Answer patient enquiries for a clinic by chat
An n8n and Gemini agent receives patient chat messages, keeps context, uses clinic info from Google Sheets and responds to enquiries.
An AI agent read email subjects, sorted them into 6 folders and wrote 12 one-line draft replies in 40 minutes.
An AI agent read email subjects, sorted them into 6 folders and wrote 12 one-line draft replies in 40 minutes.
The user gave an AI agent read access to subject lines of 1,847 unread emails. The agent grouped them into folders, produced one-line draft replies, and flagged urgent items. The user answered the flagged emails himself and sent the drafts.
After 40 minutes the inbox held 6 folders and 12 drafts, with the three urgent emails identified and the rest sorted.
Processing took 40 minutes.
Go through my unread email in [Gmail/Outlook], reading only subjects and senders to start, and sort messages into these folders: [folder names, e.g. Needs reply, Receipts, Newsletters, FYI, Waiting on others, Archive]. For anything in Needs reply, write a one-line draft reply and save it as a draft without sending. You're finished when every unread message is filed and I have a list of the drafts to review. Before you delete anything or send any message, stop and show me what you're about to do.
An n8n and Gemini agent receives patient chat messages, keeps context, uses clinic info from Google Sheets and responds to enquiries.
An n8n workflow has AI classify web-form jobs by type, urgency and summary, create an Airtable record, reply instantly and flag uncertain cases.
Lindy agents summarized meeting notes, sent action items to teammates, suggested knowledge-base replies on support tickets, and handled follow-up emails and calendar updates.