Workflow Templates

How It Works This workflow automates platform trust and safety operations by deploying a multi-agent AI system that detects abuse signals, investigates behaviour, scores risk, checks policy compliance, and enforces actions automatically. Designed for platform safety teams, content moderation managers, and compliance officers, it eliminates manual triage delays and ensures high-severity violations are actioned immediately. An abuse signal webhook triggers behaviour analysis via OpenAI, classifying signals by severity. A routing node directs cases to a Governance Agent, which orchestrates Investigation, Risk Scoring, and Policy Compliance Checker sub-agents. Enforcement data is prepared, then routed by action type-logging to abuse records, alerting the security team via Slack, sending escalation emails, or triggering auto-enforcement actions based on threshold checks—before all outcomes are logged. Setup Steps Configure Abuse Signal Webhook URL and authenticate incoming POST requests. Add OpenAI API credentials to all OpenAI Model nodes. Connect Google Sheets for abuse records and enforcement action logging. Configure Slack credentials and set security team alert channel. Add Gmail/SMTP credentials to Send Escalation Email node. Prerequisites Slack workspace with bot token Gmail or SMTP credentials Google Sheets for abuse and enforcement logging Use Cases Real-time abuse detection and auto-suspension on social platforms Customization Replace OpenAI with Anthropic Claude or NVIDIA NIM models Benefits Eliminates manual abuse triage with real-time AI signal processing

Who’s it for This workflow is designed for Japanese-speaking individuals who want to efficiently stay up to date with practical, experience-based AI and engineering insights shared by developers on platforms like Qiita and note. It specifically targets users who prefer real-world knowledge such as implementation examples, troubleshooting solutions, and hands-on AI use cases written in Japanese, rather than generalized global IT news or curated media content. The workflow is optimized for those who want to quickly consume high-quality Japanese technical content on a daily basis. What it does This workflow collects, processes, and summarizes Japanese AI and engineering-related articles published within the last 24 hours from Qiita and note RSS feeds. It merges multiple RSS sources, filters only recent articles (last 24 hours), and prepares structured data for AI processing. Then, it uses AI to evaluate and rank the articles, selects the most valuable ones, retrieves each article page, extracts readable content, and generates structured summaries in Japanese, including: Summary Target audience Use cases Merits Demerits Finally, it formats the results and sends a daily digest to Slack in Japanese. Users can also customize RSS sources to match their preferred content. How it works A scheduled trigger starts the workflow automatically. RSS feeds from Qiita and note are fetched and merged. Articles are filtered to only include those published within the last 24 hours. Articles are normalized into a structured format for AI processing. Gemini evaluates and ranks articles based on usefulness and selects the top 10. Article links are prepared and each page is fetched. HTML is cleaned and converted into readable text. OpenAI generates structured summaries in Japanese. The final digest is formatted and posted to Slack in Japanese. Requirements Google Gemini API credentials OpenAI API credentials Slack OAuth2 credentials A Slack channel for notifications How to set up Add your API credentials in n8n, set the Slack destination channel, review and adjust the AI prompts if needed, and activate the workflow. You can also customize RSS sources depending on your preferred Japanese content (e.g., specific hashtags, niche blogs, or categories). How to customize the workflow You can customize this workflow by: Adding or replacing RSS sources (e.g., Japanese niche engineering blogs or communities) Adjusting filtering conditions (e.g., time range beyond 24 hours or keyword-based filtering) Refining AI scoring criteria to better match your interests Modifying summary structure or output format (Japanese-focused customization) Customizing Slack message layout for better readability Changing the output language (default is Japanese)

How It Works This workflow automates enterprise claims cost leakage detection by identifying overpayments, policy deviations, and pricing inconsistencies across claims data. It supports claims operations, finance, and audit teams by providing continuous, AI-driven monitoring without manual review. Claims data is ingested through parallel HTTP requests, including claim history, policy details, pricing rules, and enrichment data. Historical claim patterns feed calculator-based risk scoring to flag potential leakage scenarios. All data streams are consolidated and analyzed using GPT-4 with structured outputs to detect anomalies, quantify leakage risk, and recommend corrective adjustments. The workflow generates claim-level findings and routes outcomes by severity: high-risk leakage triggers immediate email and Slack alerts, while lower-risk issues are compiled into periodic audit and recovery reports. Setup Steps Configure HTTP nodes with competitor website APIs Add OpenAI API key to Chat Model node for AI analysis Connect Gmail account and set leadership distribution list Integrate Slack workspace and configure strategy team Adjust Schedule node timing for preferred monitoring frequency Prerequisites OpenAI API key, competitor data source API access, vendor monitoring service credentials Use Cases SaaS companies tracking competitor feature releases and pricing changes Customization Modify risk scoring formulas in Calculator nodes for industry-specific metrics Benefits Transforms hours of manual competitor research into automated minutes-long cycles

How It Works This automated disaster response workflow streamlines emergency management by monitoring multiple alert sources and coordinating property protection teams. Designed for property managers, insurance companies, and emergency response organizations, it solves the critical challenge of rapidly identifying at-risk properties and deploying resources during disasters.The system continuously monitors weather, seismic, and flood alerts from authoritative sources. When threats are detected, it cross-references property databases to identify affected locations, calculates insurance exposure, and generates damage assessments using OpenAI's GPT-4. Teams receive automated maintenance schedules while property owners and insurers get instant email notifications with comprehensive reports. This eliminates manual monitoring, reduces response time from hours to minutes, and ensures no vulnerable properties are overlooked during emergencies. Setup Steps Configure alert fetch nodes with weather/seismic/flood API endpoints Connect property database credentials (specify database type) Add OpenAI API key for GPT-4 damage assessments Set up Gmail/SMTP credentials for owner and insurer notifications Customize insurance calculation formulas and team scheduling logic Prerequisites Weather/seismic/flood alert API access, property database (SQL/Sheets/Airtable) Use Cases Insurance companies automating claims preparation, property management firms protecting rental portfolios Customization Modify alert source APIs, adjust damage assessment prompts Benefits Reduces emergency response time by 90%, eliminates manual alert monitoring

How It Works Top Branch Workflow* 1. The Data Scientist: Ingest: Pulls historical sales data from Google Sheets. Math Engine: Runs 7 statistical algorithms (e.g., Seasonal Naive, Linear Trend, Regression). It backtests them against your history and scientifically selects the winner with the lowest error rate. 2. The Data Analyst: Interpret: The AI Agent takes the mathematical output and translates it into business insights, assigning confidence scores based on error margins. Report: Generates a visual trend chart (PNG) and sends a complete briefing to your phone. Bottom Branch Workflow* 3. The Consultant: AI Agent 2 handles the follow-up questions. It pulls the latest analysis context and checks historical rate data to give an informed answer. Recall: When you ask a question via WhatsApp, the bot retrieves the saved forecast state. Answer: It acts as an on-demand analyst, comparing current forecasts against historical actuals to give you instant answers. Setup Steps 1) Google Sheet: Prepare columns: Year, Month, Sales. Map the Sheet ID in the "Workflow Configuration" node. 2) Forecast Engine: No config needed. It automatically detects seasonality vs. linear trends. 3) Database: Create a table latest_forecast to store the JSON output. 4) Credentials: Connect Google Sheets, OpenAI, and WhatsApp Use Cases & Benefits For Business Owners: Gain enterprise-grade forecasting on autopilot. Always have a sophisticated financial outlook running in the background 24/7. For Sales Leaders: Get immediate visibility into future revenue trends. Bypass the wait for end-of-month manual reports and get a strategic "pulse check" delivered instantly to your phone. 🤖Virtual Data Team: Instantly add the capabilities of a Data Scientist and Data Analyst to your business or division. It works alongside your existing team to handle the heavy lifting, or stands in as your dedicated automated department. 🧠Precision & Trust: Combines the best of both worlds: rigorous, deterministic code for the math (no hallucinations) and advanced AI for the strategic explanation. You get numbers you can trust with context you can use. ⚡Decision-Ready Insights: Stop digging through dashboards. High-level intelligence is pushed directly to you on WhatsApp, allowing you to make faster, data-driven decisions from anywhere. 📬 Want to Customize This? leelin.business@gmail.com

Research competitors and generate market insights with Claude AI and Notion Who is this for SaaS product managers, startup founders, and marketing teams who need to stay informed about competitor movements without manual monitoring. Perfect for teams who want to automate competitive intelligence gathering and respond quickly to market changes. How it works The workflow runs weekly, automatically scraping competitor websites and pricing pages using HTTP requests. A code node extracts key content and creates content hashes to detect changes from previous scans. When changes are detected, Claude AI analyzes the updates and provides strategic insights about pricing shifts, feature launches, or messaging changes. All competitor data and AI analysis are automatically saved to a Notion database for historical tracking. Important changes trigger immediate Slack notifications with actionable insights. At the end of each week, a comprehensive report summarizing all competitor activity is generated and emailed to your team. How to set up Configure competitor URLs in the Set node by adding websites, pricing pages, and feature pages you want to monitor. Set up API credentials for Claude AI, Notion, Slack, and Gmail. Create a Notion database with properties for competitor name, URL, content hash, AI analysis, and scan date. Define environment variables for your Notion database ID, Slack channel, and team email list. Requirements Anthropic Claude API key for competitive analysis Notion workspace with API access for data storage Slack workspace for urgent alerts Gmail account for weekly reporting Basic HTML/CSS knowledge helpful for customizing content extraction How to customize Adjust the schedule trigger frequency, modify urgent notification keywords in the priority evaluation code, or customize the Claude AI analysis prompt to focus on specific competitive aspects like pricing, features, or market positioning.

This workflow is a complete, production-ready solution for recovering abandoned carts in Shopify stores using a multi-channel, multi-touch approach. It automates personalized follow-ups via Email, SMS, and WhatsApp, tracks every customer interaction for multi-touch attribution, and enables advanced retargeting and analytics. Key features: Multi-step, timed recovery sequence (Email → SMS → Email → WhatsApp) Customer segmentation (new, returning, VIP) and A/B testing Dynamic discounting and personalized messaging Touchpoint logging to Google Sheets for attribution analysis Facebook Custom Audience retargeting for unrecovered carts Slack notifications for high-value cart recoveries What does this workflow do? Listens for abandoned cart events from Shopify (or any e-commerce platform) via webhook. Normalizes and enriches cart data by fetching full cart details and customer purchase history. Predicts the likely reason for abandonment (e.g., price sensitivity, checkout complexity, technical issues) using rule-based logic. Segments the customer (new, returning, VIP), assigns an A/B test group, and generates a personalized discount and checkout URL. Runs a timed, multi-channel recovery sequence: 1 hour after abandonment: Checks if the order is completed. If not, sends a personalized Email #1 and logs the touchpoint. 4 hours after abandonment: Checks again. If not recovered, sends an SMS with a discount code and logs the touchpoint. 24 hours after abandonment: Checks again. If not recovered, sends Email #2 (with social proof/urgency) and logs the touchpoint. 48 hours after abandonment: Final check. If not recovered, sends a WhatsApp reminder and logs the touchpoint. If the cart is still not recovered: Hashes customer identifiers and adds them to a Facebook Custom Audience for retargeting. Logs every touchpoint (email, SMS, WhatsApp) to a Google Sheet for multi-touch attribution analysis. Sends a Slack notification if a high-value cart is recovered. Why is this workflow useful? Boosts recovery rates:** By using multiple channels and personalized timing, you maximize the chance of recovering lost sales. Improves attribution:** Every customer interaction is logged, so you can analyze which channels and messages drive conversions. Enables advanced retargeting:** Unrecovered carts are automatically added to a Facebook Custom Audience for paid retargeting. Saves time:** Fully automated, with easy configuration for your store, messaging, and analytics. Scalable and extensible:** Easily adapt the sequence, add more channels, or integrate with other tools. How to install and configure 1. Prerequisites n8n instance (v2.0.2+ recommended) Shopify store with API access Accounts and API credentials for: SendGrid (email) Twilio (SMS) WhatsApp Business API Google Sheets (service account) Facebook Graph API (for Custom Audiences) Slack (for notifications) 2. Setup steps Import the workflow into your n8n instance. Configure the “Workflow Configuration” node: Set your Shopify domain, API URLs, Google Sheets ID, and high-value threshold. Connect all required credentials in the respective nodes: Shopify, SendGrid, Twilio, WhatsApp, Google Sheets, Facebook Graph API, Slack. Create a Google Sheet named “Touchpoints” with columns: cart_id, customer_id, touchpoint_type, timestamp, channel, ab_group. Set up the webhook in your Shopify store (or e-commerce platform) to trigger the workflow on cart abandonment. Test the workflow with a sample abandoned cart event to ensure emails, SMS, WhatsApp, and logging work as expected. Enable the workflow in n8n for live operation. Node-by-node breakdown Abandoned Cart Webhook:** Receives abandoned cart events. Workflow Configuration:** Stores global settings (API URLs, Shopify domain, Google Sheets ID, high-value threshold). Normalize Cart Data:** Cleans and standardizes incoming cart data. Fetch Cart Details / Fetch Customer History:** Enriches data with full cart and customer info. Predict Abandonment Reason:** Uses business logic to guess why the cart was abandoned. Personalization Engine:** Segments the customer, assigns A/B group, calculates discount, and builds checkout URL. Customer Segment Check / Device Type Check:** Applies routing logic for personalized messaging. Wait / Check Order Status / Generate & Send Messages:** Timed sequence for Email, SMS, and WhatsApp, with order status checks at each step. Log Touchpoint (Google Sheets):** Records every message sent for attribution. Attribution Merge:** Combines all touchpoints into a single journey for analysis. Hash Customer Data for Facebook / Add to Retargeting Audience:** Adds unrecovered carts to a Facebook Custom Audience. Check Cart Value Threshold / Notify High-Value Recovery:** Sends Slack alerts for high-value recoveries. Customization tips Adjust wait times and message content to fit your brand and audience. Add or remove channels (e.g., push notifications, phone calls) as needed. Expand the Google Sheet for deeper analytics (e.g., add UTM parameters, campaign IDs). Integrate with your CRM or analytics platform for end-to-end tracking. Troubleshooting Make sure all API credentials are set and tested. Check Google Sheets permissions for the service account. Test each channel (email, SMS, WhatsApp) individually before going live. Review the workflow execution logs in n8n for errors or failed steps.

Analyze Meta ads with Gemini and Google Sheets Stop manually exporting CSVs and start automating your marketing insights. This workflow is designed for Marketing Agencies, Freelancers, and Media Buyers who want to keep a daily pulse on their Meta (Facebook/Instagram) Ads performance without logging into Ads Manager. It doesn't just scrape data; it uses Google Gemini AI to act as a virtual data analyst. It reviews your campaigns, identifies winning/losing creatives, and writes strategic suggestions for both your agency team and your clients. 🚀 What this workflow does Extracts Data: Wakes up every morning (6:00 AM) to fetch yesterday's Ad and Campaign performance from the Facebook Graph API. Cleans & Filters: Automatically ignores paused or zero-spend campaigns to keep your reports clean. Structuring: Uses a Code node to group Ads intelligently under their respective Ad Sets and Campaigns. AI Analysis: Sends the structured data to Google Gemini. The AI analyzes CTR, CPC, and Spend to identify the "Best Performing Ad" and "Worst Performing Ad" per Ad Set. Reporting: Saves raw Campaign Data to Google Sheets. Saves raw Ad Data to Google Sheets. Saves AI-Generated Insights (Client & Agency suggestions) to a dedicated sheet. Error Handling: If anything breaks (e.g., API token expiry), it instantly sends you an alert via Gmail with the error details. 💡 Key Features Zero-Spend Filter:** Keeps your spreadsheet tidy by excluding inactive ads. Hierarchical Data Processing:** Groups data logically so the AI understands the context of your tests. Dual-Perspective Insights:** The AI generates two types of advice: For the Client: Simple, performance-based updates. For the Agency: Technical optimization tips (e.g., "Pause Ad B, Scale Ad A"). Robust Error Monitoring:** Includes a dedicated error workflow to notify you of failures. 🛠️ Prerequisites To use this template, you will need: Meta/Facebook Developer App:** A System User Access Token with ads_read permission. Google Cloud Console Project:** Enabled APIs for Google Sheets, Gmail, and Vertex AI (Gemini). Google Sheet:** A sheet with three tabs: Campaigns, Ads, and AI_Insights. 📝 Setup Instructions Configure Credentials: Connect your Facebook Graph API and Google accounts in n8n. Set Configuration Node: Open the "Set Configuration" node and paste your Ad Account ID and Email Address for error alerts. Link Google Sheet: Open the three Google Sheets nodes and select your spreadsheet file. Activate: Turn on the workflow and let it run daily! Need help setting this up or want a custom automation for your agency? I specialize in building agentic workflows for consultants and agencies. 📧 Contact me: bakdaulet.mph@gmail.com

How it works This workflow runs on a daily schedule to analyze all Closed–Lost deals from your CRM and uncover the true reason behind each loss. It uses AI to classify the primary loss category, generate a confidence-backed explanation, and then create a realistic re-engagement strategy for every deal. All insights are consolidated into leadership-ready email and Slack summaries. Every analyzed deal and revival plan is logged for long-term tracking and audits. Step-by-step Trigger and fetch lost deals** Schedule Trigger – Runs the workflow automatically at a defined time. Get many deals – Fetches all deal records from the CRM. If – Filters only deals marked as Closed–Lost. Edit Fields – Standardizes key deal attributes like amount, industry, owner, and loss reason. Analyze loss reasons and generate revival strategies** Brief Explanation Creator – Uses AI to identify the primary loss category with confidence. Code in JavaScript – Parses and normalizes AI loss analysis output. Merge – Combines deal data with loss insights. Feedback Creator – Generates a practical re-engagement strategy for each lost deal. Code in JavaScript7 – Parses and safeguards revival strategy outputs. Merge4 – Merges deal details, loss analysis, and revival strategy into one final dataset. Report, notify, and store results** Code in JavaScript11 – Builds a consolidated HTML summary email. Send a message4 – Sends the summary to stakeholders via email. Code in JavaScript12 – Creates a structured Slack summary. Send a message1 – Delivers insights to a Slack channel. Code in JavaScript10 – Reconstructs final data with delivery status. Append or update row in sheet – Logs all results into Google Sheets for audit and tracking. Why use this? Turns lost deals into actionable learning instead of static CRM records Gives sales teams clear, realistic re-engagement plans without manual analysis Provides leadership with concise, decision-ready summaries Creates a historical database of loss reasons and revival outcomes Improves pipeline recovery while enforcing consistent sales intelligence

Description Connect Fireflies to this workflow once and every meeting you record is automatically categorized and logged to Google Sheets the moment transcription finishes. GPT-4o-mini reads the meeting title, keywords, overview, and action items, then assigns one of seven categories — Sales, Client, Internal, HR, Product, Finance, or Other — along with a confidence rating and a one-sentence reason. Each meeting becomes one clean row in your sheet with emoji labels for instant visual scanning. Built for founders, operations managers, and team leads who want a searchable, sortable record of how their time is actually being spent across meeting types. What This Workflow Does Triggers instantly when a meeting ends** — Fireflies fires the workflow the moment transcription completes, so no manual step is ever needed Fetches meeting details from Fireflies** — Retrieves the title, date, duration, participants, keywords, overview, and action items for each meeting Assigns one of seven categories** — GPT-4o-mini classifies every meeting as Sales, Client, Internal, HR, Product, Finance, or Other based on actual meeting content Scores confidence per classification** — Returns High, Medium, or Low confidence so you know which categorizations to trust and which to review Adds emoji labels for visual scanning** — Category and confidence values are emoji-coded before logging so your sheet is readable at a glance without filtering Logs one row per meeting to Google Sheets** — Appends a 10-column record automatically including date, title, category, confidence, reason, duration, participants, keywords, and a direct Fireflies link Setup Requirements Tools Needed n8n instance (self-hosted or cloud) Fireflies.ai account with webhook access OpenAI account with GPT-4o-mini API access Google Sheets (one sheet with a tab named Meeting Categories) Credentials Required Fireflies API key (pasted into Set Config Values) OpenAI API key Google Sheets OAuth2 Estimated Setup Time: 10–15 minutes Step-by-Step Setup Import the workflow — Open n8n → Workflows → Import from JSON → paste the workflow JSON → click Import Activate the workflow and copy the webhook URL — Toggle the workflow to Active → click on node Fireflies Webhook → copy the Production URL shown Register the webhook in Fireflies — Log in to app.fireflies.ai → Settings → Developer Settings → Webhooks → paste the webhook URL → save Get your Fireflies API key — In Fireflies, go to Settings → Integrations → Fireflies API → copy your API key Get your Google Sheet ID — Open your Google Sheet in a browser → look at the URL → copy the string between /d/ and /edit (e.g. in docs.google.com/spreadsheets/d/1ABC123xyz/edit, the ID is 1ABC123xyz) Fill in Config Values — Open node Set Config Values → replace the two placeholders: | Field | What to enter | |---|---| | YOUR_FIREFLIES_API_KEY | Your Fireflies API key from step 4 | | YOUR_GOOGLE_SHEET_ID | Your Google Sheet ID from step 5 | > ⚠️ Do NOT change the meetingId field — it is extracted automatically from the Fireflies webhook and must remain as-is. Create your Google Sheet tab — Open your Google Sheet → add a tab named exactly Meeting Categories → add these 10 column headers in row 1: Date, Meeting Title, Category, Confidence, Reason, Duration (min), Participants, Keywords, Fireflies URL, Logged At Connect OpenAI — Open node OpenAI Chat Model → click the credential dropdown → add your OpenAI API key → test the connection Connect Google Sheets — Open node Log to Google Sheets → click the credential dropdown → add Google Sheets OAuth2 → sign in with your Google account → authorize access Activate the workflow — Confirm the workflow is Active — Fireflies will now fire it automatically after every recorded meeting How It Works (Step by Step) Step 1 — Webhook: Fireflies Webhook This step listens for a signal from Fireflies. Every time a meeting finishes transcribing, Fireflies sends a POST request to this webhook URL containing the meeting ID and event type. No manual trigger is needed — it fires automatically after every recorded call where you are the organizer. Step 2 — Set: Config Values Your Fireflies API key, Google Sheet ID, sheet tab name, and the meeting ID from the webhook are stored here. The meeting ID is extracted automatically from all possible Fireflies payload formats — you never need to enter it manually. Step 3 — HTTP: Fetch Transcript from Fireflies A request is sent to the Fireflies API using your API key and the meeting ID. It retrieves the meeting title, date, duration, participants, transcript URL, keywords, overview, and action items. Only lightweight summary fields are fetched — no full sentence-by-sentence transcript — keeping the response fast and small. Step 4 — Code: Extract Meeting Data The Fireflies response is processed into clean, usable fields. Keywords are limited to the top 12, the overview is capped at 600 characters, and action items are limited to the top 5. The meeting date is formatted as a readable date string. If the transcript is not found or not yet ready, the step throws an error and the workflow stops cleanly without creating a blank row in your sheet. Step 5 — AI Agent: Categorize Meeting GPT-4o-mini receives the meeting title, keywords, overview, action items, participants, and duration. It assigns exactly one category from the seven options and returns three structured fields: the category name, a confidence level (High, Medium, or Low), and a one-sentence plain-text reason for the choice. The model runs at temperature 0.1 for highly consistent, repeatable categorization. Step 6 — OpenAI Chat Model This is the language model powering the categorization step. It uses GPT-4o-mini at temperature 0.1 and is capped at 150 tokens — only three short fields are needed per meeting, making this extremely cost-efficient at approximately $0.0002 per meeting. Step 7 — Structured Output Parser This step enforces the exact three-field schema GPT-4o-mini must return. It validates that category, confidence, and reason are all present and correctly typed before the results move forward, preventing any malformed AI output from reaching your sheet. Step 8 — Code: Prepare Sheet Row The AI output is read and emoji labels are added based on category and confidence. Category emojis: 💰 Sales, 🤝 Client, 🏢 Internal, 👥 HR, 🛠️ Product, 📊 Finance, 📋 Other. Confidence emojis: ✅ High, 🟡 Medium, ⚠️ Low. All meeting metadata from step 4 is combined with the AI output into one complete row ready for logging. Step 9 — Google Sheets: Log to Google Sheets One row is appended to your Meeting Categories tab with all 10 columns populated: date, meeting title, category with emoji, confidence with emoji, reason, duration in minutes, participants, keywords, Fireflies transcript URL, and the logged-at timestamp. The final result: every meeting ends with a new row in your Google Sheet — categorized, confidence-rated, and ready to filter or sort by meeting type. Key Features ✅ Fires automatically on every meeting — No manual input ever needed after the one-time webhook setup in Fireflies ✅ Seven category options cover all meeting types — Sales, Client, Internal, HR, Product, Finance, and Other handle virtually every business meeting scenario ✅ Confidence rating per classification — High, Medium, and Low ratings tell you exactly how certain the AI was so you can spot which rows to review manually ✅ Emoji-coded for instant visual scanning — Category and confidence columns use emojis so you can read your sheet at a glance without needing to filter ✅ Direct Fireflies link in every row — Each row includes a clickable link back to the full Fireflies transcript so you can open the original meeting in one click ✅ Ultra-low cost per meeting — GPT-4o-mini at temperature 0.1 with a 150-token cap costs approximately $0.0002 per meeting — a full year of daily meetings costs less than a dollar ✅ Structured output enforced — A schema parser validates all three AI fields before anything reaches your sheet — no broken or incomplete rows ✅ Lightweight API call — Only summary fields are fetched from Fireflies (not full sentences), keeping each API call fast and the response small Customisation Options Add your own custom categories — In node AI Agent — Categorize Meeting, edit the category list in the prompt to replace or add categories that match your business (e.g. replace "Finance" with "Investor" or add "Partnership" as an eighth option) — also update the schema in Structured Output Parser to match. Add a Slack notification for Sales meetings — After node Log to Google Sheets, add an IF check that reads the category field — if it contains "Sales", post a Slack message to your #sales-team channel with the meeting title, participants, and Fireflies link for immediate team visibility. Filter out internal meetings from the sheet — In node Prepare Sheet Row, add a condition: if the category is "Internal" and confidence is "High", set a skipLog flag to true — then add an IF check before Log to Google Sheets to bypass logging for confirmed internal meetings and keep your sheet focused on client-facing activity. Add a weekly category summary email — Add a separate Schedule trigger that fires every Monday morning, reads the Meeting Categories sheet via a Google Sheets read step, counts rows by category from the past 7 days, and sends a summary email showing how many Sales, Client, and Internal meetings happened that week. Use a different sheet per category — In node Prepare Sheet Row, add logic that maps each category to a different sheetName value — then Log to Google Sheets will route each meeting to its own dedicated tab (e.g. all Sales calls on a Sales tab, all Client calls on a Client tab). Troubleshooting Workflow not triggering when a meeting ends: Confirm the workflow is Active — inactive workflows do not receive Fireflies webhooks Log in to app.fireflies.ai → Settings → Developer Settings → Webhooks → confirm the URL is saved and matches the Production URL from node Fireflies Webhook exactly Note that Fireflies only fires the webhook for meetings where you are the organizer — guest meetings will not trigger it Fireflies API key error or transcript not found: Confirm YOUR_FIREFLIES_API_KEY in node Set Config Values is replaced with your actual key — not the placeholder text Get your key from fireflies.ai → Settings → Integrations → Fireflies API If the transcript is not found, Fireflies may still be processing the meeting — this workflow stops cleanly with an error in this case; the meeting will not be logged until you rerun it manually or wait for the next webhook OpenAI not categorizing correctly: Confirm the API key is connected in node OpenAI Chat Model and your account has available credits Check the execution log of node AI Agent — Categorize Meeting for the raw GPT response If categories are inconsistent, confirm the prompt text in AI Agent — Categorize Meeting is intact and has not been accidentally edited Google Sheets not logging rows: Confirm the Google Sheets OAuth2 credential in node Log to Google Sheets is connected and not expired — re-authorize if needed Check that YOUR_GOOGLE_SHEET_ID in node Set Config Values is the ID from the sheet URL, not the full URL Confirm the tab is named Meeting Categories exactly — capitalization must match the sheetName value in Set Config Values Verify all 10 column headers in row 1 match exactly: Date, Meeting Title, Category, Confidence, Reason, Duration (min), Participants, Keywords, Fireflies URL, Logged At AI returning a category not in the list: The Structured Output Parser enforces the schema — if an unexpected value appears, check the execution log of AI Agent — Categorize Meeting for the raw output Confirm the seven category names in the prompt match exactly what the schema parser expects: Sales, Client, Internal, HR, Product, Finance, Other Support Need help setting this up or want a custom version built for your team or agency? 📧 Email: info@isawow.com 🌐 Website: https://isawow.com/

Who it's for SaaS founders and developers who want to automate their customer onboarding experience from payment to welcome email, without any manual work. How it works A Stripe Trigger listens for successful payment events. The payment amount is converted and used to identify the subscription tier. Customer details are extracted from the Stripe payload. The AI Agent queries Pinecone to retrieve the correct plan details and generates a personalised HTML welcome email with the renewal date calculated automatically. The email is parsed and sent via Gmail. Customer details and subscription info are logged to Google Sheets for renewal tracking. Set up steps Connect your Stripe account and point it to listen for checkout.session.completed events. Store your tier information as chunks in Pinecone. Add your OpenAI credentials for the AI Agent and Embeddings nodes. Connect Gmail as your sending account. Create a Google Sheet with columns: Name, Email, Amount, Tier, Renewal Date, Status. Requirements Stripe account Pinecone account with tier knowledge base uploaded OpenAI account Gmail account Google Sheets

Title: Create daily AI news digest and send to Telegram Description: Stay ahead of the rapidly evolving artificial intelligence landscape without the information overload. This workflow acts as your personal AI news editor, automatically curating, summarizing, and visualizing the top stories of the day, delivered directly to your Telegram. It goes beyond simple RSS aggregation by using an AI Agent to rewrite headlines and summaries into a digestible format and includes a "Chat Mode" where you can ask follow-up questions about the news directly within the n8n interface. Who is it for AI Enthusiasts & Researchers:** Keep up with the latest papers and releases without manually checking multiple sites. Tech Professionals:** Get a morning briefing on industry trends to start your day informed. Content Creators:** Find trending topics for newsletters or social media posts effortlessly. How it works News Aggregation: Every morning at 8:00 AM, the workflow fetches RSS feeds from top tech sources (Google News AI, The Verge, and TechCrunch). Smart Filtering: A Code node aggregates the articles, removes duplicates, and ranks them by recency to select the top 5 stories. AI Summarization: An AI Agent (powered by OpenAI) analyzes the selected stories and writes a concise, engaging summary for each. Visual Generation: DALL-E generates a unique, futuristic header image based on the day's news context. Delivery: The digest is formatted with Markdown and emojis, then sent to your specified Telegram chat. Interactive Chat: A separate branch allows you to chat with an AI Agent via the n8n Chat interface to discuss the news or ask general AI questions. How to set up Configure Credentials: Set up your OpenAI API credential. Set up your Telegram API credential. Get Telegram Chat ID: Create a bot with @BotFather on Telegram. Send a message to your bot. Use @userinfobot to find your numeric Chat ID. Update Workflow Settings: Open the Workflow Configuration node. Paste your Chat ID into the telegramChatId value field. Activate: Toggle the workflow to "Active" to enable the daily schedule. Requirements n8n Version:** Must support LangChain nodes. OpenAI Account:** API Key with access to GPT-4o-mini (or preferred model) and DALL-E 3. Telegram Account:** To create a bot and receive messages. How to customize Change News Sources:** Edit the RSS URLs in the Workflow Configuration node to track different topics (e.g., Crypto, Finance, Sports). Adjust Personality:** Modify the system prompt in the AI News Summarizer Agent node to change the tone of the summaries (e.g., "explain it like I'm 5" or "highly technical"). Change Schedule:** Update the Daily 8 AM Trigger node to your preferred time zone and frequency.