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Automated Review Requests for Local Service Businesses
The Problem
Google reviews help local businesses get found, and every HVAC company, salon, cleaner and contractor knows it. Most of them ask for reviews inconsistently. The reason isn’t that they don’t care. Asking every customer, right after every job, depends on someone remembering at the exact moment the business is busiest. Happy customers never get asked, and the review count grows slower than it should.
The Solution
I built an n8n system that sends the review request automatically, once, at the right time, and tracks what happens next. Nobody gets asked twice and nobody gets forgotten.
How the System Works
1. Checks every 15 minutes. The workflow reads a Google Sheet that lists each job and what has already been sent. The sheet is the system’s memory, so nothing depends on timers that can fire twice.
2. Sends the request. Two hours after a job is marked complete, the customer gets an email with a review link.
3. Tracks the click. The link goes through a tracking step that records “clicked” in the sheet, then sends the customer to Google.
4. Sends one follow-up. If there’s no click after three days, one reminder goes out. Customers who clicked or opted out are skipped.
5. Alerts me on errors. A separate error workflow emails me if any step fails.
Why this design: every step re-checks the sheet before sending, so the system can’t double-send and a customer who already responded is never contacted again.
Tools: n8n, Gmail, Google Sheets
I'd test the tracking link with email security scanners before using clicks to suppress reminders. How do you distinguish an automated link check from a customer opening the review page?
You’re right, a plain GET link can be hit by a scanner and mark a false click. I’m moving it to a button-confirmation page and logging every hit with timing and user agent, then I’ll test on Gmail and Outlook and share the results.
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Automated weekly ad performance reporting with n8n and the Meta Marketing API The problem: 🔥 Agencies running paid ads for clients usually report performance manually: logging into Meta and Google dashboards, pulling numbers, writing a summary, and emailing it out every week. It's repetitive, easy to forget, and gets inconsistent the busier the agency gets.
What I built🧠 A scheduled n8n workflow that runs every Monday morning and handles the full reporting cycle without manual input.
How it works🚀
A schedule trigger fires early Monday morning
An HTTP Request node authenticates with the Meta Marketing API and pulls the latest ad account insights (spend, impressions, clicks, CTR, CPC)
The response is cleaned and mapped into a consistent format
An IF node checks whether there's real data to report
If there is, the data is handed to an AI step that writes a short, plain English summary of the week's changes, which is then logged and emailed
If there isn't, the system sends a clear "no activity this week" notice instead of a blank or broken report
Why the no-data path matters👍 A lot of automations only handle the happy path and break or go silent when something unexpected happens, like a week with no ad spend. I built this one to handle that case explicitly, so the client always gets a message, even if the message is just "nothing to report."
Tools used: n8n, the Meta Marketing API, an AI model for the summary writing, Google Sheets for the data log, and Gmail for delivery.
What I'm adding next GA4 data alongside the Meta Ads data, and a Looker Studio dashboard reading from the same sheet, so one template can be reused across multiple client accounts.
Nice that the IF branch catches empty weeks, so clients get a clear note instead of a broken email.
AI Content Automation Pipeline | n8n • ChatGPT • Airtable • Notion
Built an AI-powered content automation system that streamlines content generation, management, and publishing through one connected workflow—reducing repetitive work and improving content production efficiency.
What I Built
AI content generation with ChatGPT / OpenAI API
Automated n8n workflows
Airtable & Google Sheets data automation
Notion draft management and publishing
Custom REST API & webhook integrations
Optimized AI prompt engineering
End-to-end content workflow automation
Tech Stack
n8n • OpenAI API • ChatGPT • Airtable • Notion • Google Sheets • Python • Webhooks • REST APIs
Key Skills
AI Automation • Workflow Automation • Prompt Engineering • API Integration • Content Automation • Database Automation • ChatGPT Integration • n8n Development
SEO Keywords
AI Content Automation • n8n Automation • n8n Developer • ChatGPT Automation • ChatGPT API Integration • OpenAI API Integration • AI Workflow Automation • AI Automation Developer • Content Automation • n8n Workflow Development • OpenAI Automation • Prompt Engineering • API Integration • REST API Integration • Webhook Automation • Airtable Automation • Notion Automation • Google Sheets Automation • Python Automation • Database Automation • Business Process Automation • Marketing Automation • Automated Content Generation • Generative AI • Custom AI Automation • Custom Workflow Development
Result: A scalable AI content pipeline designed to reduce manual work, connect multiple platforms, and make content creation and management faster and easier.
#AIContentAutomation #AIAutomation #n8n #n8nAutomation #ChatGPT #OpenAI #WorkflowAutomation #ContentAutomation #ChatGPTAPI #OpenAIAPI #APIIntegration #PromptEngineering #Airtable #Notion #GoogleSheets #GenerativeAI #BusinessAutomation #AutomationDeveloper #AIDeveloper
Linking ChatGPT responses to Airtable and Notion in one flow wipes out the manual transfer step. That lets you focus on the creative side instead of data juggling.
"We can't afford a data analyst."
I've heard this from 3 different startups this year.
So I built one.
Metriva is an AI-powered business analyst that:
📊 Turns messy spreadsheets into executive dashboards
🧠 Writes C-level business briefs automatically
📈 Forecasts trends + detects anomalies
💬 Answers questions about your data in plain English
🎯 Finds your Pareto 80/20 — who drives 80% of your revenue?
No SQL. No Python. No data team.
Just upload your file and get answers.
Built with Flask, vanilla JS, and a lot of late nights.
🔗 Try it: metriva.ai1.vercel.app
#AI #BusinessIntelligence #Founder #IndieHacker #DataScience
The brief-writing piece is interesting — curious how you handle the validation step. AI-generated language tends to sound confident even when the underlying data is sparse or the trend is ambiguous. One pattern that helps: have the model output a confidence signal alongside the...