Workflow Templates

Stop switching between Slack and your analytics dashboards. Mention the bot in any Slack channel, ask a business question, and get an AI-powered answer from Databox in seconds - without leaving Slack. Who's it for Marketing and growth teams** who want instant data answers during standups Managers** who need quick metric checks without logging into dashboards Anyone using Slack** who wants to query Databox data conversationally How it works @mention the bot in Slack with a business question Slack Trigger captures the message and passes it to the AI Agent AI Agent queries Databox via MCP in real time Answer is posted back to the same Slack channel Requirements Databox account** (free plan works) OpenAI API key (or Anthropic) Slack account with a custom Slack app (setup guide in the sticky notes) How to set up Setup takes around 15 minutes. The main step is creating a custom Slack app: Go to api.slack.com/apps - create an app - add app_mentions:read and chat:write scopes Copy the Bot Token (xoxb-) and Signing Secret - add as a Slack API credential in n8n Click Databox MCP Tool - set Authentication to OAuth2 and authorize Add your OpenAI API key to the Chat Model node Activate - paste the webhook URL into Slack Event Subscriptions Invite the bot to your channel with /invite @BotName

⚙️ Sales Assistant Build: Automate Prospect Research and Personalized Outreach for Sales Calls 🚀 Steps to Connect: Google Sheets Setup Connect your Google account via OAuth2 in the "Review Calls", "Product List", "Testimonials Tool", "Update Sheet", and "Update Sheets 2" nodes. Duplicate the mock Google Sheet (ID: 1u3WMJwYGwZewW1IztY8dfbEf5yBQxVh8oH7LQp4rAk4) to your drive and update the documentId in all Google Sheets nodes to match your copy's ID. Ensure the sheet has tabs for "Meeting Data", "Products", and "Success Stories" populated with your data. Setup time: ~5 minutes. OpenAI API Key Go to OpenAI and generate your API key. Paste this key into the credentials for both "OpenAI Chat Model" and "OpenAI Chat Model1" nodes. Setup time: ~2 minutes. Tavily API Key Sign up at Tavily and get your API key. In the "Tavily" node, replace the placeholder api_key in the JSON body with your key (e.g., "api_key": "your-tavily-key-here"). Setup time: ~3 minutes. How it Works • Triggers on a new sales call booking (manual for testing). • Pulls prospect details from Google Sheets and researches their company, tech stack, and updates using Tavily. • Matches relevant products/solutions from your product list and updates the sheet. • Generates personalized email confirmation (subject + body) and SMS using testimonials for relevance. • Updates the sheet with the outreach content for easy follow-up. Setup takes ~10-15 minutes total. All nodes are pre-configured—edit only the fields above. Detailed notes (e.g., prompt tweaks) are in sticky notes within the workflow.

🧠 How it works This workflow lets users generate structured summaries from YouTube videos directly inside Slack using n8n, AssemblyAI, and OpenAI. When a user submits a YouTube link via a Slack slash command, the workflow extracts the video ID and validates the video duration. Videos longer than the supported limit are stopped early with a clear message sent back to Slack. For valid videos, the workflow downloads the video audio as an MP3 file and sends it to AssemblyAI for transcription. Once the transcription is complete, the transcript is passed to an AI model to generate a structured summary. The final result includes a concise TL;DR, key takeaways, and notable quotes, which are formatted and posted back to Slack asynchronously using the original response URL. ⚙️ Features • Triggers from a Slack slash command with a YouTube link • Validates video length before processing (maximum 10 minutes) • Downloads YouTube audio as MP3 using RapidAPI • Transcribes audio using AssemblyAI • Generates structured summaries (TL;DR, key takeaways, notable quotes) • Posts the summarized result back to Slack asynchronously 💡 Use cases & expected outcomes • Educational YouTube videos → Receive a clear summary instead of watching the full video • Long-form talks or interviews → Quickly get key points and memorable quotes • Research and learning → Extract insights from videos without manual note-taking • Content discovery → Decide whether a video is worth watching based on its summary In all cases, users receive a clear, structured summary of a YouTube video directly in Slack. 💡 Perfect for • Teams sharing YouTube links and wanting quick context • Researchers and learners reviewing long video content • Content creators analyzing videos efficiently 🧩 Notes • Please note that this workflow generates summaries, not full transcripts

This workflow implements a multi-model AI orchestration with the BEST models at now (ChatGPT 5.2, Claude Opus 4.6, Gemini 3 Pro) and response aggregation system designed to handle user chat inputs intelligently and reliably. Key Advantages 1. ✅ Higher Answer Quality By combining multiple top-tier AI models, the workflow reduces blind spots and single-model bias, resulting in more accurate and nuanced answers. 2.✅ Built-in Reliability and Redundancy If one model underperforms or misunderstands the query, the others compensate, improving robustness and consistency. 3. ✅ Intelligent Query Handling The search classification and optimization layer ensures that: research queries are handled with precision, casual conversation is not over-processed, model resources are used efficiently. 4. ✅ Balanced and Transparent Reasoning Contradictions between models are not hidden. Instead, they are reconciled or clearly explained, increasing trust in the final output. 5. ✅ Scalability and Extensibility The architecture makes it easy to: add new models, swap providers, experiment with different aggregation strategies, without redesigning the entire workflow. 6. ✅ Enterprise-Ready Design This approach is well suited for: research assistants, decision-support tools, knowledge management systems, high-stakes professional use cases where answer quality matters more than speed alone. How it Works Input Processing: When a chat message is received, it's sent to a "Search Query Optimizer" that determines whether the input is a research query or general conversation. If it's a search query, it's optimized for better search results. Multi-Model Query Execution: If the input is classified as a research query, the workflow simultaneously sends the optimized query to three different AI models: ChatGPT 5.2 (OpenAI) Claude Opus 4.6 (Anthropic) Gemini 3 Pro (Google) Response Aggregation: Each model's response is collected separately, then all three responses are sent to a "Multi-Response Aggregator" which synthesizes them into a single comprehensive answer. Fallback Handling: If the input is not a research query, the workflow bypasses the multi-model execution and sends a default message asking the user to enter a research text. Set up Steps Model Configuration: Ensure you have valid API credentials set up for: OpenAI (for ChatGPT 5.2) Anthropic (for Claude Opus 4.6) Google Gemini (for both query optimization and Gemini 3 Pro) Connection Verification: Confirm all node connections are properly established in the workflow editor, particularly: Chat trigger to Search Query Optimizer Conditional branch routing based on query classification Parallel connections to the three AI models Response collection to the aggregator Prompt Customization: Review and adjust the system prompts in: Search Query Optimizer (for query classification rules) Multi-Response Aggregator (for synthesis guidelines) Each model's chain nodes (if specific formatting is required) Testing: Activate the workflow and test with various inputs to verify: Proper classification of research vs. non-research queries Simultaneous execution of all three AI models Correct aggregation of responses Appropriate fallback message for non-research inputs 👉 Subscribe to my new YouTube channel. Here I’ll share videos and Shorts with practical tutorials and FREE templates for n8n. Need help customizing? Contact me for consulting and support or add me on Linkedin.

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This workflow automates customer support across multiple channels (Email, Live Chat, WhatsApp, Slack, Discord) using AI-powered responses enhanced with Retrieval Augmented Generation (RAG) and your product documentation. It intelligently handles incoming queries, provides instant and context-aware answers, and escalates complex or negative-sentiment cases to your human support team. All interactions are logged and categorized for easy tracking and reporting. Key Features Omnichannel Support:** Handles customer queries from Email, Live Chat, WhatsApp, Slack, and Discord. AI-Powered Answers:** Uses RAG to generate accurate, context-aware responses based on your product documentation. Automatic Escalation:** Detects low-confidence or negative-sentiment cases and escalates them to your human support team. Conversation Logging:** Automatically logs and categorizes all conversations for future analysis. Weekly Reporting:** Sends automated weekly summaries and metrics to your support team. How It Works Trigger: The workflow starts when a new message is received on any supported channel. Normalization: Incoming messages are normalized into a common format for unified processing. Context Management: Conversation history is fetched and merged with the new query for better AI context. AI Response: The workflow uses RAG to generate a response, referencing your product documentation. Confidence & Sentiment Analysis: The response is scored for confidence and sentiment. Escalation Logic: If the response is low-confidence or negative, the workflow escalates the case to your support team and creates a ticket. Response Delivery: The answer (or escalation notice) is sent back to the customer on the original channel. Logging & Reporting: All interactions are logged, categorized, and included in weekly reports. Configuration Connect Your Channels: Set up triggers for each channel you want to support (Email, Webhook, WhatsApp, Slack, Discord). Add Your Documentation: Integrate your product documentation source (e.g., Google Docs, Notion, or a knowledge base) for the RAG model. Configure AI Model: Set your preferred AI provider and model (e.g., OpenAI, Azure OpenAI, etc.). Set Escalation Rules: Adjust confidence thresholds and escalation logic to fit your support workflow. Integrate Support Tools: Connect your ticketing system (e.g., Zendesk) and reporting tools (e.g., Google Sheets, Slack). Test the Workflow: Send test queries from each channel to ensure correct routing, AI responses, and escalation. Use Cases Provide instant, accurate answers to customer questions 24/7. Reduce manual workload for your support team by automating common queries. Ensure complex or sensitive cases are quickly escalated to human agents. Gain insights into support trends with automated logging and weekly reports. Requirements n8n version 2.0.2 or later Accounts and credentials for your chosen channels and AI provider Access to your product documentation in a supported format Notes Please review and customize the workflow to fit your company’s privacy and data handling policies. For best results, keep your product documentation up to date and well-structured.

AI-Powered Fake Review Detection Workflow Using n8n & Airtable This workflow automates the detection of potentially fake or manipulated product reviews using n8n, Airtable, OpenAI and Slack. It fetches reviews for a given product, standardizes the data, generates a unique hash to avoid duplicates, analyzes each review using an AI model, updates the record in Airtable and alerts the moderation team if the review appears suspicious. Quick Implementation Steps Add Airtable, OpenAI and Slack credentials to n8n. Create an Airtable Base with a reviews table. Connect the Webhook URL to your scraper or send sample JSON via Postman. Test the workflow by passing product and review URLs. Activate the workflow for continuous automated review screening. What It Does This workflow provides an automated pipeline to analyze product reviews and determine whether they may be fake or manipulated. It begins with a webhook that accepts product information and a scraper API URL. Using this information, the workflow fetches associated reviews. Each review is then expanded into separate items and normalized to maintain a consistent structure. The workflow generates a hash for deduplication, preventing multiple entries of the same review. New reviews are stored in Airtable and subsequently analyzed by OpenAI. The resulting risk score, explanation and classification are saved back into Airtable. If a review's score exceeds a predefined threshold, a structured Slack alert is sent to the moderation team. This ensures that high-risk reviews are escalated promptly while low-risk reviews are simply stored for recordkeeping. Who’s It For eCommerce marketplaces monitoring review integrity Sellers seeking automated fraud detection for product reviews SaaS platforms that accept user-generated reviews Trust & Safety and compliance teams Developers looking for an automated review-quality pipeline Requirements n8n (Cloud or Self-Hosted) Airtable Personal Access Token OpenAI API Key Slack Bot Token or Webhook Review Scraper API Basic understanding of Airtable field setup How It Works & How To Set Up 1. Receive Product Data The workflow starts with the Webhook – Receive Product Payload, which accepts a list of products and their scraper URLs. 2. Extract and Process Each Product Extract products separates the list into individual items. Process Each Product ensures that each product’s reviews are processed one at a time. 3. Fetch and Validate Reviews Fetch Product Reviews calls the scraper API. IF – Has Reviews? determines whether any reviews were returned. 4. Expand and Normalize Reviews Expand reviews[] to items splits reviews into individual items. Prepare Review Fields ensures consistent review structure. 5. Generate Review Hash Generate Review Hash1 produces a deterministic hash based on review text, reviewer ID, and date. 6. Airtable Deduplication Check Search Records by Hash checks whether the review already exists. Normalize Airtable Result cleans Airtable’s inconsistent empty output. Is New Review? decides if the review should be inserted or skipped. 7. Store New Reviews Create Review Record inserts new reviews into Airtable. 8. AI-Based Fake Review Analysis AI Fake Review Analysis sends relevant review fields to OpenAI. Parse AI Response ensures the output is valid JSON. 9. Update Airtable With AI Results Update Review Record stores the AI’s score, classification, and reasoning. 10. Moderation Alert Check Suspicious Score Threshold evaluates if the fake score exceeds a defined limit. If so, Send Moderation Alert posts a detailed message to Slack. How To Customize Nodes Fake Score Threshold Modify threshold in Check Suspicious Score Threshold. Slack Message Format Adjust text fields in Send Moderation Alert. AI Prompt Instructions Edit the instructions inside AI Fake Review Analysis. Airtable Fields Update mappings in both Create Review Record and Update Review Record. Additional Checks Insert enrichment steps before AI analysis, such as: reviewer profile metadata geolocation or reverse IP checks keyword density analysis Add-ons Notion integration for long-term review case tracking Jira or Trello integration for incident management Automated sentiment tagging Weekly review-risk summary reports Google Sheets backup for archived reviews Reviewer behavior modeling (number of reviews, frequency, patterns) Use Case Examples Detecting manipulated Amazon product reviews. Flagging repetitive or bot-like reviews for Shopify stores. Screening mobile app reviews for suspicious content. Quality-checking user reviews on multi-vendor marketplaces. Monitoring competitor-driven false negative or positive reviews. There can be many more scenarios where this workflow helps identify misleading product reviews. Troubleshooting Guide | Issue | Possible Cause | Solution | | ---------------------------- | ----------------------------------- | ------------------------------------------------------ | | No data after review fetch | Scraper API returned empty response | Validate scraper URL and structure | | Duplicate reviews inserted | Hash mismatch | Ensure Generate Review Hash1 uses the correct fields | | Slack alert not triggered | Bot not added to channel | Add bot to the target Slack channel | | AI response fails to parse | Model returned non-JSON response | Strengthen "JSON only" prompt in AI analysis | | Airtable search inconsistent | Airtable returns empty objects | Rely on Normalize Airtable Result for correction | Need Help If you need assistance customizing this workflow, integrating additional systems or designing advanced review moderation solutions, our n8n workflow development team at WeblineIndia is available to help. We offer support for: Workflow setup and scaling Custom automation logic AI-driven enhancements Integration with third-party platforms And so much more. Feel free to contact us for guidance, implementation or to build similar automated systems tailored to your needs.

This workflow automates the creation of realistic Multi-speaker podcasts using ElevenLabsv3 API by reading a script from Google Sheets and saving the final MP3 file to Google Drive. Data Source – Dialogue scripts are stored in a Google Sheet. Each row contains: Speaker name (optional) Voice ID (from ElevenLabs) Text to be spoken Data Preparation – The workflow transforms the spreadsheet content into the proper JSON format required by the ElevenLabs API. Podcast Generation – ElevenLabs’ Eleven v3 model converts the prepared text into expressive, natural-sounding dialogue. It supports not only speech but also non-verbal cues and audio effects (e.g., \[laughs], \[whispers], \[clapping]). File Storage – The generated audio file is automatically uploaded to Google Drive, organized by timestamped filenames. Key Advantages Seamless Automation** – From dialogue writing to final audio upload, everything runs automatically in one workflow. Multi-Speaker Support** – Easily assign different voices to multiple characters for dynamic conversations. Expressive & Realistic Output** – Supports emotions, speech styles, and ambient effects, making podcasts more immersive. Flexible Content Input** – Scripts can be collaboratively written and edited in Google Sheets, with no technical knowledge required. Scalable & Reusable** – Can generate multiple podcast episodes in seconds, ideal for content creators, educators, or businesses. Cloud Integration** – Final audio files are securely stored in Google Drive, ready to be shared or published. How It Works The workflow processes a structured script from a spreadsheet and uses AI to generate a realistic conversation. Manual Trigger: The workflow is started manually by a user clicking "Execute workflow" in n8n. Get Dialogue: The "Get dialogue" node fetches the podcast script data from a specified Google Sheet. The sheet should contain columns for Speaker (optional), Voice ID, and the dialogue Input/Text. Prepare Dialogue: The "Code" node transforms the raw sheet data into the precise JSON format required by the ElevenLabs API. It creates an array of objects where each object contains the text and the corresponding voice_id for each line of dialogue. Generate Podcast: The "HTTP Request" node sends a POST request to the ElevenLabs Text-to-Dialogue API endpoint (/v1/text-to-dialogue). It sends the transformed dialogue array in the request body, instructing the API to generate a single audio file with a conversation between the specified voices. Upload File: The "Upload file" node takes the audio file response from ElevenLabs and saves it to a designated folder in Google Drive.. Set Up Steps To use this workflow, you must complete the following configuration steps: Prepare the Google Sheet: Clone the Template: Duplicate the provided Google Sheet template into your own Google Drive. Fill the Script: Column A (Speaker): Optional. Add speaker names for your reference (e.g., "Host", "Guest"). Column B (Voice ID): Mandatory. Enter the unique Voice ID for each line from ElevenLabs. Column C (Input): Mandatory. Write the dialogue text for each speaker. You can use [non-speech audio events] like [laughs] or [whispers] to add expression. Configure ElevenLabs API Credentials: Login or create FREE account on Elevenlabs Edit the "Generate podcast" node's credentials. Create an HTTP Header Auth credential named "ElevenLabs API". Set the Name to xi-api-key and the Value to your actual ElevenLabs API key. Configure Google Services: Google Sheets: Ensure the "Get dialogue" node has valid OAuth credentials and that the documentId points to your copy of the script sheet. Google Drive: Ensure the "Upload file" node has valid OAuth credentials and that the folderId points to the correct Google Drive folder where you want the audio files saved. Need help customizing? Contact me for consulting and support or add me on Linkedin.

Unleash the full potential of your website's search engine performance and user experience with this all-in-one n8n automation template. Designed for SEO professionals and webmasters, this suite provides meticulous on-page and technical SEO auditing, deep insights into Core Web Vitals (LCP & INP), and an intelligent AI-powered chatbot for instant insights and troubleshooting. Key Features: Comprehensive On-Page SEO Audit: Automatically checks for **missing or malformed titles, meta descriptions, H1s (including multiple H1s), missing alt text on images, and canonical tag issues. Detailed Technical SEO Scan: Verifies **HTTPS implementation, robots.txt accessibility and content, and sitemap.xml presence. Core Web Vitals Monitoring: Leverages **Google PageSpeed Insights to continuously track and alert on critical performance metrics like Largest Contentful Paint (LCP) and Interaction to Next Paint (INP). AI-Powered Analysis & Recommendations: Integrates advanced AI models (ChatGPT, Claude, or Gemini) to **analyze audit findings, provide actionable recommendations for improvements, and even suggest better alt text for images based on content context. Intelligent SEO Chatbot: A dynamic chatbot triggered by webhooks understands natural language queries, extracts entities (URLs, keywords, SEO topics), and provides **instant, AI-generated answers about SEO best practices, Core Web Vitals explanations, or even specific site data (via Google Search Console integration). Automated Reporting & Alerts: Logs all audit data to **Google Sheets for historical tracking and sends real-time Slack alerts for critical SEO issues or performance degradations. Streamline your SEO workflow, ensure optimal website health, and react swiftly to performance challenges. This template is your ultimate tool for staying ahead in the competitive digital landscape.

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How it works Transform your WhatsApp messages into an organized journal with AI-powered transcription and media management. • Receive text, audio, or image messages via WhatsApp • Automatically transcribe audio messages using Google Gemini AI • Store media files in organized Google Drive folders • Update a central Google Doc with timestamped entries • Get instant confirmation messages Set up steps WhatsApp Business API: Set up credentials and configure the WhatsApp Trigger node Google Doc: Create a new Google Doc and paste its URL in all three "Actualizar documento" nodes (replace YOUR_GOOGLE_DOC_URL) Google Drive folders: • Create an "Audio" folder and paste its ID in the "Subir Audio" node (replace YOUR_AUDIO_FOLDER_ID) • Create an "Imagenes" folder and paste its ID in the "Subir Imagenes" node (replace YOUR_IMAGE_FOLDER_ID) Authorized numbers: Add your authorized WhatsApp phone numbers in the "If" node (replace YOURNUMBERPHONE(1), YOURNUMBERPHONE(2), YOURNUMBERPHONE(3) with numbers in international format without + sign, e.g., 5491112345678) WhatsApp Phone Number ID: Replace YOUR_WHATSAPP_PHONE_NUMBER_ID in all three confirmation nodes with your WhatsApp Business Phone Number ID Google Gemini: Configure your Google Gemini API credentials in the "Transcribir audio" node Requirements Credentials needed: • WhatsApp Business API account and credentials • Google Workspace account (Google Docs + Google Drive) • Google Gemini API key for audio transcription Nodes used: • WhatsApp Trigger • WhatsApp (for sending messages and downloading media) • Google Docs (for journal updates) • Google Drive (for media storage) • Google Gemini (for audio transcription) • If, Switch, HTTP Request nodes Perfect for content creators, researchers, writers, or anyone who wants to capture thoughts on-the-go via WhatsApp and have them automatically organized in a searchable, timestamped journal with AI-powered transcriptions!

This workflow allows you to get all the slides from a presentation and get thumbnails of pages. Google Slides node: This Google Slides node will get all the slides from a presentation. Google Slides1 node: This node will return thumbnails of the pages that were returned by the previous node. Based on your use case, to upload the thumbnails to Dropbox, Google Drive, etc, you can use the respective nodes.