Configuration Create a new sheet titled “Support Ticket Documentation Log.” Add these headers in Row 1: | Ticket ID | Subject | Customer Name | Customer Email | Agent Name | Priority | Category | Resolved Date | Resolution Time | PDF Link | Document Generated | Status | | --------- | ------- | ------------- | -------------- | ---------- | -------- | -------- | ------------- | --------------- | -------- | ------------------ | ------ | Copy Sheet ID from URL → https://docs.google.com/spreadsheets/d/SHEET_ID_HERE/edit Paste it in the “Update Google Sheets” node configuration. Slack Channel ID Right-click your Slack channel → View Channel Details Copy the Channel ID Update it in: “Send Slack Notification” node “Error – PDF Failed” node “Error – Upload Failed” node 4. Configure Webhook in Support Tool Activate the workflow in n8n Copy the Webhook URL from the “Webhook – Receive Ticket” node In Zendesk/Freshdesk: Trigger: “Ticket Status = Resolved” Method: POST Paste the n8n webhook URL Send ticket data as JSON 5. Test the Workflow Click “Execute Workflow” manually Send a test webhook Verify each step completes successfully Check: Generated PDF in Google Drive Row entry in Google Sheets Slack notification delivery How It Works Webhook Trigger → Receives resolved ticket Data Extraction → Normalizes ticket fields AI Summarization (OpenAI) → Generates structured summary HTML Formatting → Styles and adds branding PDF Conversion (PDFMunk) → Converts HTML → PDF Google Drive Upload → Saves and returns shareable link Sheets Logging → Appends ticket info + PDF link Slack Notification → Notifies team with summary Error Handling → Detects and reports failures Result → Clean, documented ticket case study in minutes Customization Branding Update company name, logo URL, and color scheme in the “Format HTML” node. Default color: #4CAF50 → Replace with your brand color. AI Prompt Customization Modify “AI Summarization (OpenAI)” node to add: Industry-specific terms Additional sections or insights Different summary tone or length Notification Customization Add @mentions or emojis in Slack messages for better visibility. Data Flow Webhook → Extract Data → AI Summary → Format HTML → Convert to PDF ↓ Download PDF → Upload to Drive → Log in Sheets → Notify Team ↓ Error Handling (if any) Expected Output PDF Document Includes: Branded header with company name/logo Resolution time badge Ticket metadata (ID, priority, customer, agent, etc.) Full AI-generated case study Professional footer with timestamp Google Sheets Entry: All ticket info Resolution metrics Direct PDF link Status = “Generated” Slack Notification: Summary of ticket Clickable PDF link Timestamp Performance Processing Time:** 10–20 seconds/ticket Capacity:** 100+ tickets/day PDF Size:** 50–300 KB Troubleshooting Webhook not triggering → Check webhook URL, trigger conditions, and public access. PDF generation fails → Verify HTML syntax and PDFMunk API key. Google Drive upload fails → Re-authenticate credentials or check folder permissions. Slack notification missing → Ensure bot token, scopes, and channel ID are valid. Data extraction errors → Adjust field mappings or inspect payload format. Best Practices Test before production rollout Monitor first-week error logs Organize Drive by date/priority Validate Sheets columns Use a dedicated Slack channel Archive old PDFs regularly Review AI summaries weekly Document configuration changes Security Notes All credentials stored securely in n8n PDF links are restricted by Drive sharing settings Webhooks use HTTPS for secure data transfer No sensitive info logged in error messages Future Enhancements Multi-language summaries Integration with Confluence or Notion Customer satisfaction feedback link ML-based issue categorization Analytics dashboard Weekly email digests Public knowledge base generation Support Resources n8n Documentation n8n Community OpenAI API Docs PDFMunk Support Google Drive API Slack API Docs License This workflow template is provided as-is for use with n8n under the MIT License.
by Friedemann Schuetz
Welcome to my VEO3 Video Generator Workflow! This automated workflow transforms simple text descriptions into professional 8-second videos using Google's cutting-edge VEO3 AI model. Users submit video ideas through a web form, and the system automatically generates optimized prompts, creates high-quality videos with native audio, and delivers them via Google Drive - all powered by Claude 4 Sonnet for intelligent prompt optimization. This workflow has the following sequence: VEO3 Generator Form - Web form interface for users to input video content, format, and duration Video Prompt Generator - AI agent powered by Claude 4 Sonnet that: Analyzes user input for video content requirements Creates factual, professional video titles Generates detailed VEO3 prompts with subject, context, action, style, camera motion, composition, ambiance, and audio elements Optimizes prompts for 16:9 landscape format and 8-second duration Create VEO3 Video - Submits the optimized prompt to fal.ai VEO3 API for video generation Wait 30 seconds - Initial waiting period for video processing to begin Check VEO3 Status - Monitors the video generation status via fal.ai API Video completed? - Decision node that checks if video generation is finished If not completed: Returns to wait cycle If completed: Proceeds to video retrieval Get VEO3 Video URL - Retrieves the final video download URL from fal.ai Download VEO3 Video - Downloads the generated MP4 video file Merge - Combines video data with metadata for final processing Save Video to Google Drive - Uploads the video to specified Google Drive folder Video Output - Displays completion message with Google Drive link to user The following accesses are required for the workflow: Anthropic API** (Claude 4 Sonnet): Documentation Fal.ai API** (VEO3 Model): Create API key at https://fal.ai/dashboard/keys Google Drive API**: Documentation Workflow Features: User-friendly web form**: Simple interface for video content input AI-powered prompt optimization**: Claude 4 Sonnet creates professional VEO3 prompts Automatic video generation**: Leverages Google's VEO3 model via fal.ai Status monitoring**: Real-time tracking of video generation progress Google Drive integration**: Automatic upload and sharing of generated videos Structured output**: Consistent video titles and professional prompt formatting Audio optimization**: VEO3's native audio generation with ambient sounds and music Current Limitations: Format**: Only 16:9 landscape videos supported Duration**: Only 8-second videos supported Processing time**: Videos typically take 60-120 seconds to generate Use Cases: Content creation**: Generate videos for social media, websites, and presentations Marketing materials**: Create promotional videos and advertisements Educational content**: Produce instructional and explanatory videos Prototyping**: Rapid video concept development and testing Creative projects**: Artistic and experimental video generation Business presentations**: Professional video content for meetings and pitches Feel free to contact me via LinkedIn, if you have any questions!
by Daniel Shashko
How it Works This workflow accepts meeting transcripts via webhook (Zoom, Google Meet, Teams, Otter.ai, or manual notes), immediately processing them through an intelligent pipeline that eliminates post-meeting admin work. The system parses multiple input formats (JSON, form data, transcription outputs), extracting meeting metadata including title, date, attendees, transcript content, duration, and recording URLs. OpenAI analyzes the transcript to extract eight critical dimensions: executive summary, key decisions with ownership, action items with assigned owners and due dates, discussion topics, open questions, next steps, risks/blockers, and follow-up meeting requirements—all returned as structured JSON. The intelligence engine enriches each action item with unique IDs, priority scores (weighing urgency + owner assignment + due date), status initialization, and meeting context links, then calculates a completeness score (0-100) that penalizes missing owners and undefined deadlines. Multi-channel distribution ensures visibility: Slack receives formatted summaries with emoji categorization for decisions (✅), action items (🎯) with priority badges and owner assignments, and completeness scores (📊). Notion gets dual-database updates—meeting notes with formatted decisions and individual task cards in your action item database with full filtering and kanban capabilities. Task owners receive personalized HTML emails with priority color-coding and meeting context, while Google Calendar creates due-date reminders as calendar events. Every meeting logs to Google Sheets for analytics tracking: attendee count, duration, action items created, priority distribution, decision count, completeness score, and follow-up indicators. The workflow returns a JSON response confirming successful processing with meeting ID, action item count, and executive summary. The entire pipeline executes in 8-12 seconds from submission to full distribution. Who is this for? Product and engineering teams drowning in scattered action items across tools Remote-first companies where verbal commitments vanish after calls Executive teams needing auditable decision records without dedicated note-takers Startups juggling 10+ meetings daily without time for manual follow-up Operations teams tracking cross-functional initiatives requiring accountability Setup Steps Setup time:** 25-35 minutes Requirements:** OpenAI API key, Slack workspace, Notion account, Google Workspace (Calendar/Gmail/Sheets), optional transcription service Webhook Trigger: Automatically generates URL, configure as POST endpoint accepting JSON with title, date, attendees, transcript, duration, recording_url, organizer Transcription Integration: Connect Otter.ai/Fireflies.ai/Zoom webhooks, or create manual submission form OpenAI Analysis: Add API credentials, configure GPT-4 or GPT-3.5-turbo, temperature 0.3, max tokens 1500 Intelligence Synthesis: JavaScript calculates priority scores (0-40 range) and completeness metrics (0-100), customize thresholds Slack Integration: Create app with chat:write scope, get bot token, replace channel ID placeholder with your #meeting-summaries channel Notion Databases: Create "Meeting Notes" database (title, date, attendees, summary, action items, completeness, recording URL) and "Action Items" database (title, assigned to, due date, priority, status, meeting relation), share both with integration, add token Email Notifications: Configure Gmail OAuth2 or SMTP, customize HTML template with company branding Calendar Reminders: Enable Calendar API, creates events on due dates at 9 AM (adjustable), adds task owner as attendee Analytics Tracking: Create Google Sheet with columns for Meeting_ID, Title, Date, Attendees, Duration, Action_Items, High_Priority, Decisions, Completeness, Unassigned_Tasks, Follow_Up_Needed Test: POST sample transcript, verify Slack message, Notion entries, emails, calendar events, and Sheets logging Customization Guidance Meeting Types:** Daily standups (reduce tokens to 500, Slack-only), sprint planning (add Jira integration), client calls (add CRM logging), executive reviews (stricter completeness thresholds) Priority Scoring:** Add urgency multiplier for <48hr due dates, owner seniority weights, customer impact flags AI Prompt:** Customize to emphasize deadlines, blockers, or technical decisions; add date parsing for phrases like "by end of week" Notification Routing:** Critical priority (score >30) → Slack DM + email, High (20-30) → channel + email, Medium/Low → email only Tool Integrations:** Add Jira/Linear for ticket creation, Asana/Monday for project management, Salesforce/HubSpot for CRM logging, GitHub for issue creation Analytics:** Build dashboards for meeting effectiveness scores, action item velocity, recurring topic clustering, team productivity metrics Cost Optimization:** ~1,200 tokens/meeting × $0.002/1K (GPT-3.5) = $0.0024/meeting, use batch API for 50% discount, cache common patterns Once configured, this workflow becomes your team's institutional memory—capturing every commitment and decision while eliminating hours of weekly admin work, ensuring accountability is automatic and follow-through is guaranteed. Built by Daniel Shashko Connect on LinkedIn
by explorium
Research Agent - Automated Sales Meeting Intelligence This n8n workflow automatically prepares comprehensive sales research briefs every morning for your upcoming meetings by analyzing both the companies you're meeting with and the individual attendees. The workflow connects to your calendar, identifies external meetings, enriches companies and contacts with deep intelligence from Explorium, and delivers personalized research reports—giving your sales team everything they need for informed, confident conversations. DEMO Template Demo Credentials Required To use this workflow, set up the following credentials in your n8n environment: Google Calendar (or Outlook) Type:** OAuth2 Used for:** Reading daily meeting schedules and identifying external attendees Alternative: Microsoft Outlook Calendar Get credentials at Google Cloud Console Explorium API Type:** Generic Header Auth Header:** Authorization Value:** Bearer YOUR_API_KEY Used for:** Business/prospect matching, firmographic enrichment, professional profiles, LinkedIn posts, website changes, competitive intelligence Get your API key at Explorium Dashboard Explorium MCP Type:** HTTP Header Auth Used for:** Real-time company intelligence and supplemental research for AI agents Connect to: https://mcp.explorium.ai/mcp Anthropic API Type:** API Key Used for:** AI-powered company and attendee research analysis Get your API key at Anthropic Console Slack (or preferred output) Type:** OAuth2 Used for:** Delivering research briefs Alternative options: Google Docs, Email, Microsoft Teams, CRM updates Go to Settings → Credentials, create these credentials, and assign them in the respective nodes before running the workflow. Workflow Overview Node 1: Schedule Trigger Automatically runs the workflow on a recurring schedule. Type:** Schedule Trigger Default:** Every morning before business hours Customizable:** Set to any interval (hourly, daily, weekly) or specific times Alternative Trigger Options: Manual Trigger:** On-demand execution Webhook:** Triggered by calendar events or CRM updates Node 2: Get many events Retrieves meetings from your connected calendar. Calendar Source:** Google Calendar (or Outlook) Authentication:** OAuth2 Time Range:** Current day + 18 hours (configurable via timeMax) Returns:** All calendar events with attendee information, meeting titles, times, and descriptions Node 3: Filter for External Meetings Identifies meetings with external participants and filters out internal-only meetings. Filtering Logic: Extracts attendee email domains Excludes your company domain (e.g., 'explorium.ai') Excludes calendar system addresses (e.g., 'resource.calendar.google.com') Only passes events with at least one external attendee Important Setup Note: Replace 'explorium.ai' in the code node with your company domain to properly filter internal meetings. Output: Events with external participants only external_attendees: Array of external contact emails company_domains: Unique list of external company domains per meeting external_attendee_count: Number of external participants Company Research Pipeline Node 4: Loop Over Items Iterates through each meeting with external attendees for company research. Node 5: Extract External Company Domains Creates a deduplicated list of all external company domains from the current meeting. Node 6: Explorium API: Match Business Matches company domains to Explorium's business entity database. Method:** POST Endpoint:** /v1/businesses/match Authentication:** Header Auth (Bearer token) Returns: business_id: Unique Explorium identifier matched_businesses: Array of matches with confidence scores Company name and basic info Node 7: If Validates that a business match was found before proceeding to enrichment. Condition:** business_id is not empty If True:** Proceed to parallel enrichment nodes If False:** Skip to next company in loop Nodes 8-9: Parallel Company Enrichment Node 8: Explorium API: Business Enrich Endpoints:** /v1/businesses/firmographics/enrich, /v1/businesses/technographics/enrich Enrichment Types:** firmographics, technographics Returns:** Company name, description, website, industry, employees, revenue, headquarters location, ticker symbol, LinkedIn profile, logo, full tech stack, nested tech stack by category, BI & analytics tools, sales tools, marketing tools Node 9: Explorium API: Fetch Business Events Endpoint:** /v1/businesses/events/fetch Event Types:** New funding rounds, new investments, mergers & acquisitions, new products, new partnerships Date Range:** September 1, 2025 - November 4, 2025 Returns:** Recent business milestones and financial events Node 10: Merge Combines enrichment responses and events data into a single data object. Node 11: Cleans Merge Data Output Transforms merged enrichment data into a structured format for AI analysis. Node 12: Company Research Agent AI agent (Claude Sonnet 4) that analyzes company data to generate actionable sales intelligence. Input: Structured company profile with all enrichment data Analysis Focus: Company overview and business context Recent website changes and strategic shifts Tech stack and product focus areas Potential pain points and challenges How Explorium's capabilities align with their needs Timely conversation starters based on recent activity Connected to Explorium MCP: Can pull additional real-time intelligence if needed to create more detailed analysis Node 13: Create Company Research Output Formats the AI analysis into a readable, shareable research brief. Attendee Research Pipeline Node 14: Create List of All External Attendees Compiles all unique external attendee emails across all meetings. Node 15: Loop Over Items2 Iterates through each external attendee for individual enrichment. Node 16: Extract External Company Domains1 Extracts the company domain from each attendee's email. Node 17: Explorium API: Match Business1 Matches the attendee's company domain to get business_id for prospect matching. Method:** POST Endpoint:** /v1/businesses/match Purpose:** Link attendee to their company Node 18: Explorium API: Match Prospect Matches attendee email to Explorium's professional profile database. Method:** POST Endpoint:** /v1/prospects/match Authentication:** Header Auth (Bearer token) Returns: prospect_id: Unique professional profile identifier Node 19: If1 Validates that a prospect match was found. Condition:** prospect_id is not empty If True:** Proceed to prospect enrichment If False:** Skip to next attendee Node 20: Explorium API: Prospect Enrich Enriches matched prospect using multiple Explorium endpoints. Enrichment Types:** contacts, profiles, linkedin_posts Endpoints:** /v1/prospects/contacts/enrich, /v1/prospects/profiles/enrich, /v1/prospects/linkedin_posts/enrich Returns: Contacts:** Professional email, email status, all emails, mobile phone, all phone numbers Profiles:** Full professional history, current role, skills, education, company information, experience timeline, job titles and seniority LinkedIn Posts:** Recent LinkedIn activity, post content, engagement metrics, professional interests and thought leadership Node 21: Cleans Enrichment Outputs Structures prospect data for AI analysis. Node 22: Attendee Research Agent AI agent (Claude Sonnet 4) that analyzes prospect data to generate personalized conversation intelligence. Input: Structured professional profile with activity data Analysis Focus: Career background and progression Current role and responsibilities Recent LinkedIn activity themes and interests Potential pain points in their role Relevant Explorium capabilities for their needs Personal connection points (education, interests, previous companies) Opening conversation starters Connected to Explorium MCP: Can gather additional company or market context if needed Node 23: Create Attendee Research Output Formats attendee analysis into a readable brief with clear sections. Node 24: Merge2 Combines company research output with attendee information for final assembly. Node 25: Loop Over Items1 Manages the final loop that combines company and attendee research for output. Node 26: Send a message (Slack) Delivers combined research briefs to specified Slack channel or user. Alternative Output Options: Google Docs:** Create formatted document per meeting Email:** Send to meeting organizer or sales rep Microsoft Teams:** Post to channels or DMs CRM:** Update opportunity/account records with research PDF:** Generate downloadable research reports Workflow Flow Summary Schedule: Workflow runs automatically every morning Fetch Calendar: Pull today's meetings from Google Calendar/Outlook Filter: Identify meetings with external attendees only Extract Companies: Get unique company domains from external attendees Extract Attendees: Compile list of all external contacts Company Research Path: Match Companies: Identify businesses in Explorium database Enrich (Parallel): Pull firmographics, website changes, competitive landscape, events, and challenges Merge & Clean: Combine and structure company data AI Analysis: Generate company research brief with insights and talking points Format: Create readable company research output Attendee Research Path: Match Prospects: Link attendees to professional profiles Enrich (Parallel): Pull profiles, job changes, and LinkedIn activity Merge & Clean: Combine and structure prospect data AI Analysis: Generate attendee research with background and approach Format: Create readable attendee research output Delivery: Combine: Merge company and attendee research for each meeting Send: Deliver complete research briefs to Slack/preferred platform This workflow eliminates manual pre-meeting research by automatically preparing comprehensive intelligence on both companies and individuals—giving sales teams the context and confidence they need for every conversation. Customization Options Calendar Integration Works with multiple calendar platforms: Google Calendar:** Full OAuth2 integration Microsoft Outlook:** Calendar API support CalDAV:** Generic calendar protocol support Trigger Flexibility Adjust when research runs: Morning Routine:** Default daily at 7 AM On-Demand:** Manual trigger for specific meetings Continuous:** Hourly checks for new meetings Enrichment Depth Add or remove enrichment endpoints: Company:** Technographics, funding history, news mentions, hiring signals Prospects:** Contact information, social profiles, company changes Customizable:** Select only needed data to optimize speed and costs Research Scope Configure what gets researched: All External Meetings:** Default behavior Filtered by Keywords:** Only meetings with specific titles By Attendee Count:** Only meetings with X+ external attendees By Calendar:** Specific calendars only Output Destinations Deliver research to your preferred platform: Messaging:** Slack, Microsoft Teams, Discord Documents:** Google Docs, Notion, Confluence Email:** Gmail, Outlook, custom SMTP CRM:** Salesforce, HubSpot (update account notes) Project Management:** Asana, Monday.com, ClickUp AI Model Options Swap AI providers based on needs: Default: Anthropic Claude (Sonnet 4) Alternatives: OpenAI GPT-4, Google Gemini Setup Notes Domain Configuration: Replace 'explorium.ai' in the Filter for External Meetings code node with your company domain Calendar Connection: Ensure OAuth2 credentials have calendar read permissions Explorium Credentials: Both API key and MCP credentials must be configured Output Timing: Schedule trigger should run with enough lead time before first meetings Rate Limits: Adjust loop batch sizes if hitting API rate limits during enrichment Slack Configuration: Select destination channel or user for research delivery Data Privacy: Research is based on publicly available professional information and company data This workflow acts as your automated sales researcher, preparing detailed intelligence reports every morning so your team walks into every meeting informed, prepared, and ready to have meaningful conversations that drive business forward.
by Avkash Kakdiya
Quick Overview This workflow runs daily to pull cloud spend from a billing API, compare it to a Google Sheets rolling baseline, and alert on cost spikes by creating a Jira incident, posting to Slack, emailing Finance via Gmail, and logging the run back to Google Sheets. How it works Runs every day at 08:00 on a schedule. Fetches the latest daily cloud cost from a cloud billing API and loads historical daily costs from a Google Sheets “Baseline” sheet. Calculates a trailing-average baseline, computes the percentage change for today, and marks the result as valid only when enough non-zero history exists. Stops processing when the data is invalid or the increase is below the configured spike threshold. Classifies the spike severity (Watch/High/Critical) based on the percentage increase. Creates a Jira issue with the spike details, then posts an alert to a Slack channel and emails a text summary to Finance via Gmail. Appends the run details (date, service, severity, costs, and percent change) to the Google Sheets baseline sheet for future comparisons. Setup Add an HTTP Header Auth credential for your cloud billing API and replace the example Authorization header value in the HTTP request. Connect Google Sheets OAuth credentials and set the Google Sheet document ID and ensure a “Baseline” sheet exists with a historical daily cost column (and is used for logging). Review and adjust the spike threshold (currently 25%) and the minimum baseline requirements (at least 3 non-zero rows) in the code step. Connect Jira credentials and set your Jira project ID and issue type ID for incident creation. Connect Slack credentials and set the target channel ID, then connect Gmail credentials and update the Finance recipient email address. Adjust the schedule time (currently 08:00) to match when your daily billing data is available.
by Monfort N. Brian | 宁俊
Quick Overview This workflow receives support messages via a webhook, normalizes payloads from Gmail, Zendesk, Intercom, and HelpScout, and uses Anthropic Claude to detect escalation signals and severity, then posts alerts to Slack and creates Linear tickets for actionable cases. How it works Receives an inbound support message via a POST webhook. Normalizes the incoming payload (Gmail, Zendesk, Intercom, HelpScout, or a raw test payload) into a consistent schema with customer details, subject, body text, and a source URL. Sends the normalized message to Anthropic Claude and parses the response into structured JSON containing escalation flags, severity, trigger phrases, a summary, and a suggested reply opening. Stops processing silently when the message is not an escalation (severity is none). Builds a formatted Slack alert and Linear issue payload, including SLA guidance, detected signals, and links back to the original conversation. Posts the escalation alert to a configured Slack channel. Creates a Linear ticket for critical, high, and medium severity escalations and sends a Slack DM to a manager for critical and high cases. Setup Add an Anthropic API credential for the Anthropic Chat Model node. Add a Slack OAuth2 credential, set the target channel (for example, #escalations), and set the manager’s Slack user ID for direct messages. Add a Linear OAuth2 credential and replace the placeholder Linear team ID used when creating tickets. Copy the webhook URL from the webhook trigger and configure Gmail/Zendesk/Intercom/HelpScout (or your custom source) to POST message payloads to it in one of the supported formats.
by Xander Groesbeek
Quick Overview This workflow receives inbound support emails via an EmailConnect webhook, filters likely spam, matches the message against a Notion Q&A knowledge base using an OpenRouter chat model, and then either drafts and sends a grounded reply or escalates the request to a human by email. How it works Receives an inbound email payload via an EmailConnect webhook. Drops the message if the provided spam score exists and is greater than 10. Loads all Q&A entries from a Notion database and formats them into a comparison list alongside the sender and message text. Uses an OpenRouter chat model (with per-sender memory) to select the closest knowledge base question ID or return NO_MATCH when confidence is below 85%. Validates the returned ID against the Notion data and routes the request to either the answer path or the escalation path. If a match is found, uses an OpenRouter chat model (with per-sender memory) to draft a structured reply grounded in the matched Notion answer and sends it to the customer via SMTP. If no match is found, uses an OpenRouter chat model (with per-sender memory) to draft a structured acknowledgement, emails your team the original request via SMTP, and sends the acknowledgement to the customer via SMTP. Setup Configure your EmailConnect alias to POST to the workflow webhook URL for the “When Email Received” trigger. Add Notion API credentials and replace YOUR_NOTION_DATABASE_ID with your Notion database ID, ensuring the database exposes question, answer, and help_url properties. Add an OpenRouter credential for the three OpenRouter chat model nodes. Add an SMTP credential and update the from address, escalation recipient (alerts@example.com), and optional BCC settings in the email send steps. Adjust the spam-score cutoff (currently > 10) and the match confidence threshold in the matching prompt (currently 85%) to fit your inbox quality.
by Jitesh Dugar
Quick Overview This workflow runs daily to scrape competitor Shopify app reviews via Apify, logs and deduplicates new low-rated reviews in Google Sheets, looks up the reviewer’s website with Serper, enriches contacts with Hunter, generates a personalized outreach email with OpenAI, saves it as a Gmail draft, and alerts in Slack. How it works Runs every day on a schedule. Scrapes recent reviews from a set of Shopify app review URLs using an Apify actor, removes duplicate review IDs, and keeps only reviews with a rating of 4 or less. Processes each review one at a time and checks Google Sheets to see whether the Review ID already exists. For new reviews, appends the review details to Google Sheets and posts a “new review detected” alert to a Slack channel. Searches Google via the Serper API for the reviewer’s official website, extracts a clean domain from the top result, and uses Hunter to find a contact email. Uses OpenAI to draft a short personalized outreach email based on the review, saves it as a Gmail draft, updates the corresponding Google Sheets row with contact details and the draft, and posts a “draft ready” alert to Slack. Setup Connect credentials for Apify, Google Sheets, Serper (HTTP Header Auth API key), Hunter, OpenAI, Gmail, and Slack. Update the list of competitor Shopify review page URLs and the Apify actor input settings (for example, maxItems) to match what you want to monitor. Point the Google Sheets nodes to your spreadsheet and ensure it includes columns like Review ID, Date, Rating, Review Text, Competitor URL, Reviewer Domain, Website, Contact Name, Contact Title, Contact Email, Status, and Draft-Email. Select the Slack channel where both “new review” and “draft ready” messages should be posted. Ensure your Gmail account is authorized and able to create drafts addressed to the email returned by Hunter.
by Monisha Panda
Description This n8n template demonstrates how to build an AI-powered Market Research Assistant using a multi-agent workflow. It helps you get a 360-degree view of a product idea or research topic by analysing: Customer insights and pain points Market size and macro/micro-economic trends Competitive landscape and alternatives The workflow mirrors how product managers and strategy teams conduct discovery — by breaking down research into parallel workstreams and then synthesizing insights into a single narrative. How it works Planner Agent The main agent receives your research topic as input and defines: Research objective Key areas of focus (Customer, Market, Competition) Assumptions and constraints Parallel Research Agents Based on the planner’s output, three specialist agents run in parallel: Customer Insights Agent Researches public sources such as articles and forums to infer customer behaviour, pain points, and existing tools. Market Scan Agent Analyses macro-economic and micro-economic trends, estimates TAM/SAM/SOM, and highlights key risks and assumptions. Competitor Insights Agent Identifies existing competitors and substitutes and summarises how they are positioned in the market. Synthesis Agent The outputs from all research agents are consolidated and analysed by a synthesis agent, which produces a market discovery memo. Final Output The discovery memo is generated as a document and sent to your email. How to use Trigger the workflow via the chat message node. Provide your research topic or product idea, along with optional context such as target market. The workflow runs automatically and delivers a structured discovery memo to your inbox. Setup Steps API credentials for: Groq or OpenAI (LLM) Documentero (document generation) A configured Documentero template Gmail OAuth or email credentials for delivery of memo