HumationHuman + Automation
Back to Catalog

Customer support WhatsApp bot with Google Docs knowledge base and Gemini AI

Tharwat MohamedTharwat Mohamed
82518 views
2/3/2026
ExportAppwriteDatabaseCSVPagination
Official Page

  • Document-Aware WhatsApp AI Bot for Customer Support

  • Google Docs-Powered WhatsApp Support Agent

  • 24/7 WhatsApp AI Assistant with Live Knowledge from Google Docs

📝Description Template

Smart WhatsApp AI Assistant Using Google Docs

Help customers instantly on WhatsApp using a smart AI assistant that reads your company’s internal knowledge from a Google Doc in real time. Built for clubs, restaurants, agencies, or any business where clients ask questions based on a policy, FAQ, or services document.

⚙️ How it works

  • Users send free-form questions to your WhatsApp Business number (e.g. “What are the gym rules?” or “Are you open today?”)

  • The bot automatically reads your company’s internal Google Doc (policy, schedule, etc.)

  • It merges the document content with today’s date and the user’s question to craft a custom AI prompt

  • The AI (Gemini or ChatGPT) then replies back on WhatsApp using natural, helpful language

  • All conversations are logged to Google Sheets for reporting or audit

> 💡Bonus: The AI even understands dates inside the document and compares them to today’s date — e.g. if your document says “Closed May 25 for 30 days,” it will say “We're currently closed until June 24.

🧰 Set up steps

  1. Connect your WhatsApp Cloud API account (Meta)

  2. Add your Google account and grant access to the Doc containing your company info

  3. Choose your AI model (ChatGPT/OpenAI or Gemini)

  4. Paste your document ID into the Google Docs node

  5. Connect your WhatsApp webhook to Meta (only takes 5 minutes)

  6. Done — start receiving and answering customer questions!

> 📄 Works best with free-tier OpenAI/Gemini, Google Docs, and Meta's Cloud API (no phone required). Everything is modular, extensible, and low-code.

🔄 Customization Tips

  • Change the Google Doc anytime to update answers — no retraining needed

  • Add your logo and business name in the AI agent’s “System Prompt”

  • Add fallback routes like “Escalate to human” if the bot can't help

  • Clone for multiple brands by duplicating the workflow and swapping in new docs

🤝 Need Help Setting It Up?

If you'd like help connecting your WhatsApp Business API, setting up Google Docs access, or customizing this AI assistant for your business or clients…

📩 I offer setup, branding, and customization services:

WhatsApp Cloud API setup & verification

Google OAuth & Doc structure guidance

AI model configuration (OpenAI / Gemini)

Branding & prompt tone customization

Logging, reporting, and escalation logic

Just send a message via:

Email: tharwat.elsayed2000@gmail.com

WhatsApp: +20 106 180 3236

WhatsApp Customer Support Bot with Google Docs Knowledge Base and Gemini AI

This n8n workflow automates a customer support system using WhatsApp, a Google Docs-based knowledge base, and Google Gemini AI. It allows businesses to provide instant, AI-powered responses to customer queries received via WhatsApp, leveraging existing documentation for accurate information.

What it does

This workflow streamlines customer support by:

  1. Listening for Incoming WhatsApp Messages: It triggers whenever a new message is received on a configured WhatsApp Business Cloud account.
  2. Initializing AI Agent with Conversation History: It sets up an AI Agent (powered by Google Gemini) and provides it with the recent conversation history to maintain context.
  3. Accessing Google Docs Knowledge Base: The AI Agent is configured with a tool that allows it to search and retrieve information from a specified Google Docs document. This acts as the knowledge base for answering customer questions.
  4. Generating AI Responses: The AI Agent processes the incoming WhatsApp message, uses the Google Docs knowledge base if needed, and generates a relevant, helpful response using the Google Gemini chat model.
  5. Sending WhatsApp Replies: The generated AI response is then sent back to the customer via WhatsApp.
  6. Logging Interactions (Optional/Placeholder): Includes a "Sticky Note" which can be used for logging or adding comments within the workflow, indicating a potential point for further integration like logging to a database or spreadsheet.
  7. Conditional Logic (Optional/Placeholder): Includes an "If" node and a "Date & Time" node, suggesting the possibility of implementing time-based logic or conditional routing for messages (e.g., during business hours vs. after hours), though not fully configured in the provided JSON.
  8. Data Transformation (Optional/Placeholder): Includes "Code" and "AI Transform" nodes, indicating potential for custom data manipulation or further AI-driven text processing before or after the main AI interaction.
  9. Google Sheets Integration (Optional/Placeholder): Includes a "Google Sheets" node, suggesting potential for logging messages, user data, or AI responses to a spreadsheet for analytics or record-keeping.

Prerequisites/Requirements

To use this workflow, you will need:

  • n8n Instance: A running instance of n8n.
  • WhatsApp Business Cloud Account: Configured with a phone number and API access.
  • Google Cloud Project: With the Google Gemini API enabled.
  • Google Docs Document: Containing your knowledge base information.
  • n8n Credentials: Configured for WhatsApp Business Cloud, Google Gemini, Google Docs, and Google Sheets (if utilizing all placeholder nodes).

Setup/Usage

  1. Import the Workflow: Download the provided JSON and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your WhatsApp Business Cloud credential for both the "WhatsApp Trigger" and "WhatsApp Business Cloud" nodes.
    • Set up your Google Gemini credential for the "Google Gemini Chat Model" node.
    • Set up your Google Docs credential for the "Google Docs" node (if using it as a knowledge base).
    • Set up your Google Sheets credential for the "Google Sheets" node (if using it for logging).
  3. Configure Google Docs Knowledge Base: In the "AI Agent" node, ensure the Google Docs tool is correctly configured to point to your specific knowledge base document.
  4. Activate the Workflow: Once all credentials and configurations are set, activate the workflow.

The bot will now listen for incoming WhatsApp messages and respond using the AI Agent powered by your Google Docs knowledge base and Google Gemini.

Related Templates

Generate song lyrics and music from text prompts using OpenAI and Fal.ai Minimax

Spark your creativity instantly in any chat—turn a simple prompt like "heartbreak ballad" into original, full-length lyrics and a professional AI-generated music track, all without leaving your conversation. 📋 What This Template Does This chat-triggered workflow harnesses AI to generate detailed, genre-matched song lyrics (at least 600 characters) from user messages, then queues them for music synthesis via Fal.ai's minimax-music model. It polls asynchronously until the track is ready, delivering lyrics and audio URL back in chat. Crafts original, structured lyrics with verses, choruses, and bridges using OpenAI Submits to Fal.ai for melody, instrumentation, and vocals aligned to the style Handles long-running generations with smart looping and status checks Returns complete song package (lyrics + audio link) for seamless sharing 🔧 Prerequisites n8n account (self-hosted or cloud with chat integration enabled) OpenAI account with API access for GPT models Fal.ai account for AI music generation 🔑 Required Credentials OpenAI API Setup Go to platform.openai.com → API keys (sidebar) Click "Create new secret key" → Name it (e.g., "n8n Songwriter") Copy the key and add to n8n as "OpenAI API" credential type Test by sending a simple chat completion request Fal.ai HTTP Header Auth Setup Sign up at fal.ai → Dashboard → API Keys Generate a new API key → Copy it In n8n, create "HTTP Header Auth" credential: Name="Fal.ai", Header Name="Authorization", Header Value="Key [Your API Key]" Test with a simple GET to their queue endpoint (e.g., /status) ⚙️ Configuration Steps Import the workflow JSON into your n8n instance Assign OpenAI API credentials to the "OpenAI Chat Model" node Assign Fal.ai HTTP Header Auth to the "Generate Music Track", "Check Generation Status", and "Fetch Final Result" nodes Activate the workflow—chat trigger will appear in your n8n chat interface Test by messaging: "Create an upbeat pop song about road trips" 🎯 Use Cases Content Creators: YouTubers generating custom jingles for videos on the fly, streamlining production from idea to audio export Educators: Music teachers using chat prompts to create era-specific folk tunes for classroom discussions, fostering interactive learning Gift Personalization: Friends crafting anniversary R&B tracks from shared memories via quick chats, delivering emotional audio surprises Artist Brainstorming: Songwriters prototyping hip-hop beats in real-time during sessions, accelerating collaboration and iteration ⚠️ Troubleshooting Invalid JSON from AI Agent: Ensure the system prompt stresses valid JSON; test the agent standalone with a sample query Music Generation Fails (401/403): Verify Fal.ai API key has minimax-music access; check usage quotas in dashboard Status Polling Loops Indefinitely: Bump wait time to 45-60s for complex tracks; inspect fal.ai queue logs for bottlenecks Lyrics Under 600 Characters: Tweak agent prompt to enforce fuller structures like [V1][C][V2][B][C]; verify output length in executions

Daniel NkenchoBy Daniel Nkencho
601

Auto-reply & create Linear tickets from Gmail with GPT-5, gotoHuman & human review

This workflow automatically classifies every new email from your linked mailbox, drafts a personalized reply, and creates Linear tickets for bugs or feature requests. It uses a human-in-the-loop with gotoHuman and continuously improves itself by learning from approved examples. How it works The workflow triggers on every new email from your linked mailbox. Self-learning Email Classifier: an AI model categorizes the email into defined categories (e.g., Bug Report, Feature Request, Sales Opportunity, etc.). It fetches previously approved classification examples from gotoHuman to refine decisions. Self-learning Email Writer: the AI drafts a reply to the email. It learns over time by using previously approved replies from gotoHuman, with per-classification context to tailor tone and style (e.g., different style for sales vs. bug reports). Human Review in gotoHuman: review the classification and the drafted reply. Drafts can be edited or retried. Approved values are used to train the self-learning agents. Send approved Reply: the approved response is sent as a reply to the email thread. Create ticket: if the classification is Bug or Feature Request, a ticket is created by another AI agent in Linear. Human Review in gotoHuman: How to set up Most importantly, install the gotoHuman node before importing this template! (Just add the node to a blank canvas before importing) Set up credentials for gotoHuman, OpenAI, your email provider (e.g. Gmail), and Linear. In gotoHuman, select and create the pre-built review template "Support email agent" or import the ID: 6fzuCJlFYJtlu9mGYcVT. Select this template in the gotoHuman node. In the "gotoHuman: Fetch approved examples" http nodes you need to add your formId. It is the ID of the review template that you just created/imported in gotoHuman. Requirements gotoHuman (human supervision, memory for self-learning) OpenAI (classification, drafting) Gmail or your preferred email provider (for email trigger+replies) Linear (ticketing) How to customize Expand or refine the categories used by the classifier. Update the prompt to reflect your own taxonomy. Filter fetched training data from gotoHuman by reviewer so the writer adapts to their personalized tone and preferences. Add more context to the AI email writer (calendar events, FAQs, product docs) to improve reply quality.

gotoHumanBy gotoHuman
353

Ai website scraper & company intelligence

AI Website Scraper & Company Intelligence Description This workflow automates the process of transforming any website URL into a structured, intelligent company profile. It's triggered by a form, allowing a user to submit a website and choose between a "basic" or "deep" scrape. The workflow extracts key information (mission, services, contacts, SEO keywords), stores it in a structured Supabase database, and archives a full JSON backup to Google Drive. It also features a secondary AI agent that automatically finds and saves competitors for each company, building a rich, interconnected database of company intelligence. --- Quick Implementation Steps Import the Workflow: Import the provided JSON file into your n8n instance. Install Custom Community Node: You must install the community node from: https://www.npmjs.com/package/n8n-nodes-crawl-and-scrape FIRECRAWL N8N Documentation https://docs.firecrawl.dev/developer-guides/workflow-automation/n8n Install Additional Nodes: n8n-nodes-crawl-and-scrape and n8n-nodes-mcp fire crawl mcp . Set up Credentials: Create credentials in n8n for FIRE CRAWL API,Supabase, Mistral AI, and Google Drive. Configure API Key (CRITICAL): Open the Web Search tool node. Go to Parameters → Headers and replace the hardcoded Tavily AI API key with your own. Configure Supabase Nodes: Assign your Supabase credential to all Supabase nodes. Ensure table names (e.g., companies, competitors) match your schema. Configure Google Drive Nodes: Assign your Google Drive credential to the Google Drive2 and save to Google Drive1 nodes. Select the correct Folder ID. Activate Workflow: Turn on the workflow and open the Webhook URL in the “On form submission” node to access the form. --- What It Does Form Trigger Captures user input: “Website URL” and “Scraping Type” (basic or deep). Scraping Router A Switch node routes the flow: Deep Scraping → AI-based MCP Firecrawler agent. Basic Scraping → Crawlee node. Deep Scraping (Firecrawl AI Agent) Uses Firecrawl and Tavily Web Search. Extracts a detailed JSON profile: mission, services, contacts, SEO keywords, etc. Basic Scraping (Crawlee) Uses Crawl and Scrape node to collect raw text. A Mistral-based AI extractor structures the data into JSON. Data Storage Stores structured data in Supabase tables (companies, company_basicprofiles). Archives a full JSON backup to Google Drive. Automated Competitor Analysis Runs after a deep scrape. Uses Tavily web search to find competitors (e.g., from Crunchbase). Saves competitor data to Supabase, linked by company_id. --- Who's It For Sales & Marketing Teams: Enrich leads with deep company info. Market Researchers: Build structured, searchable company databases. B2B Data Providers: Automate company intelligence collection. Developers: Use as a base for RAG or enrichment pipelines. --- Requirements n8n instance (self-hosted or cloud) Supabase Account: With tables like companies, competitors, social_links, etc. Mistral AI API Key Google Drive Credentials Tavily AI API Key (Optional) Custom Nodes: n8n-nodes-crawl-and-scrape --- How It Works Flow Summary Form Trigger: Captures “Website URL” and “Scraping Type”. Switch Node: deep → MCP Firecrawler (AI Agent). basic → Crawl and Scrape node. Scraping & Extraction: Deep path: Firecrawler → JSON structure. Basic path: Crawlee → Mistral extractor → JSON. Storage: Save JSON to Supabase. Archive in Google Drive. Competitor Analysis (Deep Only): Finds competitors via Tavily. Saves to Supabase competitors table. End: Finishes with a No Operation node. --- How To Set Up Import workflow JSON. Install community nodes (especially n8n-nodes-crawl-and-scrape from npm). Configure credentials (Supabase, Mistral AI, Google Drive). Add your Tavily API key. Connect Supabase and Drive nodes properly. Fix disconnected “basic” path if needed. Activate workflow. Test via the webhook form URL. --- How To Customize Change LLMs: Swap Mistral for OpenAI or Claude. Edit Scraper Prompts: Modify system prompts in AI agent nodes. Change Extraction Schema: Update JSON Schema in extractor nodes. Fix Relational Tables: Add Items node before Supabase inserts for arrays (social links, keywords). Enhance Automation: Add email/slack notifications, or replace form trigger with a Google Sheets trigger. --- Add-ons Automated Trigger: Run on new sheet rows. Notifications: Email or Slack alerts after completion. RAG Integration: Use the Supabase database as a chatbot knowledge source. --- Use Case Examples Sales Lead Enrichment: Instantly get company + competitor data from a URL. Market Research: Collect and compare companies in a niche. B2B Database Creation: Build a proprietary company dataset. --- WORKFLOW IMAGE --- Troubleshooting Guide | Issue | Possible Cause | Solution | |-------|----------------|-----------| | Form Trigger 404 | Workflow not active | Activate the workflow | | Web Search Tool fails | Missing Tavily API key | Replace the placeholder key | | FIRECRAWLER / find competitor fails | Missing MCP node | Install n8n-nodes-mcp | | Basic scrape does nothing | Switch node path disconnected | Reconnect “basic” output | | Supabase node error | Wrong table/column names | Match schema exactly | --- Need Help or More Workflows? Want to customize this workflow for your business or integrate it with your existing tools? Our team at Digital Biz Tech can tailor it precisely to your use case from automation logic to AI-powered enhancements. Contact: shilpa.raju@digitalbiz.tech For more such offerings, visit us: https://www.digitalbiz.tech ---

DIGITAL BIZ TECHBy DIGITAL BIZ TECH
923