Work & Operations
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.
n8n · Gemini
Reconcile industry files and chase clarifications with agents
An agent workflow ingests a complex industry file, loads it into a database, spawns agents to check it against work in flight, and contacts people to resolve bad data.
Time saved
Not reported
Setup time
Not reported
Running cost
Not reported
Cost range
Not reported
Use case overview
An agent workflow ingests a complex industry file, loads it into a database, spawns agents to check it against work in flight, and contacts people to resolve bad data.
Reported experience
What they did
The author, building for a specific industry they don't name, has a $4/1M-token model ingest a file normally used in a complicated legacy program. It parses the data into a database, then spawns agents to check it against work in flight. Further agents contact the people involved to clarify bad data, and others keep reconciling against many items as updates arrive.
How it went
The author says it replaces a $500+/hr service and catches mistakes in real time across languages and cultures. No measured results are given.
Worth knowing
Expect patience: the author says it takes running tests and tweaking until it works, and their main ongoing job is maintaining the tests.
Example prompts
I receive a [file type, e.g. industry data file] from [source] every [frequency]. Write a workflow that loads it into a local [SQLite or Postgres] database, then checks each record against [my in-flight work, such as a spreadsheet or table]. List every mismatch or bad record in a report, with a draft clarification email to the person responsible for each. Stop when the report and drafts are ready, and show me the emails before sending any.
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