Tools: I Built An Ai-powered Fake Deal Detector That Caught 2,347 Scams In...
Posted on Mar 1
• Originally published at avluz.com
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.
Our first attempt was a disaster. I spent three weeks building a rule-based system with hardcoded thresholds:
The problem? E-commerce pricing is way more nuanced than simple rules can handle. We had:
I was ready to scrap the whole thing until our data scientist suggested: "What if we let the machine figure out the patterns?"
Most blog posts jump straight to "use AI!" without explaining why. Here's the reality: fake deals aren't just about simple math. Retailers employ sophisticated pricing psychology:
Traditional rules can't adapt to these evolving tactics. Machine learning can identify patterns we humans would never spot—like how certain sellers always inflate prices exactly 47% before Prime Day, or how "limited time" deals repeat every 12 days.
Source: Dev.to
