by Yusei Miyakoshi
Who’s it for Teams that start their day in Slack and want a concise, automated summary of yesterday’s emails—ops leads, PMs, founders, and anyone handling busy inboxes without writing code. What it does / How it works Runs every morning at 08:00 (cron 0 0 8 * * ), fetches all emails received *yesterday, and routes the result: if none were found, it posts a polite “no emails” notice; if emails exist, it aggregates them and asks an AI agent to produce a structured digest, then formats and posts to your chosen Slack channel. The flow uses **Gmail → If → Aggregate (Item Lists) → AI Agent (OpenRouter model with structured output) → Code (Slack formatter) → Slack. A set of sticky notes on the canvas explains each step and required inputs. How to set up Connect Gmail (OAuth2) and keep the default date window (yesterday → today at 00:00). Connect Slack (OAuth2) and select your target channel. Add OpenRouter credentials and pick a compact model (e.g., gpt-4o-mini). Keep the provided structured-output schema and formatter code. Adjust the schedule/timezone if needed (the fallback message includes an Asia/Tokyo example). Paste this description into the yellow sticky note at the top of the canvas. Requirements Gmail & Slack accounts with appropriate scopes OpenRouter API key stored in Credentials (no hard-coded keys) n8n Cloud or self-host with LangChain agent nodes enabled How to customize the workflow Narrow Gmail results with label/search filters (e.g., from:, subject:). Change the digest sections or tone in the AI Agent system prompt. Swap the model for cost/quality needs and tweak temperature/max tokens. Localize dates/timezones in the formatter code and Slack messages. Branch the output to email, Google Docs, or Sheets for archival. Security & publishing tips Rename all nodes clearly, do not hardcode API keys, remove real channel IDs/emails before sharing, and group end-user variables in a Set (Fields) node. Keep the sticky notes—they’re mandatory for reviewers.
by Abdul Mir
Overview Automate your entire LinkedIn content machine — from research and image generation to scheduling and posting — with this AI-powered workflow. This workflow pulls in past content ideas, researches new ones using Perplexity, generates a new post (with image) using your brand's voice and style, saves the output to Google Sheets, and auto-posts twice a week to LinkedIn. It’s perfect for founders, creators, and marketers who want to stay consistent on LinkedIn without manually writing or designing every post. Who’s it for Solo founders or marketers building a LinkedIn presence Content creators growing their audience Agencies managing client content calendars Anyone who wants to post consistently without spending hours on content How it works Pulls old ideas from a Google Sheet Schedules content creation using n8n’s cron node Uses Perplexity to research current topics and trends Feeds the data into an AI agent (like Claude or GPT) to generate post copy Creates a branded image using a reference style and OpenAI’s image model Saves post content + image URL into Google Sheets Twice a week, selects one ready post, downloads the image, and publishes it to LinkedIn How to set up Add your Google Sheet ID and column names for posts Connect your OpenAI (or Claude) and Perplexity API keys Upload a brand-style reference image to Google Drive Configure your LinkedIn account and connect the node Adjust the cron schedule for both post creation and auto-posting (Optional) Edit the AI prompt to match your personal voice or niche Requirements Google Drive & Sheets access OpenAI or Claude API key Perplexity API key LinkedIn credentials (via n8n’s LinkedIn integration) How to customize Change the prompt for the AI to fit your voice or audience Swap out Perplexity for another research method Adjust how often you want posts scheduled or published Swap LinkedIn for Twitter, Slack, or another platform Add Notion or Airtable as your CMS backend
by Cheng Siong Chin
How It Works This workflow automates end-to-end real estate investment analysis by aggregating data from multiple sources and applying AI-driven evaluation. It is designed for real estate investors, analysts, and portfolio managers seeking data-backed decisions without manual research overhead. The solution addresses the time-consuming challenge of collecting and analyzing fragmented real estate data—such as MLS listings, public records, demographic trends, and macroeconomic indicators—and transforms it into actionable insights using AI. Data is collected in parallel across four streams: MLS property data, public records, demographic information, and macroeconomic signals. These streams are consolidated into a unified dataset and processed by OpenAI GPT-4, using calculator tools and structured output parsing for quantitative analysis. Setup Steps Configure HTTP nodes with your MLS API, public records service Add OpenAI API key in Chat Model node credentials Connect Gmail account for acquisition team notifications Integrate Slack workspace and specify investor notification channel Set schedule trigger frequency in Schedule node for desired analysis cadence Prerequisites OpenAI API key, MLS data service access, public records API credentials Use Cases Real estate investment firms screening multiple markets simultaneously Customization Modify AI prompts to adjust investment criteria priorities, add custom financial metrics Benefits Reduces investment analysis time from hours to minutes, eliminates manual data aggregation errors
by Luka Zivkovic
Description Who's it for This workflow is designed for developers, entrepreneurs, and startup enthusiasts who want personalized, AI-driven startup idea generation and analysis. Perfect for solo developers seeking side project inspiration, startup accelerators evaluating concepts, or anyone looking to validate business ideas with professional-grade analysis. How it works The workflow uses a three-stage Claude AI agent pipeline to create comprehensive startup analyses. The first agent generates innovative startup ideas based on your technical skills and preferences. The second agent acts as a venture capitalist, critically analyzing market viability, competition, and execution challenges. The third agent performs sentiment analysis and synthesizes a final recommendation with actionable next steps. How to set up Configure Anthropic API credentials for all three Claude AI model nodes Set up Gmail OAuth2 for email delivery Fill out the "My Information" node with your developer profile Update the recipient email address in the Gmail node Test with the manual trigger before enabling daily automation Requirements n8n account Anthropic API account for Claude AI access Gmail account with OAuth2 configured Basic understanding of developer skills and market preferences How to customize the workflow Modify the AI agent prompts to focus on specific industries or business models. Adjust temperature settings for different creativity levels. Add database storage to track idea history. Configure the form trigger for team-wide idea generation or integrate with Slack for automated sharing. Got a good idea? Visit my site https://techpoweredgrowth.com to get help getting to the next level Or reach out to luka.zivkovic@techpoweredgrowth.com
by Trung Tran
📄 Auto Extract Contacts from Business Cards to Sheet With GPT4o > This smart workflow extracts names, phone numbers, emails, and more from uploaded name card photos using AI, then logs them neatly into your Google Sheet. No typing. No mess. Just upload and go. 👤 Who’s it for Sales & Business Development Teams Recruiters & Talent Acquisition Specialists Event Teams collecting business cards Admins who manage contact databases manually ⚙️ How it works / What it does This workflow automates the extraction of contact details from uploaded name card (business card) images and stores them in a structured Google Sheet for easy tracking and follow-up. Workflow Steps: User uploads one or more name card images through a web form. The uploaded files are saved to a Google Drive folder for archiving. A smart AI agent (with OCR and GPT capabilities) scans each image and extracts relevant contact data into structured JSON format. Data is transformed, cleaned (e.g., removing + from phone numbers), and filtered. Valid contacts are appended to a Google Sheet for central tracking and future use. 🛠 How to set up Create a Form Allow file upload (JPG/PNG format). Label it as “Name Card Uploader” with a clear description. Upload to Google Drive Use the Google Drive node to store uploaded images. Configure Smart Agent Use GPT-4o or similar model with OCR capability. Apply a structured output parser to extract contact fields like name, phone, email, company, etc. Transform Data Use the Code node to clean and structure contact info. Strip out unwanted characters from phone numbers (e.g., +). Filter Invalid Records Remove entries with no meaningful contact data. Append to Google Sheets Use the Google Sheets node with "Append Sheet Row". Map fields to columns like Name, Phone, Email, etc. ✅ Requirements n8n workflow environment Google Drive integration (for file storage) Google Sheets integration (for storing contacts) GPT-4o or any image-capable LLM Clear name card images (PNG/JPG, readable text) (Optional) Slack/email integration for notifications 🧩 How to customize the workflow CRM Sync**: Connect to platforms like HubSpot, Salesforce, or Zoho. Validation Logic**: Ensure records contain key fields like name or email before writing. Uploader Info**: Attach submitter metadata to each contact record. Language Adaptation**: Adjust extracted field labels/output to target your preferred language. Batch Upload**: Handle multiple cards in a single image or multiple uploads in one go.
by Kirill Khatkevich
This workflow transforms raw Meta Ads data into actionable, expert-level insights. It acts as a virtual performance marketer, analyzing each creative's performance, comparing it against your historical benchmarks, and delivering clear recommendations on whether to scale, optimize, or stop the ad. By running parallel analyses with both OpenAI and Gemini, it provides a unique, dual-perspective evaluation. This template is the perfect sequel to our "Automation of Creative Testing" workflow but also works powerfully on its own. Use Case Manually sifting through ads manager reports is tedious, and identifying true winners from early data is challenging. This workflow solves these problems by automating the entire analysis pipeline. It's designed for performance marketing teams who need to: Make faster, data-driven decisions on which creatives to scale. Get objective, AI-powered second opinions on ad performance. Systematically evaluate creatives against consistent, pre-defined benchmarks. Maintain a central log in Google Sheets with both raw metrics and qualitative AI analysis. Save hours spent on manual data crunching and report generation. How it Works The workflow is structured into three logical stages: Configuration & Data Ingestion: A central ⚙️ Set parameters node holds all key variables: the data source (Meta or Sheets), campaign_id, and, most importantly, your historical performance benchmarks as a simple text block. An IF node directs the workflow to fetch data either directly from a Meta Ads campaign or from a specified Google Sheet (ideal for analyzing a curated list of ads). Data Processing & AI Analysis (Parallel Execution): After fetching raw performance data (spend, impressions, clicks, actions), the workflow splits into three parallel branches for maximum resilience: Branch 1 (Data Logging): Immediately writes or updates a row in Google Sheets with the raw metrics for the creative. This ensures no data is lost, even if the AI analysis fails. Branch 2 (OpenAI Analysis): Prepares a CSV string of the creative's data, sends it along with the benchmarks to an OpenAI model (e.g., GPT-4), and instructs it to return a structured JSON analysis. Branch 3 (Gemini Analysis): Performs the exact same process but using Google's Gemini model via a LangChain agent, providing a second, independent evaluation. Results Aggregation: The results from both AI models are received as structured JSON. Two final Google Sheets nodes take these results and update the original row (matching by AdID), adding the evaluation, significance, summary, and recommendation into separate columns. The final sheet contains a complete picture: raw data side-by-side with analyses from two different AIs. Setup Instructions Credentials: 1.1 Connect your Meta Ads account. 1.2 Connect your Google account (for Sheets). 1.3 Connect your OpenAI account. 1.4 Connect your Google Gemini (Palm) account. The ⚙️ Set parameters Node: This is the central control panel. Open this first Set node and customize it: source: Set to "Meta" to pull from a campaign or "sheets" to read from a Google Sheet. campaign_id: If source is "Meta", enter your Meta Campaign ID here. benchmarks_data: This is critical. Paste your own historical performance data here as a CSV-formatted text block. The template includes an example. For best results, use an export from Ads Manager of your top-performing creatives, including key metrics. Google Sheets Nodes: There are three Google Sheets nodes that write data. You need to configure all of them to point to the same spreadsheet and sheet. Ad metrics (for raw metrics): Select your spreadsheet and sheet. Ensure "Operation" is set to Append or Update. Ad data from OpenAI (for OpenAI results): Select the same spreadsheet/sheet. Set "Operation" to Update. Ad data from Gemini (for Gemini results): Select the same spreadsheet/sheet. Set "Operation" to Update. Make sure your sheet has columns for all the data fields, e.g., AdID, FileName, spend, impressions, evaluation, summary, recommendation, evaluation G, summary G, etc. Activate the Workflow: Set your desired frequency in the Schedule Trigger node. Save and activate the workflow. Further Ideas & Customization This powerful analysis engine can be extended even further: Add a "Decision" Node: After the AI analyses are logged, add a final step that compares their recommendations. If both AIs say "scale", automatically increase the ad's budget via the Meta Ads API. Create Summary Reports: Add a branch that, after all ads are processed, calculates an overall summary (e.g., "3 creatives recommended for scaling, 5 for stopping") and sends it to a Slack channel. Dynamic Benchmarks: Instead of pasting benchmarks into the Set node, create a step that reads them from a dedicated "Benchmarks" tab in your Google Sheet, making them even easier to update. Experiment with Prompts and Benchmarks: The quality of the AI analysis is highly dependent on the quality of your input. Don't be afraid to: -- Refine the prompts in the AI Agent and Message a model nodes to better match your specific business context and KPIs. -- Curate your benchmarks_data. Test different sets of benchmark data (e.g., "last 30 days top performers" vs. "all-time best") to see how it influences the AI's recommendations. Finding the right combination of prompt and data is key to unlocking the most effective insights.
by Arthur Dimeglio
What this workflow does Automatically: fetches fresh news, filters out aggregators/PR wires and duplicates, writes a human-sounding LinkedIn post with GPT, downloads the article image to verify it’s usable, publishes to LinkedIn (with or without media), and logs the posted titles in Firestore to avoid re-posting. Runs on a daily schedule (cron) and supports two post variants: • Case 1: article has a description → richer post • Case 2: no description → short, still human and casual ⸻ How it works (high level flow) • Schedule Trigger (0 10,12,19,21 * * *): runs at 10:00, 12:00, 19:00, 21:00 (server timezone). • Firestore (Get Previous News Titles): loads previously posted titles (document asma/x20) to de-dupe. • HTTP Request (API NEWS): calls newsapi.org with query “AI Startup” for example, last 24–48h window, searchIn=title, sorted by publishedAt. • Code: Select Articles: • excludes Biztoc and common aggregators/PR wires (Techmeme, TheFly, PRNewswire, GlobeNewswire, MarketWatch press-releases, Medium, Substack, Yahoo consent, etc.), • requires valid URL + image, • groups by topic (normalized title + domain) and picks the best representative, • sorts by recency and returns up to 10 unique articles. • IF (URL & De-dupe checks): ensures link present and not already posted (compares against Firestore titles). • IF (Description Checker): branches on presence of description. • LLM Agents (2 prompts): generate a casual, human LinkedIn post in English (no emojis/links/markdown, 2–3 hashtags). • Post setup: cleans the text, passes the image URL forward. • HTTP Request (Image Downloader): retrieves the image as a file to confirm the link works. • LinkedIn Publisher: • If image OK → posts with media. • Otherwise → posts text-only. • Time Checkers + Firestore Upserts: after a successful publish, writes the article’s title to Firestore (asma/x20 fields title10, title12, title19, title21) so it won’t be posted again at other times. ⸻ Credentials & prerequisites • NewsAPI.org: API key (free tier works to start; mind rate limits). • LinkedIn OAuth2: connected account with permission to create posts on your profile (uses “Person” target in the node). • Google Firebase (Firestore): Service Account with read/write to the asma collection. The workflow uses document ID x20. ⸻ Setup (5 minutes) Import the workflow and open it in n8n. In API NEWS, set your NewsAPI key in the query param apiKey. In Get Previous News Titles and Firebase Article Saver [1–8], attach your Google Service Account and confirm projectId, collection=asma. In LinkedIn Publisher [1–4], attach your LinkedIn OAuth credential and ensure the Person is your profile URN. (Optional) Adjust the cron in Hourly trigger (server timezone). (Optional) Change the search query (q=AI startup), language, or time window in API NEWS. Enable the workflow. ⸻ Customization tips • Search scope: edit q, language, from/to in API NEWS to cover your niche. • Aggregator policy: tweak the aggregatorDomains set in the Select Articles code node. • Post voice: modify the LLM prompt (keeps the “human, slightly messy” tone). • Hashtags: the prompt ends with 2–3 simple tags (#AI #Startups #Innovation) — change as you like. • Posting times: change the cron or the downstream time-checker logic to map specific titles → time slots. • No-image fallback: text-only path is already handled; replace with a placeholder image if you prefer always-with-media. ⸻ Notes & constraints • Timezone: Schedule Trigger uses the n8n server timezone; adjust if your LinkedIn audience is elsewhere. • De-dupe: this template stores last posted titles in one Firestore doc (asma/x20) under title10, title12, title19, title21. You can change the schema or keep a rolling history. • Filtering: items missing URL or image are skipped by design. Yahoo consent pages are also skipped. • LLM costs: posts are short; usage is modest, but keep an eye on your OpenAI billing. • NewsAPI limits: free plans throttle requests; consider caching or widening the time window if you hit limits. ⸻ Troubleshooting • Nothing posts: check NewsAPI quota/response, then see the URL checker and Description Checker branches. • Image errors: some sites block hotlinking; the image download step will fall back to text-only posting. • Duplicates appeared: verify Firestore upserts executed after posting and that your comparison uses the right fields. • Wrong hours: confirm your n8n instance timezone and the cron expression. ⸻ Why this template You get a robust “news → LinkedIn” autoposter that feels authentically human (no corporate vibes), avoids low-quality aggregators, prevents duplicates, and gracefully handles media — all with clean, modular nodes that are easy to tweak.
by Muhammad Farooq Iqbal
Google NanoBanana Model Image Editor for Consistent AI Influencer Creation with Kie AI Image Generation & Enhancement Workflow This n8n template demonstrates how to use Kie.ai's powerful image generation models to create and enhance images using AI, with automated story creation, image upscaling, and organized file management through Google Drive and Sheets. Use cases include: AI-powered content creation for social media, automated story visualization with consistent characters, marketing material generation, and high-quality image enhancement workflows. Good to know The workflow uses Kie.ai's google/nano-banana-edit model for image generation and nano-banana-upscale for 4x image enhancement Images are automatically organized in Google Drive with timestamped folders Progress is tracked in Google Sheets with status updates throughout the process The workflow includes face enhancement during upscaling for better portrait results All generated content is automatically saved and organized for easy access How it works Project Setup: Creates a timestamped folder structure in Google Drive and initializes a Google Sheet for tracking Story Generation: Uses OpenAI GPT-4 to create detailed prompts for image generation based on predefined templates Image Creation: Sends the AI-generated prompt along with 5 reference images to Kie.ai's nano-banana-edit model Status Monitoring: Polls the Kie.ai API to monitor task completion with automatic retry logic Image Enhancement: Upscales the generated image 4x using nano-banana-upscale with face enhancement File Management: Downloads, uploads, and organizes all generated content in the appropriate Google Drive folders Progress Tracking: Updates Google Sheets with status information and image URLs throughout the entire process Key Features Automated Story Creation**: AI-powered prompt generation for consistent, cinematic image creation Multi-Stage Processing**: Image generation followed by intelligent upscaling Smart Organization**: Automatic folder creation with timestamps and file management Progress Tracking**: Real-time status updates in Google Sheets Error Handling**: Built-in retry logic and failure state management Face Enhancement**: Specialized enhancement for portrait images during upscaling How to use Manual Trigger: The workflow starts with a manual trigger (easily replaceable with webhooks, forms, or scheduled triggers) Automatic Processing: Once triggered, the entire pipeline runs automatically Monitor Progress: Check the Google Sheet for real-time status updates Access Results: Find your generated and enhanced images in the organized Google Drive folders Requirements Kie.ai Account**: For AI image generation and upscaling services OpenAI API**: For intelligent prompt generation (GPT-4 mini) Google Drive**: For file storage and organization Google Sheets**: For progress tracking and status monitoring Customizing this workflow This workflow is highly adaptable for various use cases: Content Creation**: Modify prompts for different styles (fashion, product photography, architectural visualization) Batch Processing**: Add loops to process multiple prompts or reference images Social Media**: Integrate with social platforms for automatic posting E-commerce**: Adapt for product visualization and marketing materials Storytelling**: Create sequential images for visual narratives or storyboards The modular design makes it easy to add additional processing steps, change AI models, or integrate with other services as needed. Workflow Components Folder Management**: Dynamic folder creation with timestamp naming AI Integration**: OpenAI for prompts, Kie.ai for image processing File Processing**: Binary handling, URL management, and format conversion Status Tracking**: Multi-stage progress monitoring with Google Sheets Error Handling**: Comprehensive retry and failure management systems
by Arunava
This workflow finds fresh Reddit posts that match your keywords, decides if they’re actually relevant to your brand, writes a short human-style reply using AI, posts it, and logs everything to Baserow. 💡Perfect for Lead gen without spam: drop helpful replies where your audience hangs out. Get discovered by AI surfaces (AI Overviews / SGE, AISEO/GSEO) via high-quality brand mentions. Customer support in the wild: answer troubleshooting threads fast. Community presence: steady, non-salesy contributions in niche subreddits. 🧠 What it does Searches Reddit for your keyword query on a schedule (e.g., every 30 min) Checks Baserow first so you don’t reply twice to the same post Uses an AI prompt tuned for short, no-fluff, subreddit-friendly comments Softly mentions your brand only when it’s clearly relevant Posts the comment via Reddit’s API Saves post_id, comment_id, reply, permalink, status to Baserow Processes posts one-by-one with an optional short wait to be nice to Reddit ⚡ Requirements Reddit developer API Baserow account, table, and API token AI provider API (OpenAI / Anthropic / Gemini) ⚙️ Setup Instructions Create Baserow table Fields (user-field names exactly): post_id (unique), permalink, subreddit, title, created_utc, reply (long text), replied (boolean), created_on (datetime). Add credentials in n8n Reddit OAuth2* (scopes: read, submit, identity) and set a proper *User-Agent** string (Reddit requires it). LLM**: Google Gemini and/or Anthropic (both can be added; one can be fallback in the AI Agent). Baserow**: API token. Set the Schedule Trigger (Cron) Start hourly (or every 2–3h). Pacing is mainly enforced by the Wait nodes. Update “Check duplicate row” (HTTP Request) URL**: https://api.baserow.io/api/database/rows/table/{TABLE_ID}/?user_field_names=true&filter__post_id__equal={{$json.post_id}} Header**: Authorization: Token YOUR_BASEROW_TOKEN (Use your own Baserow domain if self-hosted.) Configure “Filter Replied Posts” Ensure it skips items where your Baserow record shows replied === true (so you don’t comment twice). Configure “Fetch Posts from Reddit” Set your keyword/search query (and time/sort). Keep User-Agent header present. Configure “Write Reddit Comment (AI)” Update your brand name** (and optional link). Edit the prompt/tone** to your voice; ensure it outputs a short reply field (≤80 words, helpful, non-salesy). Configure “Post Reddit Comment” (HTTP Request) Endpoint: POST https://oauth.reddit.com/api/comment Body: thing_id: "t3_{{$json.post_id}}", text: "{{$json.reply}}" Uses your Reddit OAuth credential and User-Agent header. Update user_agent value in header by your username n8n:reddit-autoreply:1.0 (by /u/{reddit-username}) Store Comment Data on Baserow (HTTP Request) POST https://api.baserow.io/api/database/rows/table/{TABLE_ID}/?user_field_names=true Header: Authorization: Token YOUR_BASEROW_TOKEN Map: post_id, permalink, subreddit, title, created_utc, reply, replied, created_on={{$now}}. Keep default pacing Leave Wait 5m (cool-off) and Wait 6h (global pace) → \~4 comments/day. Reduce waits gradually as account health allows. Test & enable Run once manually, verify a Baserow row and one test comment, then enable the schedule. 🤝 Need a hand? I’m happy to help you get this running smoothly—or tailor it to your brand. Reach out to me via email: imarunavadas@gmail.com
by Devon Toh
Screen and Score Investment Deals with AI using OpenAI, Gmail, and Telegram Automatically screens incoming deal submissions using AI, scores them against investment criteria, and routes to the right action. Who is this for? VC firms, PE funds, angel investors, or M&A advisors who receive deal flow via email or form submissions. What problem does this solve? Manually reviewing every pitch deck and deal memo is time-consuming. Most deals don't meet investment criteria. This agent screens, scores, and prioritizes deals so your team focuses on the best opportunities. How it works: New Email Received / Deal Submission Webhook - captures deals from email or form Normalize Email/Webhook Data - standardizes fields from either source Build Deal Text - combines email body + attachment info into screening text Has Deal Content? - validates there is enough content to screen Extract Deal Info - OpenAI - AI extracts company, industry, revenue, ask, team, highlights, red flags Score Deal - OpenAI - AI scores on 5 criteria (industry fit, revenue, growth, team, clarity) Is PASS? / Is REVIEW? - routes by verdict (PASS/REVIEW/REJECT) Telegram Alerts - notifies with deal summary and scores Log Deal to Pipeline Sheet - tracks all deals in a pipeline spreadsheet Setup: Add credentials: Gmail, OpenAI, Telegram Bot, Google Sheets Replace YOUR_TELEGRAM_CHAT_ID with your chat ID Create a Google Sheet with columns: received_at, company_name, industry, stage, revenue, ask_amount, overall_score, verdict, one_line_summary, recommendation, key_highlights, red_flags, sender_name, sender_email, source, industry_fit, revenue_stage, growth_trajectory, team_strength, deal_clarity Replace YOUR_GOOGLE_SHEET_ID with your sheet ID Customization: Edit the scoring criteria in the Score Deal OpenAI prompt Adjust score thresholds for PASS/REVIEW/REJECT Add Slack notifications instead of Telegram Add auto-decline email for REJECT deals Connect to a CRM instead of Google Sheets
by Jitesh Dugar
Automatically qualify inbound demo requests, scrape prospect websites, and send AI-personalized outreach emails—all on autopilot. What This Workflow Does This end-to-end lead automation workflow helps SaaS companies qualify and nurture inbound leads with zero manual work until human approval. Key Features ✅ Smart Email Filtering - Automatically flags personal emails (Gmail, Yahoo, etc.) and routes them to a polite regret message ✅ Website Intelligence - Scrapes prospect websites and extracts business context ✅ AI Analysis - Uses OpenAI to score ICP fit, identify pain points, and find personalization opportunities ✅ Personalized Outreach - AI drafts custom emails referencing specific details from their website ✅ Human-in-the-Loop - Approval gate before sending to ensure quality control ✅ Professional Branding - Even rejected leads get a thoughtful response Perfect For B2B SaaS companies with inbound lead forms Sales teams drowning in demo requests Businesses wanting to personalize at scale Anyone needing intelligent lead qualification What You'll Need Jotform account (or any form tool with webhooks) Create your form for free on Jotform using this link OpenAI API key Gmail account (or any email service) n8n instance (cloud or self-hosted) Workflow Sections 📧 Lead Intake & Qualification - Capture form submissions and filter personal emails 🕷️ Website Scraping - Extract company information from their domain ❌ Regret Flow - Send polite rejection to unqualified leads 🤖 AI Analysis - Analyze prospects and draft personalized emails 📨 Approved Outreach - Human review + send welcome email Customization Tips: Update the AI prompt with your company's ICP and value proposition Modify the personal email provider list based on your market Adjust the regret email template to match your brand voice Add Slack notifications for high-value leads Connect your CRM to log all activities Time Saved: ~15-20 minutes per lead Lead Response: Under 5 minutes (vs hours/days manually)
by DevCode Journey
Who is this for? This workflow is designed for business founders, CMOs, marketing teams, and landing page designers who want to automatically analyze their landing pages and get personalized, unconventional, high-impact conversion rate optimization (CRO) recommendations. It works by scraping the landing page content, then leveraging multiple AI models to roast the page and generate creative CRO ideas tailored specifically for that page. What this Workflow Does / Key Features Captures a landing page URL through a user-friendly form trigger. Scrapes the landing page HTML content using an HTTP request node. Sends the scraped content to a LangChain AI Agent, which orchestrates various AI models (OpenAI, Google Gemini, Mistral, etc.) for deep analysis. The AI Agent produces a friendly, fun, and unconventional “roast” of the landing page, explaining what’s wrong in human tone. Generates 10 detailed, personalized, easy-to-implement, and 2024-relevant CRO recommendations with a “wow” factor. Delivers the analysis and recommendations via Telegram message, Gmail email, and WhatsApp (via Rapiwa). Utilizes multiple AI tools and search APIs to enhance the quality and creativity of the output. Requirements OpenAI API credentials configured in n8n. Google Gemini (PaLM) API credentials for LangChain integration. Mistral Cloud API credentials for text extraction. Telegram bot credentials for sending messages. Gmail OAuth2 credentials for email delivery. Rapiwa API credentials for WhatsApp notifications. Running n8n instance with nodes: Form Trigger, HTTP Request, LangChain AI Agent, Telegram, Gmail, and custom Rapiwa node. How to Use — step-by-step Setup 1) Credentials Add your OpenAI API key under n8n credentials (OpenAi account 2). Add Google Gemini API key (Google Gemini (PaLM) Api account). Add Mistral Cloud API key (Mistral Cloud account). Set up Telegram Bot credentials (Telegram account). Set up Gmail OAuth2 credentials (Gmail account). Add Rapiwa API key for WhatsApp messages (Rapiwa). 2) Configure the Form Trigger Customize the form title, description, and landing page URL input placeholder if desired. 3) Customize Delivery Nodes Modify the Telegram, Gmail, and Rapiwa nodes with your desired recipient info and messaging preferences. 4) Run the Workflow Open the form URL webhook and submit the landing page URL to get a detailed AI-powered CRO roast and recommendations sent directly to your communication channels. Important Notes The AI Agent prompt is designed to create a fun and unconventional roast to engage users emotionally. Avoid generic advice. All CRO recommendations are personalized and contextual based on the scraped content of the provided landing page. Ensure all API credentials are kept secure and not hard-coded. Use n8n credentials management. Adjust the delivery nodes to match your preferred communication channels and recipients. The workflow supports expansion with additional AI models or messaging platforms as needed. 🙋 For Help & Community 👾 Discord: n8n channel 🌐 Website: devcodejourney.com 🔗 LinkedIn: Connect with Shakil 📱 WhatsApp Channel: Join Now 💬 Direct Chat: Message Now