I Built an AI-Powered Fake Deal Detector That Caught 2,347 Scams in 30 Days

Last Black Friday, I watched my mom excitedly show me a "70% off" gaming laptop deal. The original price? $1,299. Sale price? $899. Seemed legit until I checked the price history—that laptop had been $899 for the past 6 months. The "original price" was completely fabricated. That moment sparked something. At Avluz.com , we track prices across 10,000+ products from Amazon, eBay, and Walmart. We had the data. We had the problem. We just needed to build something that could catch these scams automatically. Thirty days later, our AI-powered fake deal detector had flagged 2,347 suspicious "deals" and saved our users an estimated $47,000 in avoided bad purchases. Here's exactly how we built it, including the mistakes that almost derailed the entire project. The $5,000 Mistake That Taught Us Everything Our first attempt was a disaster. I spent three weeks building a rule-based system with hardcoded thresholds: # DON'T DO THIS def is_fake_deal ( current_price , original_price , avg_price ): di