Today I created an automation that keeps my inbox clean without me touching anything. The workflow automatically scans Gmail for unread emails older than 7 days, checks if they match certain categories (newsletters, promos, low-priority messages), and then does three things: Summarizes the email using AI Labels it with “Low Priority – Auto Managed” Archives it instantly The best part? The summary is sent to a Google Sheet so I can quickly review what I missed without reading the whole email. A clean inbox, less stress, and more control. Automation is truly a superpower. #n8n #AutomationExpert #EmailAutomation #InboxZero #Productivity #WorkflowAutomation

One thing many businesses struggle with is messy data. People type names differently, emails come with spaces, phone numbers come in random formats, and duplicates pile up. Over time, this leads to confusion, bad reporting, and wasted manual clean-up. Recently, I built an automation that cleans and standardizes data before it even enters the client’s system. Here’s what it did: Whenever a new entry came in from a form Google Form Typeform or a website The automation checked for missing fields Fixed formatting issues like name casing email spacing and phone number style Validated the email to make sure it was real Removed duplicates by comparing new data with existing records Tagged and organized entries before sending them to the CRM or Google Sheet All of this happened automatically in the background. The client didn’t need to open spreadsheets or correct errors manually. Every new record was clean consistent and ready to use the moment it arrived. This might not sound flashy but data quality is the backbone of any good system. Bad data breaks automations but clean data powers smooth workflows.

Most businesses want feedback but they rarely get it because asking manually is time-consuming. So I built an automation that collects feedback automatically without the team lifting a finger. Here’s how it works: When a customer makes a purchase through Shopify, WooCommerce, Stripe, or PayPal The automation waits a set amount of time usually 24 to 48 hours It sends a friendly feedback request using email, WhatsApp, or SMS The customer fills a short form or selects a rating The response automatically updates the client’s CRM or Google Sheet If the feedback is negative, it triggers a follow-up workflow for customer support If the feedback is positive, it saves it for testimonials or marketing This solves a big problem. Businesses want feedback but they forget to ask or don’t have time to sort and store the responses. With automation, every review is collected, organized, and acted on instantly without manual effort. If you want another fresh automation topic, just tell me.

Recently, I’ve been working with Nano Banana, and one thing I’ve learned is that it becomes even more powerful when paired with automation. Most people use it manually, but once you automate it, the entire image creation process becomes almost effortless. I set up a workflow where Nano Banana generates images automatically based on prompts, categories, or structured text coming from a database or form. The moment new data enters the workflow, the automation prepares the prompt, sends it to Nano Banana, waits for the image, and stores it exactly where the client needs it. What used to take minutes or hours now happens quietly in the background. This setup works perfectly for: daily social posts blog or article visuals ecommerce product variations faceless content thumbnails or banner images Automation turns Nano Banana into a complete image production engine. If you create visuals often, automating this step frees up time and gives you clean, ready-to-use images whenever you need them. If you want ideas on how to use this in your own workflow, I’m happy to share what worked for me.

One thing I have come to appreciate more in automation is filters. They may look simple, but they can be the difference between a clean workflow and complete confusion. In a recent project, I had to set up an email auto response system. The client wanted replies to go out only under very specific conditions, and not every incoming message deserved an automated answer. Without proper filtering, the automation would have sent the wrong messages, replied at the wrong time, or even created loops. So I built a set of smart filters that checked things like: message intent keywords in the email whether the sender had already been responded to time of day the category of the inquiry Once the filters were in place, everything came together. Only the right messages got responses. No duplicates. No errors. Just clean, predictable automation that behaved like a human assistant. Filters may seem small, but when you use them well, everything works smoother. If your automation feels messy or unpredictable, you probably need better filters.