Workflow Templates

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/

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.

🚀 How it works Handles GDPR Article 15 (access) and Article 17 (erasure) requests end-to-end — from inbound email to legally-compliant response — with zero manual intervention and a full audit trail. 📬 Monitors Gmail inbox for incoming data subject requests 🤖 AI Agent classifies the request (access or erasure), extracts the requester email and data subject email with structured JSON output 🗄️ Queries Supabase for all personal data records matching the subject 📋 Queries Airtable CRM for matching contact records 📝 Second AI Agent compiles all found data into a GDPR-compliant HTML report ✉️ Access requests — sends a full data report to the requester 🗑️ Erasure requests — deletes records from both Supabase and Airtable, then sends a deletion confirmation 🔒 Logs every request to Google Sheets with timestamp for your audit trail 🛠️ Set up steps Estimated setup time: ~20 minutes Gmail Trigger — connect Gmail OAuth2; point it at your DSR inbox OpenAI — connect OpenAI API credential (used by both AI Agent nodes) Supabase — connect Supabase API credential; update the table name from users to match your schema Airtable — connect Airtable Personal Access Token; replace YOUR_BASE_ID and YOUR_TABLE_NAME Google Sheets — connect Google Sheets OAuth2; replace YOUR_AUDIT_SHEET_ID; create a tab named DSR Audit Log Follow the sticky notes inside the workflow for per-node guidance 📋 Prerequisites Gmail account receiving GDPR requests OpenAI API key (GPT-4o) Supabase project with a users/contacts table Airtable base with a Contacts table containing an Email field Google Sheets for audit log Custom Workflow Request with Personal Dashboard kumar@smartflowcraft.com https://www.smartflowcraft.com/contact More free templates https://www.smartflowcraft.com/n8n-templates

This workflow automates the initial screening process for new job applications, freeing up your recruitment team to focus on qualified candidates. It receives applications from a webhook, uses OpenAI (GPT-4) to analyze resumes for skill and culture fit, generates interview questions, logs the results to Google Sheets, sends interview invitations via Gmail, and notifies your team on Slack. 🚀 Who is this for? HR and Recruitment Teams** looking to automate repetitive screening tasks. Hiring Managers** who want a consistent, data-driven first pass on applicants. Startups and SMBs** aiming to build an efficient, scalable hiring pipeline without a large HR team. 💡 How it works Receive Application: The workflow triggers when a new application is submitted via a webhook from your job board or application form. Extract & Analyze: It downloads the resume/CV, extracts the text, and sends it to OpenAI (GPT-4) with a custom prompt. Score & Generate: The AI scores the candidate on skill match and culture fit, provides a summary, and generates tailored interview questions based on their experience. Log Data: The evaluation scores, AI summary, and candidate information are appended to a new row in a Google Sheet for tracking. Schedule Interview: A personalized email is sent to the candidate via Gmail with a link to schedule their interview. Notify Team: A summary card with the AI evaluation and links to the full report is posted in a Slack channel to keep the hiring team informed. ⚙️ How to set up Configure Credentials: Set up your credentials for OpenAI, Google (for both Sheets and Gmail), and Slack in n8n. Webhook URL: Copy the "Production URL" from the "Webhook: New Application" node and set it as the destination in your job board's webhook settings (e.g., Greenhouse, Lever, Ashby, or a web form). Google Sheet: Create a Google Sheet to track applicants. Update the "G Sheets: Save Evaluation" node with your Spreadsheet ID and Sheet Name. Ensure the columns in your sheet match the data you want to save. Customize Prompts & Email: Modify the prompts in the two OpenAI nodes to match your company's values and the specific job requirements. Update the Gmail node with your email content and the logic for your scheduling link (e.g., Calendly, SavvyCal). 📋 Requirements An n8n instance (Cloud or self-hosted). An OpenAI API key. A Google account for Google Sheets and Gmail. A Slack workspace. A job application source capable of sending webhooks.

Description Automatically triage Product UAT feedback using AI, route it to the right tools and teams, and close the feedback loop with testers, all in one workflow. This workflow analyzes raw UAT feedback, classifies it (critical bug, feature request, UX improvement, or noise), validates AI confidence, escalates when human review is needed, and synchronizes everything across Jira, Slack, Notion, Google Sheets, and email. Context Product teams often receive unstructured UAT feedback from multiple sources (forms, Slack, internal tools), making triage slow, inconsistent, and error-prone. This workflow ensures: Faster bug detection Consistent categorization Zero feedback lost Clear accountability between Product, Engineering, and Design It combines AI automation with human-in-the-loop control, making it safe for real production environments. Who is this for? Product Managers running UAT or beta programs Project Managers coordinating QA and release validation Product Ops / PMO teams Engineering teams who want faster, cleaner bug escalation Any team managing high-volume UAT feedback Perfect for teams that want speed without sacrificing control. Requirements Webhook trigger (form, internal tool, Slack integration, etc.) OpenAI account (for AI triage) Jira (bug tracking) Slack (team notifications) Notion (product roadmap / UX backlog) Google Sheets (UAT feedback log) Gmail (tester & manual review notifications) How it works Trigger The workflow starts when UAT feedback is submitted via a webhook (form, Slack, or internal tool). Normalize & Clean Incoming data is normalized into a consistent structure (tester, build, page, message) and cleaned to be AI-ready. AI Triage An AI model analyzes the feedback and returns: Type (Critical Bug, Feature Request, UX Improvement, Noise) Severity & sentiment Summary and suggested title Confidence score Quality Control If the AI output is unreliable (low confidence or parsing error), the feedback is automatically routed to manual review via email and Slack. Routing & Actions If confidence is sufficient: Critical Bugs → Jira issue + Engineering Slack alert Feature Requests → Notion roadmap UX Improvements → Design / UX tracking Noise → Archived but traceable Closed Loop The tester is notified via Slack or email, and the workflow responds to the original webhook with a structured status payload. What you get One unified UAT triage system Faster bug escalation Clean product and UX backlogs Full traceability of every feedback Automatic tester communication Safe AI usage with human fallback About me : I’m Yassin a Product Manager Scaling tech products with a data-driven mindset. 📬 Feel free to connect with me on Linkedin

How It Works This workflow automates continuous data integrity monitoring and intelligent alert management across multiple data sources. Designed for data engineers, IT operations teams, and business intelligence analysts, it solves the critical challenge of detecting data anomalies and orchestrating appropriate responses based on severity levels. The system operates on scheduled intervals, fetching data from software metrics APIs and BI dashboards, then merging these sources for comprehensive analysis. It employs AI-powered validation and orchestration agents to detect anomalies, assess severity, and determine optimal response strategies. The workflow intelligently routes alerts based on severity classification, triggering critical notifications via email and Slack for high-priority issues while sending standard reports for routine findings. By maintaining detailed compliance audit logs and preparing executive summaries, it ensures stakeholders receive timely, actionable intelligence while creating audit trails for data quality monitoring initiatives. Setup Steps Configure Schedule Data Integrity Check trigger with monitoring frequency Connect Workflow Configuration node with data source parameters Set up Fetch Software Metrics and Fetch BI Dashboard Data nodes with respective API credentials Configure Merge Data Sources node for data consolidation logic Connect Data Validation Agent with OpenAI/Nvidia API credentials for anomaly detection Set up Orchestration Agent with AI API credentials for severity assessment Configure Check for Anomalies node with routing conditions Connect Route by Severity node with classification logic Prerequisites OpenAI or Nvidia API credentials for AI-powered analysis, API access to software metrics platforms Use Cases SaaS platforms monitoring service health metrics, e-commerce businesses tracking inventory data quality Customization Adjust scheduling frequency for monitoring intervals, modify severity thresholds for alert classification Benefits Reduces mean time to detection by 75%, eliminates manual data quality checks

Monitor Clutch categories for new agencies to Slack With BrowserAct and Gemini Introduction This workflow automates the discovery of new B2B service providers entering the market. It scrapes a specific category on Clutch.co weekly, standardizes the data using AI, and compares it against a historical database to identify only the fresh "new entrants." These leads are then sent to Slack as a "Hot Alert." Target Audience Sales Development Representatives (SDRs), partnership managers, and lead generation agencies looking for new agencies or service providers before their competitors find them. How it works Scheduling: A Weekly Trigger initiates the scan to ensure regular monitoring of the market. Targeting: A Set node defines the specific Clutch category URL to monitor (e.g., https://clutch.co/developers). Data Extraction: The BrowserAct node runs the "The New Entrant Asset Finder" template. It navigates to the target category and scrapes the current list of companies. Data Cleaning: An AI Agent (using OpenRouter/Gemini) processes the raw scraped data. It fixes formatting issues, such as converting "$10,000+" to integers and splitting "City, Country" strings into separate fields. Staging: The cleaned data is written to a temporary "Second Extraction" sheet in Google Sheets. Change Detection: The workflow retrieves the previous week's data ("Database") and the current week's data. A second AI Agent compares the two lists to identify companies that exist in the new scan but not the old one. Notification: If new companies are found, a Slack node posts a formatted alert with details like "Company Name," "Rate," and "Website." Database Update: The workflow clears the old database and replaces it with the latest scan, establishing a new baseline for the next week's comparison. How to set up Configure Credentials: Connect your BrowserAct, OpenRouter, Google Sheets, and Slack accounts in n8n. Prepare BrowserAct: Ensure the The New Entrant Asset Finder template is active in your BrowserAct library. Prepare Google Sheet: Create a Google Sheet with two tabs: Database (First Extarction) Second Extraction Define Target: Open the Clutch Category Link node and paste the URL of the Clutch category you want to track. Configure IDs: Update the Google Sheets nodes to point to your specific spreadsheet file and the respective tabs mentioned above. Google Sheet Headers To use this workflow, ensure your Google Sheet tabs use the following headers: company_name website_url min_project_value_usd hourly_rate_low hourly_rate_high employees_range city country short_description Requirements BrowserAct Account:* Required for scraping. Template: *The New Entrant Asset Finder**. OpenRouter Account:** Required for cleaning data and detecting changes. Google Sheets:** Acts as the historical database. Slack Account:** Used for receiving lead alerts. How to customize the workflow Change the Source: Modify the Clutch Category Link and the BrowserAct template to scrape a different directory, such as G2, Capterra, or Upwork. Filter Logic: Update the system prompt in the Detect data changes AI node to only alert on companies with a specific hourly rate (e.g., >$100/hr) or employee count. Enrichment: Add a Clearbit or Apollo node after the change detection step to find email addresses for the new companies before sending them to Slack. Need Help? How to Find Your BrowserAct API Key & Workflow ID How to Connect n8n to BrowserAct How to Use & Customize BrowserAct Templates Workflow Guidance and Showcase Video AI-Powered Lead Finder: Target New & Growing Companies (n8n + AI Tutorial)

Zoho CRM – Deal Health Predictor with AI Scoring This n8n automation monitors open Zoho CRM Deals every week, identifies stalled opportunities, scores their health using Google Gemini AI and triggers sales intervention by emailing the deal owner and creating a high-priority task in Zoho CRM — before the deal goes cold. Quick Start — Implementation in 6 Steps Import workflow into your n8n instance. Connect Zoho OAuth2 credential in all Zoho nodes. Connect Gmail OAuth2 account for outbound alerts. Confirm stage names & inactivity thresholds match your CRM. Test with sample deals before scheduling. Activate the workflow once validated by your sales team. What It Does This workflow dynamically analyzes every open sales deal retrieved from Zoho CRM. It calculates key metrics per deal such as inactivity duration, stage aging and deal age to understand whether the opportunity has stalled. Only deals with significant inactivity move forward to AI scoring. Using Google Gemini, each deal receives a Health Score (0–100), along with a risk level, reasons and recommended next actions. If a deal is considered “At-Risk,” the system automatically: Sends an alert email to the assigned sales rep Creates a high-priority Task in Zoho CRM linked to that deal It ensures timely sales intervention without needing manual checks. Who’s It For Sales teams using Zoho CRM RevOps & Sales Managers monitoring deal movement Teams wanting data-backed alerts for slow-moving deals Businesses wanting proactive pipeline management with AI Requirements | Requirement | Why | |------------|-----| | n8n instance (Self-hosted or Cloud) | Runs the workflow | | Zoho CRM OAuth2 Credentials | Fetch deals + Create tasks | | Gmail (or SMTP) credentials | Send alert emails | | Google Gemini API access | AI scoring on deals | | Deals must have Stage + Owner + Activity history | Ensures accurate scoring | How It Works — Setup & Configuration Steps Step-by-Step Setup Import workflow into n8n Enable Zoho CRM OAuth2 credentials in: Fetch Open Deals Create At-Risk Task Enable Gmail OAuth2 on the email alert node Validate fields from Zoho API: Last_Activity_Time Stage Owner.email Update the deal stage list in the Fetch URL if needed: Example: Qualification, Negotiation, Proposal, etc. Confirm scanning frequency in the Weekly Trigger Run the workflow manually once → check logs + emails + tasks Turn workflow Active How To Customize Nodes | Node | What You Can Customize | Example | |------|-----------------------|---------| | Weekly Trigger | Change execution frequency | Daily scan | | Fetch Deals | Include additional deal stages | Add “Value Proposition” | | Filter Stalled Deals | Adjust inactivity threshold | > 15 days instead of 30 | | AI Prompt | Add more data points | Probability to close | | Email Alert | Modify message template | Localization | | Task creation | Add reminder / follow-up info | Due date +1 day | Add-Ons (Optional Enhancements) You can easily extend this workflow by adding: Stage Change Webhook Trigger** → near real-time monitoring Google Sheets or Database Logging** for reporting Duplicate task checker** so the same deal isn’t flagged repeatedly Slack / Microsoft Teams alerts** SLA-based scoring** (pipeline aging logic) Manager escalation** if no response in X days Practical Use Cases This workflow is ideal for: Sales manager auto-alert system for aging deals Leaderboard tracking for untouched deals Weekly CRM hygiene and rep performance tracking Early detection of churn risk in B2B sales cycles AI-assisted deal coaching and next-step suggestions Many more scenarios are possible based on deal movement automation. Troubleshooting Guide | Issue | Possible Cause | Fix / Solution | |------|----------------|----------------| | No deals processed | Stage filters too narrow | Update fetch URL stage list | | Emails not sent | Gmail credentials missing or expired | Reconnect Gmail OAuth2 | | Tasks not created | Zoho API permissions missing | Enable write permissions | | Owner email missing | CRM field not mapped correctly | Update sendTo expression | | Health score always null | Output parser mismatch | Check prompt & parser config | | Workflow runs but no alerts sent | No stalled deals or score >= threshold | Temporarily lower threshold for testing | Need Help? If you would like expert help customizing this workflow or implementing additional automation like stage-based triggers, dashboards or integration with Slack/Teams, our n8n automation team at WeblineIndia can assist you.