Quick overview This workflow collects a landing page URL, scrapes its HTML, and uses OpenAI to generate a structured conversion-rate-optimization (CRO) audit with scores and prioritized tips, then renders a styled report in the browser and logs each audit to an n8n Data Table. How it works Presents an n8n Form where the user submits a landing page URL. Fetches the landing page HTML via an HTTP request with retries, and routes to an error page if the URL can’t be retrieved. Sends the scraped HTML and URL to OpenAI to produce a CRO audit including an overall score, category scores, a written roast, and a ranked list of optimization tips. Uses a structured output parser with an OpenAI “fixer” model to validate and auto-correct the AI response into the required JSON schema. Builds a styled HTML report from the structured audit results. Logs the audit metadata (URL, score, verdict, summary, language, timestamp) to an n8n Data Table and displays the report back in the user’s browser. Setup Add an OpenAI API credential and select it for both the main OpenAI model and the output-fixer model.
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OpenAI Chat Model
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Verify Japanese invoice registration numbers from Gmail using Gemini, NTA API, Sheets and Slack
Quick Overview This workflow monitors Gmail for unread invoice emails with PDF attachments, extracts invoice details with Google Gemini, verifies Japanese qualified invoice registration numbers against the National Tax Agency (NTA) Invoice Web-API, logs results to Google Sheets, and sends Slack alerts for invalid, missing, or inconclusive checks. How it works Polls Gmail hourly for unread emails matching “has:attachment invoice” and downloads the first attachment. Extracts text from the attached invoice PDF. Uses Google Gemini to parse the invoice text into structured fields such as the T-registration number, issuer name, invoice date, and amounts. Normalizes and validates the registration number to ensure it matches the required “T + 13 digits” format. If the format is valid, calls the NTA Invoice Web-API to verify the registration number and interprets the response as valid, invalid, or not found. Appends the invoice details and verification status to a Google Sheets worksheet. Routes the outcome by sending the appropriate Slack alert for invalid/not found/inconclusive results or marking the invoice email as read when the number is valid. Setup Connect your Gmail OAuth2 credentia
BForce AI to use a specific output format
This workflow is for anyone looking to automatically fetch, validate, and parse complex language-based queries into a structured format. Its unique capability lies in not only processing language but also fixing invalid outputs before structuring them. Note that to use this template, you need to be on n8n version 1.19.4 or later.
Route urgent Gmail support emails and WhatsApp digests with Groq and Supabase
Quick overview This workflow monitors a Gmail inbox, uses Groq LLM classification to label each support email as urgent, routine, or spam, logs the result to Supabase, sends immediate WhatsApp alerts for urgent messages, and delivers a daily WhatsApp digest summarizing routine emails. How it works Triggers every minute when a new email arrives in Gmail. Uses Groq (via an LLM prompt) to classify the email as urgent, routine, or spam and generate a one-sentence summary and reason. Inserts the email metadata, category, and summary into a Supabase table for tracking. Sends an immediate WhatsApp message to the support owner when the email is classified as urgent and updates the Supabase record to reflect it was notified. Runs on a daily schedule to fetch all routine emails in Supabase that have not yet been included in a digest. Aggregates those routine emails into a single WhatsApp digest message, sends it to the support owner, and marks the included Supabase records as digested. Setup Connect Gmail OAuth2 credentials and ensure the trigger watches the support inbox/labels you want monitored. Add Groq API credentials for the chat model used to classify and summarize emails. Add Supabas