Workflow Templates

📍Overview This no-code workflow is built for creators, agencies, and operators who want to automate the repurposing of Instagram Reels. It runs end-to-end and outputs structured insights and content-ready scripts—without touching a single tool manually. 🧰 What It Does Triggered simply by sending an Instagram Reel URL via Telegram. Downloads the Reel automatically. Converts video to audio using FreeConvert API. Transcribes speech to text using AssemblyAI. Analyzes both transcript and description using a connected LLM (OpenAI or Mistral). Extracts: Niche Core message 3 viral content hooks 3 ready-to-use short-form video scripts Saves all data to a Google Sheet for easy reuse by the creator or team. 🧪 APIs & Integrations Telegram Bot API (for triggering) FreeConvert API (MP4 to MP3 conversion) AssemblyAI (for transcription) OpenAI or Mistral (LLM for content analysis) Google Sheets API (for logging all outputs) ✅ Requirements An n8n instance (self-hosted or cloud) AssemblyAI API key FreeConvert API key Telegram Bot token Google service account credentials Your preferred LLM key (OpenAI or Mistral) 💡 Why Use This Workflow Runs entirely from Telegram—no dashboards required Helps you extract deep insights and reusable content from any Instagram Reel All tools used are free or very low cost Ideal for scaling personal brands or agency operations

Automated WhatsApp Welcome Messages for Sales Leads with Google Sheets & Rapiwa Who is this for? This automation is ideal for sales teams, digital marketers, support agents, or small business owners who collect leads in Google Sheets and want to automatically send WhatsApp welcome messages. It's a cost-effective and easy-to-use solution built for those not using the official WhatsApp Business API but still looking to scale communication. What this Workflow Does This n8n automation reads leads from a connected Google Sheet, verifies if the provided WhatsApp numbers are valid using the Rapiwa API, and sends a personalized welcome message. It updates the sheet based on delivery success or failure, and continues this process every 5 minutes — ensuring new leads are automatically engaged. Key Features Automatic Scheduling**: Runs every 5 minutes (adjustable) Google Sheets Integration**: Reads and updates lead data WhatsApp Number Validation**: Confirms number validity via Rapiwa Personalized Messaging**: Uses lead name for custom messages Batch Processing**: Sends up to 60 messages per cycle Safe API Usage**: Adds 5-second delay between each message Error Handling**: Marks failed messages as not sent and unverified Live Status Updates**: Sheet columns are updated after each attempt Loop Logic**: Repeats continuously to catch new rows How to Use Step-by-step Setup Prepare Your Google Sheet Copy this Sample Sheet Ensure it includes the following columns: WhatsApp No name (note: trailing space is required) row_number status, check, validity Connect Google Sheets in n8n Use OAuth2 credentials to allow n8n access Set the workflow to fetch rows where check is not empty Get a Rapiwa Account Sign up at https://rapiwa.com Add your WhatsApp number Retrieve your Bearer Token from your Rapiwa dashboard Configure HTTP Request Nodes Use Rapiwa's API endpoints: Verify Number: https://app.rapiwa.com/api/verify-whatsapp Send Message: https://app.rapiwa.com/api/send-message Add your Bearer Token to the header Start Your Workflow Run the n8n automation It will read leads, clean phone numbers, verify WhatsApp validity, send messages, and update the sheet accordingly Requirements A Google Sheet with correctly formatted columns Active Rapiwa subscription (~$5/month) A valid Bearer Token from Rapiwa Your WhatsApp number connected to Rapiwa n8n instance with: Google Sheets integration (OAuth2 setup) HTTP Request capability Google Sheet Column Reference | name | number | email | time | check | validity | status | |-----------------|--------------|-------------------|-----------------------------|---------|------------|-----------| | Abdul Mannan | 8801322827799| contact@spagreen.net| September 14th 2025, 10:34 | checked | verified | sent | | Abdul Mannan | 8801322827798| contact@spagreen.net| September 14th 2025, 10:34 | checked | unverified | not sent | Workflow Logic Summary Trigger Every 5 Minutes Fetch All Rows with Pending Status Limit to 60 Rows per Execution Clean and Format Phone Numbers Check Number Validity via Rapiwa Condition Check: If valid → Send Message If invalid → Update status as not sent, unverified Send WhatsApp Message via Rapiwa Update Sheet Row On success: sent, verified, checked On failure: not sent, unverified Delay 5 seconds before next message Repeat for next lead Customization Ideas Add image or document sending support via Rapiwa Customize messages based on additional fields (e.g., product, service) Log failures to a separate sheet Send admin email for failed batches Add support for multilingual messages Notes & Warnings The column name "name " includes a space — do not remove or rename it. International number format is required for Rapiwa to work correctly. If you're sending many messages, increase the Wait node delay to prevent API throttling. Support WhatsApp Support: Chat Now Discord: Join SpaGreen Community Facebook Group: SpaGreen Support Website: https://spagreen.net Developer Portfolio: Codecanyon SpaGreen

Simplify event planning with this automated n8n workflow. Triggered by incoming requests, it fetches speaker and audience data from Google Sheets, analyzes profiles and preferences, and generates optimized session recommendations. The workflow delivers formatted voice responses and updates tracking data, ensuring organizers receive real-time, tailored suggestions. 🎙️📊 Key Features Real-time analysis of speaker and audience data for personalized recommendations. Generates optimized session lineups based on profiles and preferences. Delivers responses via voice agent for a seamless experience. Logs maintain a detailed recommendation history in Google Sheets. Workflow Process The Webhook Trigger node initiates the workflow upon receiving voice agent or external system requests. Parse Voice Request** processes incoming voice data into actionable parameters. Fetch Database** retrieves speaker ratings, past sessions, and audience ratings from Google Sheets. Calculate & Analyze** combines voice request data with speaker profiles and audience insights for comprehensive matching. AI Optimization Engine** analyzes speaker-audience fit and recommends optimal session lineups. Format Recommendations** structures the recommendations for voice agent response. Voice Agent Response** returns formatted recommendations to the user with natural language summary and structured data. Update Tracking Sheet** saves recommendation history and analytics to Google Sheets. If errors occur, the Check for Errors node branches to: Format Error Response prepares an error message. Send Error Response delivers the error notification. Setup Instructions Import the workflow into n8n and configure Google Sheets OAuth2 for data access. Set up the Webhook Trigger with your voice agent or external system's API credentials. Configure the AI Optimization Engine node with a suitable language model (e.g., Anthropic Chat Model). Test the workflow by sending sample voice requests and verifying recommendations. Adjust analysis parameters as needed for specific event requirements. Prerequisites Google Sheets OAuth2 credentials Voice agent API or integration service AI/LLM service for optimization (e.g., Anthropic) Structured speaker and audience data in a Google Sheet Google Sheet Structure: Create a sheet with columns: Speaker Name Rating Past Sessions Audience Rating Preferences Updated At Modification Options Customize the Calculate & Analyze node to include additional matching criteria (e.g., topic expertise). Adjust the AI Optimization Engine to prioritize specific session formats or durations. Modify voice response templates in the Voice Agent Response node with branded phrasing. Integrate with event management tools (e.g., Eventbrite) for live data feeds. Set custom error handling rules in the Check for Errors node. Discover more workflows – Get in touch with us

Overview When designing user interfaces, toolbar icons often get overlooked, even though their placement and grouping dramatically impact usability and user flow. This workflow leverages Gemini AI to automatically analyze UI screens, classify toolbar icons based on Apple’s Human Interface Guidelines (HIG), and suggest optimal placements. By combining AI analysis with structured placement logic, this workflow helps designers build more consistent, efficient, and user-friendly interfaces—without spending hours manually arranging icons. 🚀 Features AI Classification**: Uses Gemini AI to analyze screenshots and classify icons into roles like .primaryAction, .navigation, .confirmationAction, and more. HIG-Based Placement**: Automatically assigns icons to the correct toolbar areas—Leading (Left), Trailing (Right), Center, Bottom, or System-decided. Usage-Aware Reordering**: Reorders icons based on frequency of use so the most relevant actions appear where users expect them. JSON Output**: Delivers structured results for seamless integration into design tools or documentation. 🔧 Setup Instructions Install the Workflow: Import the workflow into your n8n instance. Configure Input: Upload a screenshot of your UI. Upload a set of icons you want to classify and place. Set Up Gemini AI Node: Add your Gemini AI API key in the node’s credentials. Run the Workflow: Submit the inputs and let the AI classify and assign placements. Export Results: Copy the JSON output or connect the workflow to your preferred design/documentation tools. ⚙️ How It Works Form Submission – Capture screenshot + icons. Gemini AI Agent – Interprets screen context and classifies each icon. Placement Logic – Maps icons to the correct toolbar areas. Reordering – Adjusts order based on relevance and HIG standards. Structured Output – Produces clean JSON for further use. 🎨 Customization Change AI Prompts**: Modify the Gemini AI node prompts to reflect your app’s design language. Adjust Placement Rules**: Update logic to follow custom guidelines beyond Apple HIG. Integrate with Design Tools**: Send the JSON output directly to tools like Figma, Sketch, or internal systems. 💡 Why This Matters Consistency**: Ensures toolbar designs always follow Apple’s HIG. Efficiency**: Saves designers hours of manual icon placement. Scalability**: Works across multiple screens, flows, and apps. AI-Assisted Design**: Augments designer decisions with structured insights instead of replacing them.

Gets Google Calendar events for the day (12 hours from execution time), and filters out in-person meetings, Signal meetings, and meetings canceled by Calendly ("transparent").

This workflow implements a Retrieval-Augmented Generation (RAG) system that integrates Google Drive and Qdrant. This setup creates a powerful, self-updating knowledge base that provides accurate, context-aware answers to user queries. Key Advantages Automated Knowledge Base Updates** No manual intervention is required—documents in Google Drive are automatically synchronized with Qdrant. Efficient Search and Retrieval** Vector embeddings enable fast and precise retrieval of relevant information. Scalable and Flexible** Works with multiple documents and supports continuous growth of your dataset. Seamless AI Integration** Combines OpenAI embeddings for vectorization and Google Gemini for high-quality natural language answers. Metadata-Enhanced Storage** Each document stores metadata (file ID and name), making it easy to manage and track document versions. End-to-End RAG Pipeline** From document ingestion to AI-powered Q\&A, everything is handled inside one n8n workflow. How It Works This workflow implements a Retrieval-Augmented Generation (RAG) system that automatically processes, stores, and retrieves document information for AI-powered question answering. Here’s how it functions: Document Processing & Vectorization: The system monitors a specified Google Drive folder for new or updated files. When a file is added or modified, it is downloaded and split into manageable chunks using a Recursive Character Text Splitter. Each chunk is converted into vector embeddings using OpenAI's embedding model. These vectors, along with metadata (file ID, file name), are stored in a Qdrant vector database. Automatic Updates: The workflow includes a mechanism to delete old vectors associated with an updated file before inserting the new ones, ensuring the knowledge base remains current. Query Handling & Response Generation: When a user sends a chat message (via a chat trigger), the system: Retrieves the most relevant document chunks from Qdrant based on the query's semantic similarity. Uses a Google Gemini language model to generate a context-aware answer grounded in the retrieved documents. This provides accurate, source-based responses instead of relying solely on the AI's internal knowledge. Initial Setup & Maintenance: The workflow can be triggered manually to create the Qdrant collection or clear all existing data. It processes all existing files in the Drive folder during initial setup, populating the vector store. Set Up Steps To configure this workflow, follow these steps: STEP 1: Create Qdrant Collection Replace QDRANTURL in the "Create collection" and "Clear collection" nodes with your Qdrant instance URL (e.g., http://your-qdrant-host:6333). Replace COLLECTION with your desired collection name. Ensure the Qdrant API credentials are correctly set in the respective HTTP Request nodes. STEP 2: Configure Google Drive Access Set up OAuth credentials for Google Drive to allow the workflow to: Read files from a specific folder . Download files for processing. Update the Folder ID in the "Search files" and "Update?" trigger nodes to point to your target Google Drive folder. STEP 3: Set Up AI Models Configure the OpenAI API credentials in the Embeddings nodes for generating text embeddings. Configure the Google Gemini (PaLM) API credentials in the Google Gemini Chat Model node for generating answers. STEP 4: Configure Metadata The system automatically attaches metadata (file_id, file_name) to each document chunk. This is set in the Default Data Loader nodes. This metadata is crucial for identifying the source of information and for the update mechanism. STEP 5: Test the RAG System The workflow includes a chat trigger ("When chat message received") for testing. Send a query to test the retrieval and answer generation process. Need help customizing? Contact me for consulting and support or add me on Linkedin.

🔧 How It Works **This n8n workflow leverages an agentic AI solution, where multiple AI agents collaborate to process and generate tailored job application assets. ✅ Features Agent-based AI Coordination: Utilizes multiple AI agents working in sequence to analyze the job description and generate results. Outputs: A customized cover letter An optimized resume (CV) A list of interview preparation questions Automated Delivery: The final outputs are created as Google Docs and stored in your connected Google Drive folder. 🧾 Input Requirement Simply provide a LinkedIn job URL as the input. Example: https://www.linkedin.com/jobs/view/4184156975 ⚙️ Setup Instructions To deploy and run this workflow, you'll need to configure the following credentials: Google Cloud Platform (GCP) Enable the Google Drive API Set up OAuth credentials for n8n integration OpenAI API Key Needed for generating the content (cover letter, CV, and questions) BrightData (formerly Luminati) Used to scrape and extract job details from the LinkedIn job link ⚠️ Setup requires moderate technical familiarity with APIs and OAuth. A step-by-step configuration guide is recommended for beginners.

Auto-Routing Nicereply Feedback to Microsoft Teams by Team and Sentiment Automatically collect client feedback from Nicereply, analyze sentiment, and send it to the right Microsoft Teams channels — smartly split by team, tone, and comment presence. About this Workflow This workflow pulls customer satisfaction feedback from Nicereply, filters out irrelevant or test entries, and evaluates each item based on the team it belongs to and the sentiment of the response (Great, OK, Bad). It automatically routes the feedback to Microsoft Teams — either as a summary in a channel or a direct message — depending on the team's role and whether a comment is included. Perfect for support, delivery, consulting, and documentation teams that want to stay in the loop with customer sentiment. It ensures that positive feedback reaches the teams who earned it, and that negative feedback is escalated quickly to leads or management. Use Cases Send daily customer feedback directly to the responsible teams in MS Teams Automatically escalate negative responses to leads or managers Avoid clutter by filtering out unimportant or test entries Keep internal teams motivated by sharing only the most relevant praise How it works Schedule Trigger Starts the workflow on a set schedule (e.g., daily at 7:00 AM) Get Feedback Pulls customer feedback from Nicereply using survey ID Split Out Processes each feedback entry separately Edit Feedbacks Renames or adjusts fields for easier filtering and readability Change Survey ID Maps internal survey identifiers for accurate team routing (Survey ID can be found in Nicereply: Settings > Surveys > [Survey] > ID) Filter Excludes old responses Code Node Tag unknown clients Change Happiness Value Converts score into “Great”, “OK”, or “Bad” for routing logic Without Comment Checks if feedback includes a text comment or not Send Feedback Without Comment Routes simple feedback (no comment) to MS Teams based on team + score Send Feedback With Comment Routes full feedback with comment to MS Teams for closer review Feedback Routing Logic Each team receives only what’s most relevant: Support, Docs, Consulting* get only *Great** feedback to boost morale Team Leads* receive *OK and Bad** feedback so they can follow up Management* is only alerted to *Bad** feedback for critical response These rules can be freely customized. For example, you may want Support to receive all responses, or Management only when multiple Bad entries are received. The structure is modular and easily adjustable. How to Use Import the workflow Load the .json file into your Easy Redmine automation workspace Set up connections Nicereply API key or integration setup Microsoft Teams integration (chat and/or channel posting) Insert your Survey ID(s) You’ll find these in the Nicereply admin panel under Survey settings Customize team logic Adjust survey-to-team mappings and message routing as needed Edit Teams message templates Modify message text or formatting based on internal tone or content policies Test with real data Run manually and verify correct delivery to MS Teams Deploy and schedule Let it run on its own to automate the feedback cycle Requirements Nicereply account with active surveys Microsoft Teams account with permissions to post to channels or send chats Optional Enhancements Add AI to summarize long comments Store feedback history in external DB Trigger follow-up tasks or alerts for repeated Bad scores Localize messages for multilingual feedback systems Integrate additional tools like Slack, Easy Redmine, etc. Tips for a Clean Setup Keep team routing logic in one place for easy updates Rename all nodes clearly to reflect their function (e.g., Change Happiness Value) Add logging or alerting in case of failed delivery or empty feedback pull Use environment variables for tokens and survey IDs where possible

HubSpot Lead Refinement 🚀 How it works Triggers: HubSpot Trigger: Fires when contacts are created/updated. Manual Trigger: Run on demand for testing or batch checks. Get Recently Created/Updated Contacts: Pulls fresh contacts from HubSpot. Edit Fields (Set): Maps key fields (First Name, Last Name, Email) for the Agent. AI Agent: First reads your Google Doc (via the Google Docs tool) to learn the research steps and output format. Then uses SerpAPI (Google engine) to locate the contact’s likely LinkedIn profile and produce a concise result. Code – Remove Think Part: Cleans the model output (removes hidden “think” blocks / formatting) so only the final answer remains. HubSpot Update: Writes the cleaned LinkedIn URL to the contact (via email match). 🔑 Required Credentials: HubSpot App Token (Private App) — for Get/Update contact nodes. HubSpot Developer OAuth (optional) — if you use the HubSpot * Trigger node for event-based runs. Google Service Account — for the Google Docs tool (share your * playbook doc with this service account). OpenRouter — for the OpenRouter Chat Model used by the AI Agent. SerpAPI — for targeted Google searches from within the Agent. 🛠️ Setup Instructions HubSpot Create a Private App and copy the Access Token. Add or confirm the contact property linkedinUrl (Text). Plug the token into the HubSpot nodes. If using HubSpot Trigger, connect your Developer OAuth app and subscribe to contact create/update events. Google Docs (Living Instructions) ➡️ Sample configuration doc file Copy the sample doc file and modify to your need. Share the doc with your Google Service Account (Viewer is fine). In the Read Google Docs node, paste the Document URL. OpenRouter & SerpAPI Add your OpenRouter key to the OpenRouter Chat Model credential. Add your SerpAPI key to the SerpAPI tool node. (Optional) In your Google Doc or Agent prompt, set sensible defaults for SerpAPI (engine=google, hl=en, gl=us, num=5, max 1–2 searches). ✨ What you get Auto-enriched contacts with a LinkedIn URL and profile insights (clean, validated output). A research process you can change anytime by editing the Google Doc—no workflow changes needed. Tight, low-noise searches via SerpAPI to keep costs down. And that’s it—publish and let the Agent enrich new leads automatically while you refine the rules in your doc. It allows handing off to a team who wouldn't necessarily tweak the automation nodes.

Blockify® Technical Manual Data Optimization Workflow Blockify Optimizes Data for Technical Manual RAG and Agents - Giving Structure to Unstructured Data for ~78X Accuracy, when pairing Blockify Ingest and Blockify Distill Learn more at https://iternal.ai/blockify Get Free Demo API Access here: https://console.blockify.ai/signup Read the Technical Whitepaper here: https://iternal.ai/blockify-results See example Accuracy Comparison here: https://iternal.ai/case-studies/medical-accuracy/ Blockify is a data optimization tool that takes messy, unstructured text, like hundreds of sales‑meeting transcripts or long proposals, and intelligently optimizes the data into small, easy‑to‑understand "IdeaBlocks." Each IdeaBlock is just a couple of sentences in length that capture one clear idea, plus a built‑in contextualized question and answer. With this approach, Blockify improves accuracy of LLMs (Large Language Models) by an average aggregate 78X, while shrinking the original mountain of text to about 2.5% of its size while keeping (and even improving) the important information. When Blockify's IdeaBlocks are compared with the usual method of breaking text into equal‑sized chunks, the results are dramatic. Answers pulled from the distilled IdeaBlocks are roughly 40X more accurate, and user searches return the right information about 52% more accurate. In short, Blockify lets you store less data, spend less on computing, and still get better answers- turning huge documents into a concise, high‑quality knowledge base that anyone can search quickly. Blockify works by processing chunks of text to create structured data from an unstructured data source. Blockify® replaces the traditional "dump‑and‑chunk" approach with an end‑to‑end pipeline that cleans and organizes content before it ever hits a vector store. Admins first define who should see what, then the system ingests any file type—Word, PDF, slides, images—inside public cloud, private cloud, or on‑prem. A ​context‑aware splitter finds natural breaks, and a series of specially developed Blockify LLM model turns each segment into a draft IdeaBlock. GenAI systems fed with this curated data return sharper answers, hallucinate far less, and comply with security policies out of the box. The result: higher trust, lower operating cost, and a clear path to enterprise‑scale RAG without the cleanup headaches that stall most AI rollouts.

1. Overview The IngestionDocs workflow is a fully automated **document ingestion and knowledge management system* built with *n8n**. Its purpose is to continuously ingest organizational documents from Google Drive, transform them into vector embeddings using OpenAI, store them in Pinecone, and make them searchable and retrievable through an AI-powered Q&A interface. This ensures that employees always have access to the most up-to-date knowledge base without requiring manual intervention. 2. Key Objectives Automated Ingestion** → Seamlessly process new and updated documents from Google Drive.\ Change Detection** → Track and differentiate between new, updated, and previously processed documents.\ Knowledge Base Construction** → Convert documents into embeddings for semantic search.\ AI-Powered Assistance** → Provide an intelligent Q&A system for employees to query manuals.\ Scalable & Maintainable** → Modular design using n8n, LangChain, and Pinecone. 3. Workflow Breakdown A. Document Monitoring and Retrieval The workflow begins with two Google Drive triggers: File Created Trigger → Fires when a new document is uploaded.\ File Updated Trigger → Fires when an existing document is modified.\ A search operation lists the files in the designated Google Drive folder.\ Non-downloadable items (e.g., subfolders) are filtered out.\ For valid files: The file is downloaded.\ A SHA256 hash is generated to uniquely identify the file's content. B. Record Management (Google Sheets Integration) To keep track of ingestion states, the workflow uses a **Google Sheets--based Record Manager**:\ Each file entry contains:\ Id** (Google Drive file ID)\ Name** (file name)\ hashId** (SHA256 checksum)\ The workflow compares the current file's hash with the stored one:\ New Document** → File not found in records → Inserted into the Record Manager.\ Already Processed** → File exists and hash matches → Skipped.\ Updated Document** → File exists but hash differs → Record is updated. This guarantees that only new or modified content is processed, avoiding duplication. C. Document Processing and Vectorization Once a document is marked as new or updated:\ Default Data Loader extracts its content (binary files supported).\ Pages are split into individual chunks.\ Metadata such as file ID and name are attached.\ Recursive Character Text Splitter divides the content into manageable segments with overlap.\ OpenAI Embeddings (text-embedding-3-large) transform each text chunk into a semantic vector.\ Pinecone Vector Store stores these vectors in the configured index:\ For new documents, embeddings are inserted into a namespace based on the file name.\ For updated documents, the namespace is cleared first, then re-ingested with fresh embeddings. This process builds a scalable and queryable knowledge base. D. Knowledge Base Q&A Interface The workflow also provides an **interactive form-based user interface**:\ Form Trigger** → Collects employee questions.\ LangChain AI Agent**:\ Receives the question.\ Retrieves relevant context from Pinecone using vector similarity search.\ Processes the response using OpenAI Chat Model (gpt-4.1-mini).\ Answer Formatting**:\ Responses are returned in HTML format for readability.\ A custom CSS theme ensures a modern, user-friendly design.\ Answers may include references to page numbers when available. This creates a self-service knowledge base assistant that employees can query in natural language. 4. Technologies Used n8n** → Orchestration of the entire workflow.\ Google Drive API** → File monitoring, listing, and downloading.\ Google Sheets API** → Record manager for tracking file states.\ OpenAI API**: text-embedding-3-large for semantic vector creation.\ gpt-4.1-mini for conversational Q&A.\ Pinecone** → Vector database for embedding storage and retrieval.\ LangChain** → Document loaders, text splitters, vector store connectors, and agent logic.\ Crypto (SHA256)** → File hash generation for change detection.\ Form Trigger + Form Node** → Employee-facing Q&A submission and answer display.\ Custom CSS** → Provides a modern, responsive, styled UI for the knowledge base. 5. End-to-End Data Flow Employee uploads or updates a document → Google Drive detects the change.\ Workflow downloads and hashes the file → Ensures uniqueness and detects modifications.\ Record Manager (Google Sheets) → Decides whether to skip, insert, or update the record.\ Document Processing → Splitting + Embedding + Storing into Pinecone.\ Knowledge Base Updated → The latest version of documents is indexed.\ Employee asks a question via the web form.\ AI Agent retrieves embeddings from Pinecone + uses GPT-4.1-mini → Generates a contextual answer.\ Answer displayed in styled HTML → Delivered back to the employee through the form interface. 6. Benefits Always Up-to-Date** → Automatically syncs documents when uploaded or changed.\ No Duplicates** → Smart hashing ensures only relevant updates are reprocessed.\ Searchable Knowledge Base** → Employees can query documents semantically, not just by keywords.\ Enhanced Productivity** → Answers are immediate, reducing time spent browsing manuals.\ Scalable** → New documents and users can be added without workflow redesign. ✅ In summary, IngestionDocs is a **robust AI-driven document ingestion and retrieval system* that integrates *Google Drive, Google Sheets, OpenAI, and Pinecone* within *n8n**. It continuously builds and maintains a knowledge base of manuals while offering employees an intelligent, user-friendly Q&A assistant for fast and accurate knowledge retrieval.

Description: Keep your customer knowledge base up to date with this n8n automation template. The workflow connects Zendesk with Google Sheets, automatically fetching tickets tagged as “howto,” enriching them with requester details, and saving them into a structured spreadsheet. This ensures your internal or public knowledge base reflects the latest customer how-to queries—without manual copy-pasting. Perfect for customer support teams, SaaS companies, and service providers who want to streamline documentation workflows. What This Template Does (Step-by-Step) ⚡ Manual Trigger or Scheduling Run the workflow manually for testing/troubleshooting, or configure a schedule trigger for daily/weekly updates. 📥 Fetch All Zendesk Tickets Connects to your Zendesk account and retrieves all available tickets. 🔍 Filter for "howto" Tickets Only Processes only tickets that contain the “howto” tag, ensuring relevance. 👤 Enrich User Data Fetches requester details (name, email, profile info) to provide context. 📊 Update Google Sheets Knowledge Base Saves ticket data—including Ticket No., Description, Status, Tag, Owner Name, and Email. ✔️ Smart update prevents duplicates by matching on description. 🔁 Continuous Sync Each new or updated “howto” ticket is synced automatically into your knowledge base sheet. Key Features 🔍 Tag-based filtering for precise categorization 📊 Smart append-or-update logic in Google Sheets ⚡ Zendesk + Google Sheets integration with OAuth2 ♻️ Keeps knowledge base fresh without manual effort 🔐 Secure API credential handling Use Cases 📖 Maintain a live “how-to” guide from real customer queries 🎓 Build self-service documentation for support teams 📩 Monitor and track recurring help topics 💼 Equip knowledge managers with a ready-to-export dataset Required Integrations Zendesk API (for ticket fetch + user info) Google Sheets (for storing/updating records) Why Use This Template? ✅ Automates repetitive data entry ✅ Ensures knowledge base accuracy & freshness ✅ Reduces support team workload ✅ Easy to extend with more tags, filters, or sheet logic