Workflow Templates

Run an AI-powered degree audit for each senior student. This template reads student rows from Google Sheets, evaluates completed courses against hard-coded program requirements, and writes back an AI Degree Summary of what's still missing (major core, Gen Eds, major electives, and upper-division credits). It's designed for quick advisor/registrar review and SIS prototypes. Trigger: Manual — When clicking "Execute workflow" Core nodes: Google Sheets, OpenAI Chat Model, (optional) Structured Output Parser Programs included: Computer Science BS, Business Administration BBA, Psychology BA, Mechanical Engineering BS, Biology BS (Pre-Med), English Literature BA, Data Science BS, Nursing BSN, Economics BA, Graphic Design BFA Who's it for Registrars & advisors** who need fast, consistent degree checks Student success teams** building prototype dashboards SIS/EdTech builders** exploring AI-assisted auditing How it works Read seniors from Google Sheets (Senior_data) with: StudentID, Name, Program, Year, CompletedCourses. AI Agent compares CompletedCourses to built-in requirements (per program) and computes Missing items + a short Summary. Write back to the same sheet using "Append or update" by StudentID (updates AI Degree Summary; you can also map the raw Missing array to a column if desired). Example JSON (for one student): { "StudentID": "S001", "Program": "Computer Science BS", "Missing": [ "GEN-REMAIN | General Education credits remaining | 6", "CS-EL-REM | CS Major Electives (200+ level) | 6", "UPPER-DIV | Additional Upper-Division (200+ level) credits needed | 18", "FREE-EL | Free Electives to reach 120 total credits | 54" ], "Summary": "All core CS courses are complete. Still need 6 Gen Ed credits, 6 CS electives, and 66 total credits overall, including 18 upper-division credits — prioritize 200/300-level CS electives." } Setup (2 steps) 1) Connect Google Sheets (OAuth2) In n8n → Credentials → New → Google Sheets (OAuth2) and sign in. In the Google Sheets nodes, select your spreadsheet and the Senior_data tab. Ensure your input sheet has at least: StudentID, Name, Program, Year, CompletedCourses. 2) Connect OpenAI (API Key) In n8n → Credentials → New → OpenAI API, paste your key. In the OpenAI Chat Model node, select that credential and a model (e.g., gpt-4o or gpt-5). Requirements Sheet columns:** StudentID, Name, Program, Year, CompletedCourses CompletedCourses format:** pipe-separated IDs (e.g., GEN-101|GEN-103|CS-101). Program labels:** should match the built-in list (e.g., Computer Science BS). Credits/levels:** Template assumes upper-division ≥ 200-level (adjust the prompt if your policy differs). Customization Change requirements:** Edit the Agent's system message to update totals, core lists, elective credit rules, or level thresholds. Store more output:** Map Missing to a new column (e.g., AI Missing List) or write rows to a separate sheet for dashboards. Distribute results:** Email summaries to advisors/students (Gmail/Outlook), or generate PDFs for advising folders. Add guardrails:** Extend the prompt to enforce residency, capstone, minor/cognate constraints, or per-college Gen Ed variations. Best practices (per n8n guidelines) Sticky notes are mandatory:** Include a yellow sticky note that contains this description and quick setup steps; add neutral sticky notes for per-step tips. Rename nodes clearly:** e.g., "Get Seniors," "Degree Audit Agent," "Update Summary." No hardcoded secrets:** Use credentials—not inline keys in HTTP or Code nodes. Sanitize identifiers:** Don't ship personal spreadsheet IDs or private links in the published version. Use a Set node for config:** Centralize user-tunable values (e.g., column names, tab names). Troubleshooting OpenAI 401/429:** Verify API key/billing; slow concurrency if rate-limited. Empty summaries:** Check column names and that CompletedCourses uses |. Program mismatch:** Align Program labels to those in the prompt (exact naming recommended). Sheets auth errors:** Reconnect Google Sheets OAuth2 and re-select spreadsheet/tab. Limitations Not an official audit:** It infers gaps from the listed completions; registrar rules can be more nuanced. Catalog drift:** Requirements are hard-coded in the prompt—update them each term/year. Upper-division heuristic:** Adjust the level threshold if your institution defines it differently. Tags & category Category: Education / Student Information Systems Tags: degree-audit, registrar, google-sheets, openai, electives, upper-division, graduation-readiness Changelog v1.0.0 — Initial release: Senior_data in/out, 10 programs, AI Degree Summary output, append/update by StudentID. Contact Need help tailoring this to your catalog (e.g., per-college Gen Eds, capstones, minors, PDFs/email)? 📧 rbreen@ynteractive.com 📧 robert@ynteractive.com 🔗 Robert Breen — https://www.linkedin.com/in/robert-breen-29429625/ 🌐 ynteractive.com — https://ynteractive.com

Promo Seeker finds fresh, working promo codes and vouchers on the web so your team never misses a deal. This n8n workflow uses SerpAPI and Decodo Scrapper for real-time search, an agent powered by GPT-5 Mini for filtering and validation, and Chat Memory to keep context—saving time, reducing manual checks, and helping marketing or customer support teams deliver discounts faster to customers (and yes, it's better at hunting promos than your inbox). 💡 Why Use Promo Seeker? Speed: Saves hours per week by automatically finding and validating current promo codes, so you can publish deals faster. Simplicity: Eliminates manual searching across sites, no more copy-paste scavenger hunts. Accuracy: Reduces false positives by cross-checking results and keeping only working vouchers—fewer embarrassed "expired code" moments. Edge: Combine search APIs with an AI agent to surface hard-to-find, recently-live offers—win over competitors who still rely on manual scraping. ⚡ Perfect For Marketing teams: Quickly populate newsletters, landing pages, or ads with valid promos. Customer support: Give verified discount codes to users without ping-ponging between tabs. Deal aggregators & affiliates: Discover fresh vouchers faster and boost conversion rates. 🔧 How It Works ⏱ Trigger: A user message via the chat webhook starts the search (Message node). 📎 Process: The agent queries SerpAPI and Decodo Scrapper to collect potential promo codes and voucher pages. 🤖 Smart Logic: The Promo Seeker Agent uses GPT-5 Mini with Chat Memory to filter for fresh, working promos and to verify validity and relevance. 💌 Output: Results are returned to the chat with clear, copy-ready promo codes and source links. 🗂 Storage: Chat Memory stores context and recent searches so the agent avoids repeating old results and can follow up with improved queries. 🔐 Quick Setup Import JSON file to your n8n instances Add credentials: SerpAPI, Azure OpenAI (Gpt 5 Mini), Decodo API Customize: Search parameters (brands, regions, validity window), agent system message, and result formatting Update: Azure OpenAI endpoint and API key in the Gpt 5 Mini credentials; add your SerpAPI key and Decodo key Test: Run a few queries like "latest Amazon promo" or "food delivery voucher" and confirm returned codes are valid 🧩 You'll Need Active n8n instances SerpAPI account and API key Azure OpenAI (for GPT-5 Mini) with key and endpoint Decodo account/API key 🛠️ Level Up Ideas Push verified promos to a Slack channel or email digest for the team. Add scheduled scans to detect newly expired codes and remove them from lists. Integrate with a CMS to auto-post verified deals to landing pages. Made by: khaisa Studio Tags: promo, vouchers, discounts Category: Marketing Automation Need custom work? Contact Us

Score open-ended AI responses with a judge model. This template shows how to evaluate a customer support agent using a separate LLM that rates each response on correctness and helpfulness, going beyond what exact match scoring can capture. What you'll do Open the workflow and review the production path (chat trigger, AI Agent generates a support response, response returned to the user). Open the Evaluations tab and click Run Test to feed question + expected answer pairs through the AI Agent. Watch the judge model score each response on correctness (1-5) and helpfulness (1-5). Review per-test-case scores in the Evaluations tab alongside token usage and execution time. What you'll learn How LLM-as-a-Judge works and when it beats deterministic scoring How to wire a separate judge model into your evaluation path How to write a custom scoring prompt that returns a numeric score and a justification When to use n8n's built-in Correctness and Helpfulness metrics versus a custom judge Why it matters Customer-facing responses are subjective. A response can be technically accurate but tonally wrong, or polite but useless. LLM-as-a-Judge gives you a measurable signal for the kind of quality that matters but resists simple matching, so you can iterate on prompts with confidence instead of guesswork. This template is a learning companion to the Production AI Playbook, a series that explores strategies, shares best practices, and provides practical examples for building reliable AI systems in n8n.

Route AI-generated content to reviewers on Slack or email, with automatic escalation if nobody responds. This template demonstrates a full multi-channel review workflow with timeout handling. What you'll do Submit content through a Form Trigger with a body and preferred review channel (Slack or email). Watch the AI Agent enhance the content, then route the review request to Slack or Gmail with built-in Approve/Reject buttons. See how unanswered requests automatically escalate to a manager after a configurable timeout. What you'll learn How to use a Form Trigger for structured workflow input How Switch nodes route requests to different review channels based on user selection How Slack and Gmail approval nodes pause workflows and present Approve/Reject buttons to reviewers How configuring a wait time limit on approval nodes enables automatic timeout handling How Switch nodes with three routing rules (approved, rejected, timed out) handle all decision outcomes How to implement timeout-based escalation paths for unresponsive reviewers Why it matters Real teams don't all live in the same tool. Some reviewers check Slack, others check email. This template meets reviewers where they are and ensures nothing falls through the cracks with built-in escalation logic. This template is a learning companion to the Production AI Playbook, a series that explores strategies, shares best practices, and provides practical examples for building reliable AI systems in n8n. Link to blog

Video Introduction Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin Workflow Overview This workflow creates an intelligent AI chatbot that retrieves recipes from an external API through natural conversation. When users ask for recipes, the AI agent automatically determines when to use the recipe lookup tool, fetches real-time data from the API Ninjas Recipe API, and provides helpful, conversational responses. This demonstrates the powerful capability of API-to-API integration within n8n, allowing AI agents to access external data sources on demand. Key Features Intelligent Tool Calling:** The AI agent automatically decides when to use the HTTP Request Tool based on user queries External API Integration:** Connects to API Ninjas Recipe API using Header Authentication for secure access Conversational Memory:** Maintains context across multiple turns for natural dialogue Dynamic Query Generation:** The AI model automatically generates the appropriate search query parameters based on user input Common Use Cases Build AI assistants that need access to real-time external data Create chatbots with specialized knowledge from third-party APIs Demonstrate API-to-API integration patterns for custom automation Prototype AI agents with tool-calling capabilities Setup & Configuration Required Credentials: OpenAI API: Sign up at OpenAI and obtain an API key for the language model. Configure this in n8n's credential manager. API Ninjas: Register at API Ninjas to get your free API key for the Recipe API (supports 400+ calls/day). This API uses Header Authentication with the header name "X-Api-Key". Agent Configuration: The AI Agent includes a system message instructing it to "Always use the recipe tool if i ask you for recipe." This ensures the agent leverages the external API when appropriate. The HTTP Request Tool is configured with the API endpoint (https://api.api-ninjas.com/v1/recipe) and set to accept query parameters automatically from the AI model. The tool description "Use the query parameter to specify the food, and it will return a recipe" helps the AI understand when and how to use it. Language Model: Currently configured to use OpenAI's gpt-5-mini, but you can change this to other compatible models based on your needs and budget. Memory: Uses a window buffer to maintain conversation context, enabling natural multi-turn conversations where users can ask follow-up questions.

Protect your workflows with n8n's native Guardrails node, placed before and after your AI step. The input guardrails catch jailbreak attempts and PII before they reach your model. The output guardrails scan AI responses for NSFW content and secret keys before they reach your users. What you'll do Send clean input and watch it pass through both input and output guardrails successfully. Send a prompt injection attempt and see the input guardrails block it with jailbreak detection. Send a message containing PII and see it get flagged before reaching the AI. See how flagged outputs fall back to a safe templated response. What you'll learn How to use n8n's Guardrails node for input and output validation How jailbreak detection catches prompt injection attempts using an LLM-based check How PII detection identifies personal data (SSNs, credit card numbers, emails, and more) How to build fallback paths that return safe responses when guardrails trigger Why it matters Guardrails are the seatbelts of your AI workflow. You hope you don't need them, but when a user sends a prompt injection attempt or the AI leaks sensitive data, you'll be glad they're there. This template uses n8n's dedicated Guardrails node to make safety checks a first-class part of your workflow without writing custom validation code. This template is a learning companion to the Production AI Playbook, a series that explores strategies, shares best practices, and provides practical examples for building reliable AI systems in n8n. https://go.n8n.io/PAP-D&A-Blog

Automate Financial Operations with O3 CFO & GPT-4.1-mini Finance Team This workflow builds a virtual finance department inside n8n. At the center is a CFO Agent (O3 model) who acts like a strategic leader. When a financial request comes in, the CFO interprets it, decides the strategy, and delegates to the specialist agents (each powered by GPT-4.1-mini for cost efficiency). 🟢 Section 1 – Entry & Leadership Nodes: 💬 When chat message received → Entry point for user financial requests. 💼 CFO Agent (O3) → Acts as the Chief Financial Officer. Interprets the request, decides the approach, and delegates tasks. 💡 Think Tool → Helps the CFO brainstorm and refine financial strategies. 🧠 OpenAI Chat Model CFO (O3) → High-level reasoning engine for strategic leadership. ✅ Beginner view: Think of this as your finance CEO’s desk — requests land here, the CFO figures out what needs to be done, and the right specialists are assigned. 📊 Section 2 – Specialist Finance Agents Each specialist is powered by GPT-4.1-mini (fast + cost-effective). 📈 Financial Planning Analyst → Builds budgets, forecasts, and financial models. 📚 Accounting Specialist → Handles bookkeeping, tax prep, and compliance. 🏦 Treasury & Cash Management Specialist → Manages liquidity, banking, and cash flow. 📊 Financial Analyst → Runs KPI tracking, performance metrics, variance analysis. 💼 Investment & Risk Analyst → Performs investment evaluations, capital allocation, and risk management. 🔍 Internal Audit & Controls Specialist → Checks compliance, internal controls, and audits. ✅ Beginner view: This section is your finance department — every role you’d find in a real company, automated by AI. 📋 Section 3 – Flow of Execution User sends a request (e.g., “Create a financial forecast for Q1 2026”). CFO Agent (O3) interprets it → “We need planning, analysis, and treasury.” Delegates tasks to the relevant specialists. Specialists process in parallel, generating plans, numbers, and insights. CFO Agent compiles and returns a comprehensive financial report. ✅ Beginner view: The CFO is the conductor, and the specialists are the musicians. Together, they produce the financial “symphony.” 📊 Summary Table | Section | Key Roles | Model | Purpose | Beginner Benefit | | ---------------------- | ------------------------------------------------------- | ----------------- | ------------------- | -------------------------------------- | | 🟢 Entry & Leadership | CFO Agent, Think Tool | O3 | Strategic direction | Acts like a real CFO | | 📊 Finance Specialists | FP Analyst, Accounting, Treasury, FA, Investment, Audit | GPT-4.1-mini | Specialized tasks | Each agent = finance department role | | 📋 Execution Flow | All connected | O3 + GPT-4.1-mini | Collaboration | Output = complete financial management | 🌟 Why This Workflow Rocks Full finance department in n8n** Strategic + execution separation** → O3 for CFO, GPT-4.1-mini for team Cost-optimized** → Heavy lifting done by mini models Scalable** → Easily add more finance roles (tax, payroll, compliance, etc.) Practical outputs** → Reports, budgets, risk analyses, audit notes 👉 Example Use Case: “Generate a Q1 financial forecast with cash flow analysis and risk report.” CFO reviews request. Financial Planning Analyst → Budget + Forecast. Treasury Specialist → Cash flow modeling. Investment Analyst → Risk review. Audit Specialist → Compliance check. CFO delivers a packaged financial report back to you.

[Workflow Overview] ⚠️ Self-Hosted Only: This workflow uses the gotoHuman community node and requires a self-hosted n8n instance. Who's It For Content teams, bloggers, news websites, and marketing agencies who want to automate content creation from RSS feeds while maintaining editorial quality control. Perfect for anyone who needs to transform news articles into detailed blog posts at scale. What It Does This workflow automatically converts RSS feed articles into comprehensive, SEO-optimized blog posts using AI. It fetches articles from your RSS source, generates detailed content with GPT-4, sends drafts for human review via gotoHuman, and publishes approved articles to Google Docs with automatic Slack notifications to your team. How It Works Schedule Trigger runs every 6 hours to check for new RSS articles RSS Read node fetches the latest articles from your feed Format RSS Data extracts key information (title, keywords, description) Generate Article with AI creates a structured blog post using OpenAI GPT-4 Structure Article Data formats the content with metadata Request Human Review sends the article for approval via gotoHuman Check Approval Status routes the workflow based on review decision Create Google Doc and Add Article Content publish approved articles Send Slack Notification alerts your team with article details Requirements OpenAI API key** with GPT-4 access Google account** for Google Docs integration gotoHuman account** for human-in-the-loop approval workflow Slack workspace** for team notifications RSS feed URL** from your preferred source How to Set Up Configure RSS Feed: In the "RSS Read" node, replace the example URL with your RSS feed source Connect OpenAI: Add your OpenAI API credentials to the "OpenAI Chat Model" node Set Up Google Docs: Connect your Google account and optionally specify a folder ID for organized storage Configure gotoHuman: Add your gotoHuman credentials and create a review template for article approval Connect Slack: Authenticate with Slack and select the channel for notifications Customize Content: Modify the AI prompt in "Generate Article with AI" to match your brand voice and article structure Adjust Schedule: Change the trigger frequency in "Schedule Trigger" based on your content needs How to Customize Article Style**: Edit the AI prompt to change tone, length, or structure Keywords & SEO**: Modify the "Format RSS Data" node to adjust keyword extraction logic Publishing Destination**: Change from Google Docs to other platforms (WordPress, Notion, etc.) Approval Workflow**: Customize the gotoHuman template to include specific review criteria Notification Format**: Adjust the Slack message template to include additional metadata Processing Volume**: Modify the Code node to process multiple RSS articles instead of just one

Description Save hours of manual reporting with this end-to-end automation. This workflow pulls campaign performance data (demo or live), generates a clear AI-powered executive summary, and compiles everything into a polished weekly report. The report is formatted in Markdown, automatically stored in Google Docs, and instantly shared with your team via Slack — no spreadsheets, no copy-paste, no delays. What it does ⏰ Runs on a schedule (e.g. every Monday morning) 📊 Collects performance metrics (Google Ads, Meta, TikTok, YouTube – demo data included) 🤖 Uses AI to summarize wins, issues, and recommendations 📝 Builds a structured Markdown report (totals, channel performance, top campaigns) 📄 Creates and updates a Google Doc with the report 💬 Notifies your team in Slack with topline numbers + direct report link 📧 Optionally email the report to stakeholders or clients Why it’s valuable Saves time** – no manual data aggregation Standardizes reporting** – same format and quality every week Adds insights** – AI highlights what matters most Improves transparency** – instant access via Docs, Slack, or Email Scales easily** – adapt to multiple clients or campaigns Professional delivery** – branded, polished reports on autopilot 💡 Extra recommendation: Connect to a Google Docs template to give your reports a professional, branded look.

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.

Financial services lead magnet with Lovable/Base44 and n8n Automatically route and nurture leads for Business Funding, Life Insurance, Credit Repair, and Agent Recruitment with hyper-personalized AI-generated emails. How it works 1. Lead Capture via Webhook Your landing page submits lead data (name, email, phone, interest, service-specific details) to the workflow webhook. 2. Intelligent Routing by Interest Switch node routes leads to the appropriate path based on their selected service interest. 3. AI Email Generation OpenAI generates hyper-personalized HTML emails using the lead's specific data - calculating funding amounts, insurance premiums, credit repair timelines, or recruitment benefits. 4. Automated Email Delivery Gmail sends the customized email with dynamic subject lines and professional templates. 5. Lead Tracking Google Sheets logs all lead data for follow-up and analytics. Set up steps Time to set up: 15-20 minutes 1. Configure Credentials (5 min) Add your OpenAI API key (all AI nodes use the same credential) Connect Gmail OAuth2 for sending emails Connect Google Sheets OAuth2 for lead tracking 2. Create Your Lead Tracking Sheet (2 min) Create a Google Sheet with columns: Name, Interest, Phone, Email, Details/Additional Data Copy the Sheet ID and update all Google Sheets nodes 3. Set Up Webhook (3 min) Activate the workflow to generate your webhook URL Copy the webhook URL for your landing page integration 4. Customize Your Brand (5 min) Replace all "Your Name" placeholders with your name Update "Your Company" and "Your Title" references Add your actual service links (funding, insurance, credit repair, recruitment) Adjust brand colors in HTML templates if desired 5. Test the Workflow (5 min) Use the pinned test data in the Webhook node to validate each path Send test emails to yourself Verify data appears correctly in Google Sheets Detailed setup instructions are included in the sticky notes throughout the workflow. Building Your Landing Page Frontend This workflow needs a landing page to collect leads. Here's how to build one quickly: Option 1: Build with Lovable (Recommended - Fastest) Lovable Prompt: Create a modern lead magnet landing page for a financial services company with the following: LAYOUT: Hero section with headline "Unlock Your Financial Future" and subheadline about multiple services Service selection cards for: Business Funding, Life Insurance, Credit Repair, Become an Agent Lead capture form that appears when user selects a service Professional color scheme: Green (#2EAE4E) primary, Cream (#F5F5DC) secondary, Gold (#FFD700) accents FORM FUNCTIONALITY: Universal fields: First Name, Last Name, Email, Phone Service-specific conditional fields that appear based on selection: Business Funding: Business Length (dropdown: <1 year, 1-2 years, 2-5 years, 5+ years) Monthly Revenue (dropdown: Under $10k, $10k-$50k, $50k-$100k, $100k+) Credit Score (dropdown: Below 600, 600-650, 650-700, 700+) Funding Purpose (text input) Life Insurance: Age Range (dropdown: 18-30, 31-40, 41-50, 51-60, 60+) Has Dependents (radio: Yes/No) Health Status (dropdown: Excellent, Good, Fair) Coverage Needed (dropdown: $250k-$500k, $500k-$1M, $1M+) State (dropdown: all US states) Credit Repair: Current Credit Score (dropdown: Below 500, 500-580, 580-650, 650-700, 700+) Credit Goals (dropdown: Buy a home, Get a loan, Better rates, Start a business) Negative Items (text input) Become an Agent: Has Experience (radio: Yes/No) Is Licensed (radio: Yes/No) Interests (checkboxes: Flexible schedule, Passive income, Helping others, Building team) Start Time (dropdown: Immediately, 1-2 weeks, 1 month, Just exploring) FORM SUBMISSION: On submit, POST form data as JSON to webhook URL: [YOUR_WEBHOOK_URL_HERE] JSON structure: { "firstName": "string", "lastName": "string", "email": "string", "phone": "string", "interest": "string", "additionalData": { // service-specific fields as object }, "timestamp": "ISO date string", "source": "leadmagnet_page" } Show success message after submission Reset form for next lead Add subtle animations and hover effects Mobile responsive design Include trust badges/testimonials section Add privacy policy notice STYLING: Modern, professional design Gradient buttons with hover effects Card-based layout for services Smooth transitions between form states Loading state during submission After Lovable generates it: Replace [YOUR_WEBHOOK_URL_HERE] with your actual n8n webhook URL Customize branding, copy, and testimonials Deploy to Netlify/Vercel (Lovable can do this automatically) Option 2: Build with Base44 (v0.dev) Base44/v0 Prompt: Create a conversion-optimized lead capture landing page for financial services with: STRUCTURE: Single-page application with smooth scrolling Hero section with value proposition 4 service option cards (Business Funding, Life Insurance, Credit Repair, Agent Recruitment) Dynamic form that shows service-specific fields based on card selection Social proof section with stats/testimonials Footer with company info FORM BEHAVIOR: When user clicks a service card: Highlight selected card Scroll smoothly to form section Show universal fields (name, email, phone) Show conditional fields based on service: Business Funding → creditScore, monthlyRevenue, businessLength, fundingPurpose Life Insurance → ageRange, hasDependents, healthStatus, coverageNeeded, state Credit Repair → currentScore, creditGoals, negativeItems Agent Recruitment → hasExperience, isLicensed, interests (multi-select), startTime TECHNICAL REQUIREMENTS: React components with TypeScript Form validation with error messages Submit to webhook endpoint via fetch API with POST JSON payload structure: { firstName, lastName, email, phone, interest, additionalData: { ...service-specific fields }, timestamp: new Date().toISOString(), source: "leadmagnet_page" } Loading spinner during submission Success confirmation modal Error handling with user-friendly messages DESIGN: Color palette: Primary green #2EAE4E, Cream #F5F5DC, Gold accents #FFD700 Tailwind CSS styling Responsive breakpoints for mobile/tablet/desktop Accessible form inputs with proper labels Smooth animations using Framer Motion Professional sans-serif font (Inter or similar) COMPONENTS TO CREATE: ServiceCard: clickable cards with icon, title, description DynamicForm: form that renders fields based on selected service SuccessModal: confirmation after submission TestimonialsSection: social proof carousel HeroSection: headline, subheadline, CTA Make it modern, trustworthy, and conversion-focused. Option 3: Use Existing Form Builders If you prefer no-code tools: Typeform: Create multi-step form with conditional logic Use Typeform webhook to send data to n8n Map Typeform fields to expected JSON structure Webflow: Build custom landing page Use Webflow Forms with custom code to POST to webhook Add JavaScript to format data correctly Carrd: Simple single-page landing Use custom form with JavaScript submission Lightweight and fast-loading What You Get ✅ 4 Complete Service Paths: Business Funding (dynamic loan amount calculations) Life Insurance (age/health-based premium estimates) Credit Repair (realistic timeline projections) Agent Recruitment (experience-matched messaging) ✅ AI-Powered Personalization: Uses prospect's specific data points Calculates service-specific metrics First-person conversational tone Professional HTML email templates ✅ Complete Lead Management: Automated email delivery Google Sheets tracking Service-specific data capture Timestamp logging ✅ Production-Ready Prompts: Detailed AI instructions for each service Dynamic calculations built-in Brand voice guidelines HTML template structure Perfect For Financial advisors and planners Insurance agents and brokers Business funding consultants Credit repair services MLM/Network marketing in financial services Lead generation agencies serving finance clients Customization Ideas 🎨 Branding: Update colors in HTML templates to match your brand Add your logo to email headers Customize email signatures 📊 Integrations: Add SMS notifications (Twilio node) Connect to your CRM (HubSpot, Salesforce) Set up Slack notifications for high-value leads Add calendar booking links 🔄 Follow-up Sequences: Add delay nodes for drip campaigns Create nurture paths for non-responders Set up reminder emails 📈 Analytics: Connect to Google Analytics Track conversion rates per service Monitor email open/click rates Set up performance dashboards Technical Requirements n8n instance (cloud or self-hosted) OpenAI API key (GPT-4o-mini recommended for cost efficiency) Gmail account with OAuth2 enabled Google Sheets access Landing page with webhook integration Cost Estimate Per 100 leads: OpenAI API: ~$0.50-$1.00 (GPT-4o-mini) Gmail: Free (within limits) Google Sheets: Free n8n: Depends on hosting (cloud ~$20/mo, self-hosted variable) Total: Roughly $1-2 per 100 leads for AI generation Support & Documentation 📚 Included Documentation: 10+ detailed sticky notes throughout workflow Setup checklist Data structure specifications Troubleshooting guide Customization tips 🎥 Recommended Resources: n8n webhook documentation OpenAI API best practices Gmail API quotas and limits Author David Olusola Version History v1.0 - Initial release 4 service paths with AI personalization Complete email automation Google Sheets integration Production-ready prompts Comprehensive documentation License & Usage Free to use and modify for your business. ⭐ If this template helps you, please: Leave a review Share with others in financial services Tag me in your success stories Questions? Find me on the n8n community forum or LinkedIn.

Video Introduction Want to automate your inbox or need a custom workflow? 📞 Book a Call | 💬 DM me on Linkedin What This Workflow Does This AI-powered email assistant automatically processes incoming Gmail messages, categorizes them, adds appropriate labels, and drafts intelligent responses based on your company's knowledge base. When a new email arrives, the AI agent analyzes the content, determines the category (such as "existing_order" or "quote_request"), applies the correct Gmail label for organization, and drafts a contextually appropriate response. You receive a Telegram notification with a direct link to review and send the draft with one click. Key Features Automatic Email Categorization**: The AI analyzes incoming emails and assigns them to predefined categories Smart Response Drafting**: Generates detailed, personalized responses using your company's knowledge base Gmail Label Management**: Automatically applies appropriate labels to keep your inbox organized Instant Notifications**: Get Telegram alerts with direct links to review drafts Knowledge Base Integration**: Uses n8n Data Tables to store company information, pricing, policies, and common responses Common Use Cases Customer support teams handling quotation requests Sales teams managing order inquiries Small businesses automating routine email responses Anyone wanting to save hours on email management each week Setup and Configuration Required Accounts: Gmail account with OAuth2 connection OpenAI API account for the chat model Telegram bot for notifications Initial Setup: Connect your Gmail account to both the Gmail Trigger and Gmail tool nodes Create a Data Table called "Customer Support Knowledge Base" containing your business information, response style guide, product details, pricing, delivery policies, and FAQs Configure your Telegram bot credentials and update the chat ID in the Telegram node Set up your OpenAI API credentials Customization: Knowledge Base**: Populate the Data Table with your specific company information - this is what the AI uses to draft accurate responses AI Prompt**: Adjust the categorization rules and response criteria in the AI Agent node to match your business needs Polling Frequency**: The Gmail Trigger checks for new emails every minute by default; modify this in the trigger settings if needed The workflow uses the "Simplify" option turned off in the Gmail Trigger to ensure the AI receives the full email text for processing.