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How I build Retailizy - An Agentic AI based Retail Catalog Builder

How I build Retailizy - An Agentic AI based Retail Catalog Builder

Amazon Nova based AI agentic application to solve everyday challenges of small retailers for catalog building and planning campaigns

One thing I kept noticing — small shop owners are brilliant at picking products and knowing their customers, but they struggle badly with the content side of things. Writing product descriptions, figuring out who to market to, planning promotions — it eats up hours every week.
So for this hackathon, I decided to build Retailizy — an AI-powered retail catalog builder that does the heavy lifting for small and medium retailers.

What It Does

Retailizy is a web app where a retailer can:
  • Add their products
  • Let AI generate taglines, descriptions, and target customer demographics
  • Get a 3-month campaign calendar planned out automatically
  • See customer sentiment from reviews and get improvement suggestions
  • Generate campaign images for Instagram and Pinterest
The whole idea is: you put your product in, you get a ready-to-use catalog entry out.

Where Amazon Nova Comes In

The app used AI capabilities of Amazon-Nova via Amazon Bedrock for production. Nova handles all the structured reasoning tasks:
  • Generating product taglines and catalog copy
  • Analysing customer demographics from product details
  • Summarising review sentiment
  • Creating month-by-month campaign plans

Real-World Application

I tested this with a few product examples — electronics, clothing, home goods — and the outputs are surprisingly usable without much editing. Amazon-Nova generates contextual taglines that actually match the product tone rather than generic marketing fluff.

What I'd Tell Other Builders

If you're building for non-technical users, structured JSON generation from Nova is your best friend. Keep the UI dead simple — the AI should feel invisible, like the app is just smart, not like the user is "using AI."
#Amazon-Nova #Amazon-Bedrock
Any opinions in this article are those of the individual author and may not reflect the opinions of AWS.
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