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I built a Smart Request Manager to automate customer request workflows using Google Forms, Google Sheets and Google Apps Script.
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🚀 My First AI Automation Project with n8n
I recently built my first automation project — an AI-Powered Invoice Extraction & Tracking System.
The problem I wanted to solve was simple:
Imagine a business receiving dozens of invoices or cash memos every week. Someone has to open each file, read the information, and manually enter the vendor name, date, items, total amount, and category into a spreadsheet.
That’s repetitive, time-consuming, and easy to get wrong.
So I built a workflow where:
📂 An invoice is uploaded to Google Drive
→ 🤖 Gemini AI reads and extracts the invoice data
→ ⚙️ n8n processes and validates the information
→ 📊 The data is automatically added to Google Sheets
The best part?
No manual data entry. Just upload the invoice and the rest happens automatically.
While building it, I also worked with things like workflow triggers, loop control, API errors/retries, and handling multiple files.
For me, this project was less about simply “building an n8n workflow” and more about learning how AI + automation + data workflows can solve real business problems.
This is just my first step into AI automation, and I’m excited to build more practical systems like this.
If your business has a repetitive process that could be automated with AI, n8n, or data workflows, feel free to DM me. I’d be happy to discuss it.
#AIAutomation #n8n #Automation #ArtificialIntelligence #DataAnalytics #DataAnalyst #WorkflowAutomation #GenerativeAI #BusinessAutomation #AI
Smart pipeline! Have you added strict JSON schema validation after Gemini extracts the invoice fields? Found that it stops edge case hallucinations before the data ever hits Sheets.
AI Personal Assistant — Email, Calendar, Tasks & Expenses in One Agent
Most productivity tools solve one problem each — an inbox tool, a calendar tool, a task manager, a notes app — which means the user still has to be the one connecting them. I built an AI agent that acts as the connective layer instead.
The system is triggered via webhook (so it can be reached from anywhere — chat, a form, or another app) and routed through a central AI Agent that decides which tool to use based on what's asked, including:
Gmail — read, search, and send messages
Google Calendar — create, update, and search events across multiple calendars
Task management — create, update, retrieve, and delete tasks
Notes — create and update notes/documents automatically
Expense tracking — logs and retrieves expenses on request
Web search — pulls live information when the answer isn't in any connected tool
Calculator — handles quick math without leaving the conversation
Instead of a rigid, single-purpose bot, this is built as a true multi-tool agent — it holds context (via memory), picks the right tool for the request, and responds like a real assistant instead of a fixed menu of commands.
Super impressive work
Building the AI Layer for a Food Delivery Support Bot🚀
A regional food delivery platform needed a customer-facing AI agent that could handle live order queries in Arabic and English — without hallucinating status, misrouting frustrated customers, or breaking under injection attempts.
My role was the AI behavior layer: system prompt architecture, decision logic, and the test suite that kept it honest. I designed an 11-gate decision tree where routing is determined by order status × elapsed time — not customer tone. A frustrated message doesn't trigger escalation. A VENDOR_ACCEPTED order delayed 20 minutes does.
The test suite ran 41 versions across 10 categories: routing thresholds, language switching, ETA edge cases, multi-turn flows, and injection resistance.
Result: live in production for over a month, 98% customer satisfaction rating from the support team.
Super impressive work