Build a private Llama chatbot with Ollama, Groq, Slack and Google Sheets

This workflow builds a fully private, self-hosted AI chatbot using Meta Llama models. Unlike cloud-based AI APIs, every conversation stays on your infrastructure — no data leaves your environment. The chatbot remembers conversation history per session, routes different query types to specialized Llama prompts, logs all interactions, and can escalate unresolved queries to a human agent via Slack.

Powered by Ollama (local) or Groq/Together AI (cloud Llama endpoints) — configurable in one node.

What's the Goal? To give businesses a production-grade private AI chatbot that: Runs on their own servers with zero data exposure Handles customer support, internal helpdesk, sales FAQs, and onboarding Remembers context across a full conversation session Routes intelligently: support vs sales vs general vs escalation Logs every turn for quality review, training, and compliance

Why Does It Matter? Most businesses cannot send sensitive conversations to OpenAI or Anthropic due to: GDPR, HIPAA, SOC2, or internal data governance policies Confidential customer data in support queries Proprietary internal knowledge that must stay private

Llama models run fully on-premise. This workflow gives those businesses the same quality AI chatbot experience with complete data sovereignty.

Monetization: sell this as a private AI chatbot deployment package to enterprises. Setup fee plus monthly hosting — recurring revenue.

How It Works

Stage A — Message Intake Webhook receives incoming chat message with session ID and user message text. Set node stores Llama endpoint config and normalizes the payload.

Stage B — Session Memory Code node loads conversation history for the session from an in-memory store. Appends the new user message to build the full context window for Llama.

Stage C — Intent Router IF node checks the message for keywords to classify intent: support issue, sales inquiry, general question, or escalation request. Routes to the matching Llama system prompt branch.

Stage D — Llama Inference HTTP Request calls the Llama API (Ollama local, Groq, or Together AI). Sends full conversation history plus the matched system prompt. Returns the assistant reply.

Stage E — Response Handling Code node parses the Llama output, updates the session memory, checks if escalation is needed, and formats the final response.

Stage F — Logging and Delivery Google Sheets logs every turn. Slack fires only when escalation is flagged. Webhook responds with the chatbot reply and session metadata.

Configuration Requirements LLAMA_ENDPOINT — Your Ollama URL (http://localhost:11434) or Groq/Together AI base URL LLAMA_API_KEY — API key if using Groq or Together AI (leave blank for local Ollama) LLAMA_MODEL — Model name e.g. llama3, llama3.1:8b, llama3.1:70b, mixtral SLACK_WEBHOOK_URL — For human escalation alerts GOOGLE_SHEET_ID — Conversation audit log

Setup Guide Option A (Local / Private): Install Ollama: curl -fsSL https://ollama.ai/install.sh | sh Pull model: ollama pull llama3.1 Set LLAMA_ENDPOINT to http://localhost:11434 Leave LLAMA_API_KEY blank

Option B (Cloud Llama via Groq — fastest): Sign up at groq.com and copy your API key Set LLAMA_ENDPOINT to https://api.groq.com/openai/v1 Set LLAMA_MODEL to llama-3.1-8b-instant or llama-3.1-70b-versatile Paste your Groq API key in LLAMA_API_KEY

Option C (Together AI): Sign up at together.ai Set endpoint to https://api.together.xyz/v1 Set model to meta-llama/Llama-3.1-8B-Instruct-Turbo

Steps for all options: Open Set Llama Config node — fill in all values Set SLACK_WEBHOOK_URL and GOOGLE_SHEET_ID Activate and POST to /webhook/llama-chat

Sample Payload { sessionId: user-abc-123, message: My order arrived damaged and I need a refund, userId: user_123, botPersona: support, userName: Sarah }

Explore More Automation: Contact us to design AI-powered lead nurturing, content engagement, and multi-platform reply workflows tailored to your growth strategy.