Testing an AI Tool That Finds Winning Products Before They Trend

I’ve been experimenting with an idea:
An AI-powered product curator that helps you discover high-potential products before they go viral.

This post is part of my experiment.
If enough people show interest, I’ll turn it into a real tool.

💡 The Problem

If you sell online, you’ve probably asked:

“What should I sell next?”
“Will this product actually take off?”
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Most product tools show what’s already trending.
By then, the opportunity is often gone.

What we need is something smarter—something that predicts what people will want next, before the market floods.

🚀 The Idea: AI Product Curator

I’m building a prototype that uses AI to:

Scan product data (from marketplaces, social media, etc.)
Analyze emotional appeal and descriptions
Predict which products will trend soon
Group similar products into niche clusters

Imagine a 24/7 virtual product scout that shows you what to test, stock, or market—based on real signals.

🧠 How It Works (So Far)

TF-IDF + NLP → understands product descriptions
Trend signals → popularity score, price, emotional tone
Logistic regression → predicts whether it will trend
KMeans clustering → groups products into themes

You get a simple shortlist:

✅ “Test this”
💤 “Skip this”
🧠 “This category is heating up”

🗺 System Workflow (Mermaid.js)

graph TD
    A["Start: Product Idea Testing"] --> B["Collect Product Data"]
    B --> C["TF-IDF Vectorization of Descriptions"]
    C --> D["Combine with Numerical Features (Price, Popularity)"]
    D --> E["Train Logistic Regression Model"]
    E --> F["Predict If Product is Likely to Trend"]
    D --> G["Run KMeans Clustering"]
    G --> H["Group Similar Products (Market Segments)"]
    F --> I["Select High-Potential Products"]
    H --> I
    I --> J["Create Shortlist for Testing / Stocking"]
    J --> K["Review or Automate Decisions"]
    K --> L["Deploy to Live System or Dashboard"]