Workflow Templates

This n8n template helps you turn business cards into qualified sales opportunities — instantly. No more lost leads after events or networking meetups. Just send a business card photo via Telegram, and let AI handle the rest. The workflow extracts details (name, company, role, email, phone) using Google Vision OCR, analyzes context with OpenAI, and then generates personalized WhatsApp or email messages to help you follow up faster. Use Cases Capture leads instantly from events or meetups. Auto-analyze business card data into structured CRM entries. Send hyper-personalized WhatsApp messages or emails within minutes. Ideal for founders, marketers, and business development teams. How it Works Trigger: Send a photo of a business card via Telegram. Extract: Google Vision OCR reads and extracts all text from the image. Process: The extracted text is sent to an AI agent (OpenAI GPT-4.1-mini) for: Contact parsing (name, company, role, email, phone) Business/industry inference AI-generated personalized follow-up messages Output: The system returns structured JSON containing: Email subject & body WhatsApp draft message Fit score, opportunities, and next action steps How to Use Import the JSON template into n8n. Add your Telegram Bot Token to the “Telegram Trigger” node. Add your Google Cloud Vision API key in the HTTP Request node. Add your OpenAI credentials in the “OpenAI Chat Model” node. (Optional) Connect the output to Google Sheets, Airtable, or your CRM. Run the workflow — take a photo of a business card and watch the magic happen! Requirements Telegram Bot for image input Google Vision API Key OpenAI API Key (Optional) Integration with CRM or WhatsApp API Customization Ideas Replace Telegram with Email or WhatsApp triggers. Push qualified leads directly into HubSpot or Notion. Automate multi-step sequences based on AI fit scores.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Blockify® Data Optimization Workflow Blockify Optimizes Data for RAG - 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://api.blockify.ai/register 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.

Overview This n8n template demonstrates how you can generate an AI-produced weather analysis of your local radar loop and home assistant precipitation sensor(s) to keep your family informed of National Weather Service Alerts. With as crazy as things have been lately in the open world, how will you and your family know when a severe or extreme alert impacts your area? How it Works This workflow is triggered by a webhook which takes a latitude and longitude json payload to identify the area for monitoring. Then, it fetches the National Weather Service Alerts and filters them down to alerts which are currently active and their severity. Next, it fetches the local precipitation value from your Home Assistant instance (a value like "Light Rain" or "No Rain", etc) coupled with your respective weather.gov radar loop image. It then submits this data to OpenAI and produces an output regarding the image analysis. Finally, it takes this analysis and uses OpenAI to again generate a short summary. How to Use Import the workflow into your n8n instance Update the credentials in the problematic nodes Make sure you adjust the radar loop image that is being used Requirements A Home Assistant Instance (you could remove this data if you wanted) An OpenAI account for LLM and image analysis

Who’s it for This template is designed for anyone who wants to use Telegram as a personal AI assistant hub. If you often juggle tasks, emails, calendars, and expenses across multiple tools, this workflow consolidates everything into one seamless AI-powered agent. What it does Jarvis listens to your Telegram messages (text or audio) and processes them with OpenAI. Based on your request, it can:

AI Creative Director: Transform Phone Photos into Viral Ad Campaigns Description Turn amateur product snapshots into high-conversion advertising assets automatically. This agentic workflow acts as your personal Art Director. Simply send a photo to a Telegram bot, and the AI will analyze the image, conceptualize 3 distinct marketing angles, and generate professional-grade commercial photography. How it Works Ingest: Receives a photo and user caption via Telegram. Analyze: Uses Gemini Vision to understand the product and critique the original image quality. Ideate: A LangChain Agent (powered by Claude Sonnet via OpenRouter) acts as a Creative Director to brainstorm 3 unique ad concepts (e.g., Cyberpunk, Luxury, Minimalist). Create: Generates high-fidelity images using the Gemini 3 Pro API. Deliver: Sends the new ad creatives back to you on Telegram. Key Features Multi-Modal Pipeline:** Chains Vision models (Gemini) with Reasoning models (Claude) for superior context awareness. Structured Output:** Uses LangChain's Structured Output Parser to ensure consistent JSON formats. Secure Configuration:** Features a centralized CONFIG node to easily manage API keys and user IDs without editing complex logic. Visual Verification:* Includes an authentication check to ensure only *you (the authorized user) can trigger the bot. Prerequisites n8n Version:** 1.0+ (requires LangChain nodes). Telegram:** A Bot Token and your personal User ID. Google Cloud:** An API Key with access to Gemini models. OpenRouter:** An API Key for accessing Claude Sonnet (or your preferred LLM). Setup Instructions Import the Workflow: Paste the JSON into your n8n canvas. Open the CONFIG Node: Locate the green node at the start of the workflow. Enter Credentials: authorized_user_id: Your numeric Telegram User ID (to prevent strangers from using your API credits). telegram_bot_token: Your Bot Father token. google_api_key: Your Gemini API key. Activate: Toggle the workflow to "Active" and send a photo to your bot! Tags #AI #LangChain #Gemini #Telegram #Marketing #Agent #ImageGeneration #OpenRouter

Dungeons and Goblins — AI Telegram Voice Adventure with Persistent Memory This n8n template demonstrates how to use an AI agent with persistent memory to run a structured, rules-driven fantasy role-playing game entirely through Telegram voice messages. The workflow acts as a Dungeon Master, narrating scenes, resolving mechanics, performing dice rolls when authorized, and explicitly saving game state between turns. How it works A player actions are provided to Telegram bot via voice messages. The AI agent loads the current game state from n8n memory. A strict system prompt enforces rules, turn flow, and narration. When an action requires a dice roll, the agent waits for player authorization. Once authorized, the AI rolls, resolves the outcome, and applies changes. All state updates are emitted as structured data and saved to memory. The request and response are processed in Groq's STT and TTS. Use cases Solo text-based fantasy campaigns Persistent AI-driven adventures Testing stateful AI agents in n8n Educational examples of memory-aware workflows Requirements Groq API token (free tier supported) Telegram bot API token

Overview Auto-translate YouTube uploads to Japanese and post to Slack (DeepL + Slack) Who’s it for Marketing or community teams that follow English-speaking creators but share updates with Japanese audiences; language learners who want JP summaries of newly released videos; internal comms teams curating industry channels for a JP workspace. What it does This workflow detects new YouTube uploads, retrieves full metadata, translates the title and description into Japanese using DeepL, and posts a formatted message to a Slack channel. It also skips non-English titles to avoid unnecessary translation. How it works ・RSS watches a channel for new items. ・YouTube API fetches the full snippet (title/description). ・Text is combined into a single payload and sent to DeepL. ・The translated result + original metadata is merged and posted to Slack. Requirements ・YouTube OAuth (for reliable snippet retrieval) ・DeepL API key (Free or Pro) ・Slack OAuth How to set up ・Duplicate this template. ・Open the Config (Set) node and fill in YT_CHANNEL_ID, TARGET_LANG, SLACK_CHANNEL. ・Connect credentials for YouTube, DeepL, and Slack (don’t hardcode API keys in HTTP nodes). ・Click Execute workflow and verify one sample post. How to customize ・Change TARGET_LANG to any language supported by DeepL. ・Add filters (exclude Shorts, skip videos under N characters). ・Switch to Slack Blocks for richer formatting or thread replies. ・Add a fallback translator or retry logic on HTTP errors. Notes & limits DeepL Free/Pro have different endpoints/quotas and monthly character limits. YouTube and Slack also enforce rate limits. Keep credentials in n8n’s credential store; do not commit keys into templates. Rotate keys if you accidentally exposed them.

LinkedIn Content Generation Workflow Summary Automated workflow that transforms Notion content notes into publication-ready LinkedIn posts using Claude AI. Monitors Notion database and generates multiple variations based on structured outlines, so that the author can pick the one they like most. Use Cases Automate LinkedIn content creation from content planning database. Generate multiple post variations from a single outline. Maintain consistent voice and formatting across all posts. Scale content production while preserving quality. How It Works Trigger - Monitors Notion "Content Plan" database hourly for updates. Conditional Check - Verifies "LinkedIn Post (Main)" tag and "Ready for Writing" status Main Post - Claude generates single post from project name and notes Outline Analysis - Parallel process creates 3 distinct post concepts with different angles Multi-Post Generation - Each outline becomes a complete LinkedIn post Save to Notion - All posts automatically saved to database AI Setup: Claude Sonnet 4.5 (claude-sonnet-4-5-20250929) Main post: temperature 0.8 (creative) Multi-post: default temperature (consistent) How to Use Setup a content database in notion, or link your existing one: Use field mapping as outlined below or update field mapping in n8n template. Add content to Notion: Project name (topic) Notes (article content/key points) Tag: "LinkedIn Post (Main)" Status: "Ready for Writing" Workflow triggers automatically (hourly check) Retrieve posts from Notion database Review and publish to LinkedIn Requirements Credentials: Notion API (access to Content Plan database) Anthropic API key OpenAI API Key Notion Database: Connect Database Required properties: Project name (text) Notes (rich text) Tags (multi-select with "LinkedIn Post (Main)") Status (select with "Ready for Writing") Notes: Posts optimized for 1800 character limit Generates both single posts and multi-angle variations

This n8n workflow automatically generates a comprehensive dataset of 50 AI search prompts tailored to a specific company. It combines AI-powered company research with structured prompt generation to create monitoring queries for tracking brand visibility across AI search engines like ChatGPT, Perplexity, Claude, and Gemini. The dataset is ready for use and can be uploaded to any major AI search analytics platforms (like ALLMO.ai,...) or used in your own model. Who's it for & Use Cases SEO/GEO Marketing teams, Growth Managers, GTM engineers and Founders who want to: Create custom prompt datasets for visibility tracking platforms like ALLMO.ai Generate industry-specific search queries for AI model monitoring How It Works Phase 1: Company Research Start the workflow via the form and input your company name and website URL GPT-5 Mini with web search collects company information, including buyer personas, key features, and value proposition Phase 2: Prompt Generation Claude Sonnet 4.5 generates and refines natural language prompts based on Phase 1 findings English prompts are automatically translated into German Phase 3: Export & Implementation Wait for processing (~total of 2-5 minutes depending on website complexity) English and German prompt sets are merged with metadata and structured into table format Download the CSV file containing 50 prompts ready for import into AI Search monitoring systems (allmo.ai, etc.) How to Setup Just enter your API credentials in the Claude and ChatGPT Nodes. How to Expand You can update the system prompts for the "prompt writing engine" to create more prompts. You can update or add more translations. Output Structure: 25 English prompts + 25 German prompts (can be changed flexibly). Each prompt tagged with: company name, industry, category, language, and AI model for simple tracking. Ready for direct import into any GEO/ALLMO visibility tracking system. Requirements API Credentials: Anthropic API (Claude Sonnet 4.5) OpenAI API (GPT-5 Mini with web search capability) Data Input: Valid company website URL (publicly accessible) Company name as it should appear in tracking

This n8n template demonstrates how to automatically convert voice messages from Telegram into structured, searchable notes in Google Docs using AI transcription and intelligent tagging. Use cases are many: Try capturing ideas on-the-go while walking, recording meeting insights hands-free, creating voice journals, or building a personal knowledge base from spoken thoughts! Good to know OpenAI Whisper transcription costs approximately $0.006 per minute of audio ChatGPT tagging adds roughly $0.001-0.003 per message depending on length The workflow supports both German and English voice recognition Text messages are also supported - they bypass transcription and go directly to AI tagging Perfect companion: Combine with the "Weekly AI Review**" workflow for automated weekly summaries of all your notes! How it works Telegram receives your voice message or text and triggers the workflow An IF node intelligently detects whether you sent audio or text content For voice messages: Telegram downloads the audio file and OpenAI Whisper transcribes it to text For text messages: Content is passed directly to the next step ChatGPT analyzes the content and generates up to 3 relevant keywords (Work, Ideas, Private, Health, etc.) A function node formats everything with Swiss timestamps, message type indicators, and clean structure The formatted entry gets automatically inserted into your Google Doc with date, keywords, and full content Telegram sends you a confirmation with the transcribed/original text so you can verify accuracy How to use Simply send a voice message or text to your Telegram bot - the workflow handles everything automatically The manual execution can be used for testing, but in production this runs on every message Voice messages work best with clear speech in quiet environments for optimal transcription Requirements Telegram Bot Token and configured webhook OpenAI API account for Whisper transcription and ChatGPT tagging Google Docs API access for document writing A dedicated Google Doc where all notes will be collected Customising this workflow Adjust the AI prompt to use different tagging categories relevant to your workflow (e.g., project names, priorities, emotions) Add multiple Google Docs for different contexts (work vs. private notes) Include additional processing like sentiment analysis or automatic task extraction Connect to other apps like Notion, Obsidian, or your preferred note-taking system And don't forget to also implement the complimentary workflow Weekly AI Review!

Overview This workflow turns photos sent to a LINE bot into tiny AI-generated diary entries and saves everything neatly in Google Drive. Each time a user sends an image, the workflow creates a timestamped photo file and a matching text file with a short diary sentence, stored inside a year/month folder structure (KidsDiary/YYYY/MM). It’s a simple way to keep a lightweight visual diary for kids or daily life without manual typing. LINE Photo to AI Diary with Goo… Who this is for Parents who want to archive kids’ photos with a short daily comment People who often send photos to LINE and want them auto-organized in Drive Anyone who prefers a low-friction, “take a photo and forget” style diary How it works Trigger: A LINE Webhook receives an image message from the user. Extract metadata: The workflow extracts the messageId and replyToken. Download image: It calls the LINE content API to fetch the image as binary. AI diary text: OpenAI Vision generates a one-sentence, diary-style caption (about 50 Japanese characters). Folder structure: A KidsDiary/YYYY/MM folder is created (or reused) in Google Drive. Save files: The photo is saved as YYYY-MM-DD_HHmmss.jpg and the diary text as YYYY-MM-DD_HHmmss_diary.txt in the same folder. Confirm on LINE: The bot replies to the user that the photo and diary have been saved. How to set up Connect your LINE Messaging API credentials in the HTTP Request nodes. Connect your Google Drive credential in the Google Drive nodes and choose a root folder. Make sure the webhook URL is correctly registered in the LINE Developers console. Customization ideas Change the AI prompt to adjust tone (e.g., more playful, more sentimental). Localize the diary language or add an English translation. Add a second branch to post the saved diary entry to Slack, Notion, or email. Organize Google Drive folders by child’s name instead of only by date.

This workflow contains community nodes that are only compatible with the self-hosted version of n8n. Gmail Auto-Reply with AI Automatically draft smart email replies using ChatGPT. Reclaim your time typing the same responses again and again. Who is this for? If you're overwhelmed with emails and constantly repeating yourself in replies, this workflow is for you. Whether you're a freelancer, business owner, or team lead, it saves you time by handling email triage and drafting replies for you. What does it solve? This workflow reads your unread Gmail messages and uses AI to: Decide whether the email needs a response Automatically draft a short, polite reply when appropriate Skip spam, newsletters, or irrelevant emails Save the AI-generated reply as a Gmail draft (you can edit it before sending) It takes email fatigue off your plate and keeps your inbox moving. How it works Trigger on New Email: Watches your Gmail inbox for unread messages. AI Agent Review: Analyzes the content to decide if a reply is needed. OpenAI ChatGPT: Drafts a short, polite reply (under 120 words). Create Gmail Draft: Saves the response as a draft for you to review. Label It: Applies a custom label like Action so you can easily find AI-handled emails. How to set up? Connect credentials: Gmail (OAuth2) OpenAI (API key) Create the Gmail label: In your Gmail, create a label named Action (case-sensitive). How to customize this workflow to your needs Change the AI prompt**: Add company tone, extra context, or different reply rules. Label more intelligently**: Add conditions or labels for “Newsletter,” “Meeting,” etc. Adjust frequency**: Change how often the Gmail Trigger polls your inbox. Add manual review**: Route drafts to a team member before sending.