Workflow Templates

Feedback Sentiment Workflow (Typeform → GCP → Notion/Slack/Trello) This template ingests feedback from Typeform, runs Google Cloud Natural Language sentiment analysis, routes based on sentiment, and then creates a Notion database page and posts a Slack notification for positive items, or creates a Trello card for negative items. The flow is designed for quick setup and safe sharing using placeholders for IDs and credentials. How it Works Typeform Trigger Captures each new submission and exposes answers like Name and the long-text Feedback field. Google Cloud Natural Language Analyzes the feedback text and returns a sentiment score in: documentSentiment.score Check Sentiment Score (IF) True branch: Score > 0 → Positive False branch: Score ≤ 0 → Non-positive Add Feedback to Notion (True branch) Creates a new page in a Notion database with mapped properties. Notify Slack (after Notion) Posts the feedback, author, and score to a Slack channel for visibility. Create Trello Card (False branch) Logs non-positive items to a Trello list for follow-up. Required Accounts Google Cloud Natural Language API** enabled (OAuth2 or service credentials). Notion integration** with database access to create pages. Slack app/bot token** with permission to post to the target channel. Typeform account** with a form including: Long Text feedback question Name field Notion Database Columns Name (title):** Person name or responder label Feedback (rich_text):** Full feedback text Sentiment Score (number):** Numeric score from GCP ∈ [-1, 1] Source (select/text):** "Typeform" for provenance Submitted At (date):** Timestamp from the trigger Customization Options Sentiment Threshold:** Adjust IF condition (e.g., ≥ 0.25) for stricter positivity. Slack Routing:** Change channel, add blocks/attachments for richer summaries. Trello Path:** Point to a triage list and include labels for priority. Field Mapping:** Update the expression for feedback question to match Typeform label. Database Schema:** Add tags, product area, or customer tier for reporting. Setup Steps Connect credentials: Typeform, GCP Natural Language, Notion, Slack, Trello. Replace placeholders in workflow JSON: Form ID Database ID Slack Channel Trello List ID Map fields: Set Feedback + Name expressions from Typeform Trigger output into Notion and Slack. Adjust IF threshold for your definition of "positive". Test with a sample response and confirm: Notion page creation Slack notification Trello card logging

⭐ Google Review Sentiment Analysis & Slack Notification Workflow This workflow automates the process of collecting Google Business Profile reviews 🏪, analyzing customer sentiment with Google Gemini 🤖✨, and sending structured reports to Slack 💬. 🔑 Key Advantages 📥 Fetches Google Business Profile reviews for a given business and time period 🧠 Runs sentiment analysis using Gemini AI 📊 Consolidates comments, ratings, and trends into a JSON-based summary 🧩 Restructures results into Slack Block Kit format for easy readability 🚀 Sends automated sentiment reports directly to a Slack channel ⚙️ Set Up Essentials You’ll Need 🔑 Google Business Profile API access with project approval ✅ Enabled Google Business Profile API service 🔐 Gemini API credentials 💬 Slack workspace & channel for receiving reports 🚀 How to Get Started 🔧 Configure your Google Business Profile API and enable access 👤 Set the owner name and 📍 location to fetch reviews ⏳ Define the review time period using the Set Time Period node 🔗 Connect your Slack account and select a channel for notifications 🕒 Deploy and let the workflow run on schedule for automated insights

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This n8n template from Intuz provides a complete solution to automate the syncing of new subscribers from Google Sheets to MailerLite. It intelligently identifies and adds only new contacts, preventing duplicates and ensuring your email lists are clean and accurate. Who's this workflow for? Marketing Teams Email Marketers Small Business Owners Community Managers How it works 1. Read from Google Sheets: The workflow begins by reading all contact rows from your designated Google Sheet. 2. Check for Existing Subscribers: For each contact, it performs a search in MailerLite to check if a subscriber with that email address already exists. 3. Handle Duplicates: If the subscriber is found in MailerLite, the workflow stops processing that specific contact, preventing any duplicates from being created. 4. Create New Subscribers: If the contact is not found, the workflow proceeds to create a new subscriber in MailerLite, using all the details from the Google Sheet (like name, company, and country) and assigns them to the specified group. Setup Instructions 1. Google Sheets Setup: Connect your Google Sheets account to n8n. Create a sheet with the required columns: Email, first_name, last_name, Company, Country, and group_id. In the Get row(s) in sheet node, select your credentials and specify the Document ID and Sheet Name. 2. MailerLite Setup: Connect your MailerLite account to n8n using your API key. In both the Get a subscriber and Create subscriber... nodes, select your MailerLite credentials. Make sure the group_id values in your Google Sheet correspond to valid Group IDs in your MailerLite account. 3. Activate Workflow: Save the workflow and click "Execute workflow" to run the sync whenever you need to update your subscriber list. Connect with us Website: https://www.intuz.com/services Email: getstarted@intuz.com LinkedIn: https://www.linkedin.com/company/intuz Get Started: https://n8n.partnerlinks.io/intuz For Custom Worflow Automation Click here- Get Started

1. Data Ingestion Workflow (Left Panel – Pink Section) This part collects data from the ServiceNow Knowledge Article table, processes it into embeddings, and stores it in Qdrant. Steps: Trigger: When clicking ‘Execute workflow’ The workflow starts manually when you click Execute workflow in n8n. Get Many Table Records Fetches multiple records from the ServiceNow Knowledge Article table. Each record typically contains knowledge article content that needs to be indexed. Default Data Loader Takes the fetched data and structures it into a format suitable for text splitting and embedding generation. Recursive Character Text Splitter Splits large text (e.g., long knowledge articles) into smaller, manageable chunks for embeddings. This step ensures that each text chunk can be properly processed by the embedding model. Embeddings OpenAI Uses OpenAI’s Embeddings API to convert each text chunk into a high-dimensional vector representation. These embeddings are essential for semantic search in the vector database. Qdrant Vector Store Stores the generated embeddings along with metadata (e.g., article ID, title) in the Qdrant vector database. This database will later be used for similarity searches during chatbot interactions. 2. RAG Chatbot Workflow (Right Panel – Green Section) This section powers the Retrieval-Augmented Generation (RAG) chatbot that retrieves relevant information from Qdrant and responds intelligently. Steps: Trigger: When chat message received Starts when a user sends a chat message to the system. AI Agent Acts as the orchestrator, combining memory, tools, and LLM reasoning. Connects to the OpenAI Chat Model and Qdrant Vector Store. OpenAI Chat Model Processes user messages and generates responses, enriched with context retrieved from Qdrant. Simple Memory Stores conversational history or context to ensure continuity in multi-turn conversations. Qdrant Vector Store1 Performs a similarity search on stored embeddings using the user’s query. Retrieves the most relevant knowledge article chunks for the chatbot. Embeddings OpenAI Converts user query into embeddings for vector search in Qdrant.

Workflow Summary This automation keeps your Supabase vector database synchronized with documents stored in Google Drive, while also making the data contextual and vector based for better retrieval. When a file is added or modified, the workflow extracts its text, splits it into smaller chunks, and enriches each chunk with contextual metadata (such as summaries and document details). It then generates embeddings using OpenAI and stores both the vector data and metadata in Supabase. If a file changes, the old records are replaced with updated, contextualized content. The result is a continuously updated and context-aware vector database, enabling highly accurate hybrid search and retrieval. To setup 1. Connect Google Drive • Create a Google Drive folder to watch. • Connect your Google Drive account in n8n and authorize access. • Point the Google Drive Trigger node to this folder (new/modified files trigger the flow). 2. Configure Supabase • Please refer to the Setting Up Supabase Sticky Note. 3. Connect OpenAI (or your embedding model) • Add your OpenAI API key in n8n credentials.

This workflow allows you to create, update and get a post using the Discourse node. Discourse node: This node creates a new post under a category. Based on your use-case, you can select a different category. Discourse1 node: This node updates the content of the post. Discourse2 node: This node fetches the node that we created using the Discourse node. Based on your use-case, you can add or remove nodes to connect Discourse to different services.

✅ What problem does this workflow solve? Salon staff often spend hours juggling appointment calls, managing bookings manually, and keeping track of customer preferences. This workflow automates your entire salon appointment system via WhatsApp, delivering a personalized and human-like booking experience using AI, memory, and Google Sheets. 💡 Main Use Cases 💁‍♀️ Offer personalized stylist recommendations by remembering customer preferences and past visits. 📅 Provide real-time availability and salon opening hour information. 📝 Book and update appointments directly from customer chat. 🔁 Simplify appointment changes by recalling previous booking details. 🧠 Enable context-aware, memory-driven conversations across multiple interactions. 🧠 How It Works – Step-by-Step 1. 📲 Chat Message Trigger The workflow is triggered whenever a customer sends a message to your WhatsApp salon assistant. 2. 🧠 Memory Buffer for Context Management The assistant uses a Memory Buffer to: Recognize returning customers Avoid repeating questions Maintain conversation flow across multiple sessions This enables a seamless and intelligent dialogue with each customer. 3. 💇 Stylist & Service Lookup When the customer asks for stylist suggestions, available time slots, or services: Extracts request details using AI Queries a Google Sheet containing: Stylist availability Service types Salon opening hours Returns personalized recommendations based on preferences and availability 4. ✅ Appointment Booking Collects all necessary info: Date, time, selected service, stylist, contact info Stores the appointment in Google Sheets Sends a confirmation message to the customer in WhatsApp 5. 🔄 Modify or Cancel Bookings Customers can update or cancel appointments Bot matches records by phone number Modifies or deletes the appointment in the sheet accordingly 🧩 Integrations Used WhatsApp Integration** (via Twilio, Meta API, or other connector) OpenAI/GPT Model** for natural conversation flow and extraction Google Sheets** as a simple and effective appointment database Memory Buffer** for ongoing context across chats 👤 Who can use this? Perfect for: 💇‍♀️ Salons and barbershops 💅 Spas and beauty centers 🧖‍♀️ Wellness studios 🛠 Developers building vertical AI assistants for SMBs If you're looking to modernize your booking process and impress customers with an AI-powered, memory-enabled WhatsApp bot—this workflow delivers. 🚀 Benefits ⏰ Save time for your staff 🧠 Offer truly personalized experiences 📲 Book appointments 24/7 via WhatsApp 📋 Keep all records organized in Google Sheets 🧘 Reduce human error and double bookings 📦 Ready to Launch? Just configure: ✅ Your WhatsApp number + webhook integration ✅ Google Sheet with stylist and service data ✅ OpenAI key for AI-powered conversation ✅ Memory Buffer to enable smart replies And your salon will be ready to offer automated, intelligent booking—right from a simple WhatsApp chat.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. This n8n workflow template helps you automatically convert unstructured contact information—such as customer details copied from emails, web forms, or chat messages—into clean, structured JSON using an AI agent. What It Does: Accepts unstructured contact data via a Webhook (as form-data under the key prompt) Uses AI to intelligently extract key fields such as: Company Name First Name Last Name Address City Country Phone Fax Email Parses and formats the extracted data into a valid JSON object Prepares the output for seamless integration into systems like: Dolibarr Other ERP/CRM platforms Any service that consumes JSON via API or webhook Use Cases: Automate manual data entry from emails into your ERP system Clean and normalize contact data from various input sources Reduce human error in your customer onboarding workflows This template saves you time and ensures consistency across your business systems. Simply connect your systems and let the automation handle the rest.

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Who’s it for Teams and project managers who want to turn meeting transcripts into actionable Trello tasks automatically, without worrying about duplicate cards. What it does This workflow receives a transcript file in .txt format and processes it with AI to extract clear, concise tasks. Each task includes a short title, a description, an assignee (if mentioned), and a deadline (if available). The workflow then checks Trello for duplicates across all lists, comparing both card titles (name) and descriptions (desc). If a matching card already exists, the workflow returns the existing Trello card ID. If not, it creates a new card in the predefined default list. Finally, the workflow generates a user-friendly summary: how many tasks were found, how many already existed, how many new cards were created, and how many tasks had no assignee or deadline. Requirements A Trello account with API credentials configured in n8n (no hardcoded keys). An OpenAI (or compatible) LLM account connected in n8n. How to customize Adjust similarity thresholds for title/description matching in the Trello Sub-Agent. Modify the summary text to always return in your preferred language. Extend the Trello card creation step with labels, members, or due dates.

Automated DA PA Checker Workflow for SEO Analysis Description This n8n workflow collects a website URL via form submission, retrieves SEO metrics like Domain Authority (DA) and Page Authority (PA) using the Moz DA PA Checker API, and stores the results in Google Sheets for easy tracking and analysis. Node-by-Node Explanation On form submission – Captures the website input from the user to pass to the Moz DA PA Checker API. DA PA API Request – Sends the website to the Moz DA PA Checker API via RapidAPI to fetch DA, PA, spam score, DR, and organic traffic. If – Checks if the API request to the Moz DA PA Checker API returned a successful response. Clean Output – Extracts only the useful data from the Moz DA PA Checker API response for saving. Google Sheets – Appends the cleaned SEO metrics to a Google Sheet for record-keeping. Use Cases SEO Analysis** – Quickly evaluate a website’s DA/PA metrics for optimization strategies. Competitor Research** – Compare domain authority and organic traffic with competitors. Link Building** – Identify high-authority domains for guest posting and backlinks. Domain Purchase Decisions** – Check metrics before buying expired or auctioned domains. Benefits Automated Workflow** – From input to Google Sheets without manual intervention. Accurate Metrics* – Uses the trusted *Moz DA PA Checker API** for DA, PA, spam score, DR, and traffic. Instant Insights** – Get SEO scores in seconds for faster decision-making. Easy Integration** – Seamless connection between RapidAPI and Google Sheets for data storage.