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n end-to-end automated Complaint Management and Follow-Up system built to log tickets, track resolution status, send multi-channel reminders, and automatically escalate pending issues to managers.
Core Workflow Steps
Data Ingestion & Cleaning: Receives complaints via API/Form/Callbot, then cleans and validates incoming payload using JavaScript.
Database Tracking (Airtable): Searches Airtable by tenant_id and phone—creates a new ticket if missing, or updates the existing ticket if found.
Automated Multi-Channel Notifications: Sends reminders via WhatsApp, Email (Gmail), or SMS (Twilio) if the status is not set to "Done."
Smart Loop & Escalation: Pauses for a set duration, re-checks completion status, increments follow-up attempt counters, and automatically escalates to a manager if maximum attempt limits are reached.
The network for creativity
Join 1.25M professional creatives like you
Connect with clients, get discovered, and run your business 100% commission-free
Creatives on Contra have earned over $150M and we are just getting started
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Art made by children of the past. Rebuilt for children today.
The goal is not to replace the original work, but to create a bridge between children separated by centuries.
Laura Hyde, age 13 — Sampler, 1800
https://www.metmuseum.org/art/collection/search/14059
Rebekah White, age 13 — Sampler, 1766
https://www.metmuseum.org/art/collection/search/14087
John Singleton Copley, about age 15 — The Return of Neptune, ca. 1754
https://www.metmuseum.org/art/collection/search/10534
Some concept videos
An AI agent in an inventory system shouldn't place orders. It should notice things.
In TechFoundry IMS the agents do two jobs: read incoming purchase orders (messy PDFs, emails, the occasional photo) into structured records, and flag anomalies — a supplier's price jumping 30%, a quantity that doesn't match history, a duplicate PO with a different number.
The agent never changes stock levels. It writes a suggestion with its reasoning attached, and a person clicks approve. Same pattern as my review console, because it's the only pattern I've seen survive real operations.
If you run inventory or ops and you've been pitched "fully autonomous" — ask what happens on the day it's wrong. Case study: https://contra.com/p/eemeBeMW-tech-foundry-ims-ai-automated-inventory-and-order-operations
Super impressive work
🚀 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.
