## The Problem I Discovered
Every day, people buy products without fully understanding what they contain or whether they’re actually safe or suitable for them.
Product labels are hard to read, written in tiny fonts, multiple languages, or filled with technical terms. Whether it’s groceries, medicines, cosmetics, or packaged goods — users are often left guessing. I saw parents worried about food safety, people with health conditions checking ingredients repeatedly, and even shop owners manually entering product details.
That’s when I realized the real problem wasn’t lack of products — it was lack of clear, accessible product information.
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Planning & Tech Stack Decision
Before building ScanZen, I validated the idea by talking to users across different categories — health-conscious shoppers, parents, small business owners, and people managing medical conditions.
Once the need was clear, I designed ScanZen as a universal product scanning platform, not limited to one category.
Chosen Stack:
- Frontend: React with TypeScript for scalability and maintainability
- Backend: Node.js with Express for fast API development
- AI & Vision: Image recognition + AI analysis for product understanding
- Database: MongoDB for flexible product and scan data storage
The goal was to keep the system modular, fast, and easy to expand.
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Development Journey
Week 1: Image Recognition & Core AI Logic
The first week focused on building the core — recognizing products from images.
I implemented image processing to extract product data such as names, brands, ingredients, labels, and documents. Once extracted, AI analyzes the data to generate safety insights, health scores, and explanations.
const analyzeProduct = async (extractedText) => {
const response = await openai.chat.completions.create({
model: "gpt-4",
messages: [
{ role: "system", content: "You are a product and health analysis expert." },
{ role: "user", content: `Analyze this product data: ${extractedText}` }
]
});
return response.choices[0].message.content;
};
This became the foundation for ingredient risk detection, health scoring, and smart recommendations.
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Week 2: Frontend & User Experience
In the second week, I focused on building a simple, mobile-first interface.
Users can:
- Scan or upload product images
- Scan documents, receipts, or labels
- View extracted data instantly
- Understand results with clear explanations
The UI was designed to feel friendly, not technical — because ScanZen is meant for everyone.
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Week 3: Testing, Feedback & Refinement
User testing revealed an important insight:
People don’t just want data — they want understandable guidance.
So I added:
- Clear risk labels (Safe / Moderate / High Risk)
- Simple explanations for each ingredient
- Personalized advice based on age and health needs
- Alternative product suggestions with reasons
This made ScanZen far more useful than a basic scanner.
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Week 4: Launch Preparation
In the final week, I prepared ScanZen for launch:
- Optimized performance and scanning accuracy
- Added multi-language support
- Enabled offline scanning for core features
- Prepared export options (PDF, images, documents)
- Set up pricing and future API access plans
After internal testing, I soft-launched ScanZen to early users.
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Launch Results
Early usage validated the idea strongly:
- Users scanned products across food, medicine, cosmetics, and documents
- High engagement due to instant insights
- Strong interest from businesses and developers for API access
- Positive feedback on health scoring and clarity
ScanZen quickly evolved from a scanner into a decision-making tool.
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Key Takeaways
ScanZen isn’t just about scanning products — it’s about empowering people to make informed decisions.