AI email reply based on HubSpot data + Slack approval

This n8n template drafts customer-ready email replies using Google Gemini, enriched with HubSpot context (contact, deals, companies, tickets). Each draft is routed to Slack for one-click approval before it’s sent from Gmail—so you move fast without losing control. Ideal for support and sales teams that want speedy, personalized responses while keeping humans in the loop. --- How it works Gmail Trigger watches for new inbound emails. Sender filter excludes internal domains (e.g., n8n.io) to avoid auto-replying to teammates. HubSpot contact lookup finds the sender and fetches associated deals/companies/tickets via association + batch read. CRM context is normalized into clean, LLM-friendly fields (no IDs or sensitive noise). Gemini (Google AI Studio) generates a concise, friendly reply using: Sender name, subject, and message snippet Safe, relevant HubSpot context (e.g., top 1–2 deals or an open ticket) Style constraints (≤ \150 words, single CTA, optional clarifying question) Slack approval posts the draft to a channel; if approved, n8n replies via Gmail in the original thread. --- How to use 1. Gmail: Connect the same account for the trigger and reply nodes. 2. HubSpot: Connect OAuth on the search + HTTP request nodes. 3. Gemini: Add your Google AI Studio API key to the Google Gemini Chat Model node. 4. Slack: Connect and select the channel for draft approvals. 5. (Optional) Filter: Adjust the Allowed Sender filter before going live. 6. (Optional) Prompt: Edit “Draft Reply (AI Agent)” tone/length or how much CRM detail to include. 7. Activate the workflow. New emails will produce Slack-approved replies automatically. --- Requirements Gmail (trigger + send) HubSpot (OAuth2) for contact + associations Slack for approval step Google Gemini (Google AI Studio API key) --- Notes & customization Safety rails: The prompt avoids exposing IDs/raw JSON and caps CRM details to what’s useful. Auto-send mode: Skip Slack if you want fully automated replies for specific senders/labels. Richer context: Extend the batch read to pull more properties (e.g., next step, renewal date). Triage: Branch on subject/labels to route billing vs. technical requests to different prompts. QA queue: If the model asks a clarifying question, keep it to one—the node enforces that.