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.
A guide shows Codex summarizing transcripts into a Word doc, building an Excel revenue model and making carousel images from a document.
A guide shows Codex summarizing transcripts into a Word doc, building an Excel revenue model and making carousel images from a document.
The guide switches Codex from coding mode to everyday work mode and points it at a local project folder. In the demos, it read two transcripts and wrote a Word summary. It built an Excel revenue model from a one-line prompt. It turned an existing document into four carousel images, first as HTML slides, then as image files after the user asked for image generation.
The Word summary took about five minutes. The Excel model took 3 minutes 46 seconds, and a follow-up prompt fixed an initial import formatting problem. The carousel images took about five minutes.
Codex burns credits faster than normal chat, so check the Usage tab. The guide suggests medium reasoning for document work and saving high reasoning for code generation.
Read the transcript at [file path] and produce a Word document with a summary, key decisions, and action items by owner. Then build an Excel model from [assumptions or data file] with [revenue drivers] and a short assumptions tab. Stop when both files are saved in [output folder], and tell me anything you had to guess.
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.