Web · Trulio
Full Stack Developer — platform, scrapers, and recommendation surfaces
Trulio
AI product recommendations for shopping
What it is
Trulio recommends products from how people actually browse, not from a static merchandising grid. I built the React/Node experience and Python scrapers (BeautifulSoup, Scrapy) that keep MongoDB/MySQL catalogs fresh so recommendations stay aligned with live inventory and behavior.
The product is the loop: observe preference, refresh catalog, recommend again—across devices.
Why I built it
Catalogs go stale and “recommended for you” becomes random. Trulio needed a pipeline from the live web into a shop that still feels personal.
Whom it is for
Trulio — for shoppers discovering products, and the business that needed recommendations grounded in a living catalog.
Aim
Keep recommendations honest: they should track user behavior and a catalog that is actually in stock.
Goal
A shopping experience that feels curated because the data underneath is continuously collected and scored.
Hope
That smaller retailers can offer personalization without a FAANG-sized data org.
What it does
- Personalized recommendations from user behavior
- Python scraping (BeautifulSoup, Scrapy) feeding MongoDB/MySQL
Stack
React · Node.js · Python · MongoDB · MySQL