Quick overview This workflow answers incoming WhatsApp customer questions using an OpenAI-powered agent that reads your live website via HTTP requests, escalates unclear or sensitive cases to your support team via Gmail, and logs each conversation to Google Sheets. How it works Triggers when a new WhatsApp message is received. Loads configuration values (company name, website root URL, AI model, language rules, escalation email, and WhatsApp phone number ID) and extracts the customer phone number and message text. Ignores non-text WhatsApp updates (such as images, audio, and status messages). Uses OpenAI with conversation memory to navigate your website by listing links and fetching relevant pages, then generates a structured result containing an answer, escalation flag, sentiment, topic, and source URLs. If the agent marks the request as needing a human, sends an escalation email via Gmail to your support address with the customer details and the drafted reply. Sends the final plain-text reply back to the customer on WhatsApp. Appends the conversation details to a Google Sheets log.
Tags
Gmail
Google Sheets
OpenAI Chat ModelSSimple Memory
Structured Output ParserRelated workflows
See all AI Automation→AI: Summarize podcast episode and enhance using Wikipedia
The workflow automates the process of creating a summarized and enriched podcast digest, which is then sent via email. Note that to use this template, you need to be on n8n version 1.19.4 or later.
SSlack chatbot powered by AI
This workflow offers an effective way to handle a chatbot's functionality, making use of multiple tools for information retrieval, conversation context storage, and message sending. It's a setup tailored for a Slack environment, aiming to offer an interactive, AI-driven chatbot experience. Note that to use this template, you need to be on n8n version 1.19.4 or later.
Recommend supplier consolidation with Google Sheets, Groq, Slack and Gmail
Quick overview This workflow runs weekly to read supplier spend data from Google Sheets, detect overlapping suppliers across departments, estimate potential consolidation savings, and use Groq-hosted LLM analysis to assess risk and produce recommendations that are sent to Slack and emailed via Gmail. How it works Runs every week on a scheduled trigger. Reads supplier records from a Google Sheets spreadsheet and keeps key fields like supplier name, department, spend, risk rating, and stakeholder email. Groups and normalizes supplier names to find suppliers used by multiple departments and calculates estimated savings and an opportunity level. Sends each consolidation opportunity to a Groq chat model to evaluate risk versus savings and return a JSON decision and recommendation. Parses the AI JSON output, merges it with the calculated savings data, and formats a final recommendation record. Posts the recommendation to a Slack channel and emails a formatted report to the supplier stakeholder email address. Setup Connect your Google Sheets OAuth account and update the spreadsheet ID and sheet/tab to match your supplier records. Add a Groq API credential for the chat model used to evalua