Today I mapped out an interesting automation project I’m about to embark on. The challenge is something many brands face but rarely solve properly. A company has thousands of user-generated videos stored in Google Drive. The content shows dogs wearing their products, but the entire library is chaotic. No tags, no metadata, no searchable structure — just folders filled with videos collected over several years. Finding the right clip for marketing or social media becomes almost impossible. So I designed an AI-powered automation system using Make that will automatically organize the entire video library. The system will work in **three stages. Stage 1 – Video Library Indexing The first automation will scan the entire Google Drive library, including nested folders. Every video file will be logged into Google Sheets with key details like file name, file ID, link, and size. This instantly converts thousands of scattered files into a structured video database. **Stage 2 – AI Tagging Engine** Next comes the intelligence layer. The automation will read rows in Google Sheets where tagging is missing, download the video, and send it to Gemini for analysis. The AI will return structured JSON metadata such as: • Dog breed • Product type • Product color • Content type (UGC, review, demo, etc.) • Setting (indoors, outdoors, park, home) • Season • Usability rating for marketing • A short content description The automation will then parse the JSON and automatically write all the metadata back into the spreadsheet. Stage 3 – Continuous Automation Finally, a monitoring workflow will watch for new uploads in Google Drive. Any new video added to the folder will automatically pass through the same pipeline — analyzed, tagged, and added to the structured database. The result will be a fully searchable AI-organized video library. Instead of scrolling endlessly through folders, the brand will be able to filter content instantly by: • Dog breed • Product type • Scene or setting

Last week I rebuilt a complex Alteryx workflow inside n8n for a client. The goal was simple Stop running manual data routines across multiple tools and turn everything into one automated pipeline. Before this setup the client had to run the workflow manually every time they needed to process new data. It involved multiple steps data cleaning transformations and sending the results to different systems. So I replicated the entire logic inside n8n. Now the system automatically pulls data processes it cleans it transforms it and sends the final output to the correct tools without anyone touching it. What the automation handles now Replicates the full Alteryx workflow inside n8n Automatically processes incoming data Connects multiple APIs and data sources Cleans and transforms datasets automatically Runs on schedule or real time triggers Sends processed data to the correct systems instantly The interesting part was translating the original Alteryx logic into nodes conditions and data transformations inside n8n while making sure everything stayed reliable and scalable. What used to be a manual routine is now a fully automated data pipeline running quietly in the background.

A few weeks ago, I worked on a customer support system that was completely scattered. Messages were coming in from a website chat widget, Instagram, Facebook, WhatsApp Business, email, and bookings through Calendly. Nothing was connected. The team was constantly switching between platforms trying to keep up and occasionally missing conversations. It was not a tool problem. It was a process problem. Instead of jumping straight into building workflows in n8n, I started by mapping the support journey. Where does a conversation begin. What information is needed upfront. When should automation handle it and when should a human step in. Once that logic was clear, the technical build became much more intentional. Using n8n, I created a centralized workflow that collected messages from all channels, routed and tagged them automatically, and prioritized conversations based on context. AI was added where it made sense for intent detection and draft responses without removing the human touch. Calendly bookings were synced and everything was structured in a clean and maintainable way so the system could scale as the business grows. The biggest result was not just automation. It was clarity. No more missed messages. Faster response times. Clear ownership within the team. Far less manual triaging. What stood out most to me is that effective automation is not just about connecting APIs. It is about translating business requirements into logical and reliable workflows. When the process is designed well, the technology simply brings it to life.

Today I am working on a full scale recruitment automation for a specialist food and beverage plus entertainment recruitment agency This is not a basic setup Everything is already designed documented and specced out My job here is pure execution building exactly to specification The system is made up of eight connected Make workflows that handle everything from CV parsing to candidate ranking invoicing outreach syncing and commission calculation Gmail Drive Airtable Sheets Claude AI PDF extraction and outreach tools all talk to each other without manual work CVs dropped into Gmail are parsed and classified by AI and written into Airtable and Google Sheets at the same time Role context is built automatically from job descriptions transcripts and decks LinkedIn PDFs are ranked by AI against full role context and shortlists are generated and emailed Invoices are generated and sent when placements are confirmed Calls transcripts update records Outreach CSVs sync stages Commissions are calculated and recruiters are notified The Airtable setup is just as deep with linked tables recruiter views manager views and automated calculations that run quietly in the background What makes this project interesting is the level of reliability required Every workflow has error handling Failures trigger owner alerts Everything is tested end to end with real data before handover This is a great example of how Make can be used to replace an entire recruitment operations team with clean well designed automation that actually scales

Today I want to talk about a type of automation work that goes beyond building scenarios and actually helps businesses scale properly This is the kind of setup where a business optimization consultancy sells an automation product and someone like me brings it to life using Make The process starts with a clear client brief and prep materials so there is no confusion from day one From there the automation is built tested and refined inside Make to connect the right tools and ensure everything works smoothly Before handoff every workflow is tested properly so the client gets a clean and reliable system What makes this interesting is that it is client facing You are not just building automations you are explaining them in simple terms and helping founders understand how to use what was built This model scales well because as more clients are sold more automation projects are delivered It is a great example of how Make can be used as a repeatable automation product not just a one off setup