AI Recommendation Engine for eCommerce by NetSet Software

The Problem

Guidalor's customers were facing challenges in using the eCommerce based platform to search for products and get the right product recommendations for Amazon. There was a massive daily need to work with long Amazon URL lists or seller spreadsheets that required manual review. This process was slow and inconsistent, which often led to delays, inaccurate recommendations, and reduced customer satisfaction. Without automation, the curation of products was not having scalability, personalization, and user engagement. Additionally, there was no interactive method for presenting the curated suggestions to end users.

Guidalor required a solution that could process the product information in bulk, interpret the user intent, categorize items with the best accuracy, and give dynamic results. The overall goal was to simplify the research workflows while making the user experience better using an AI powered system that creates real time, personalized product recommendations for integration on the websites of their clients.

Let's Talk

The Challenges

When Guidalor approached NetSet Software, creating such AI driven product recommendation engines for a range of product types came with multiple technical and usability challenges.

AI Categorization:

Creating AI agents that can understand vague or minimize the user preferences and still assign the accurate product categories was quite complex.

File Handling Versatility:

Supporting the uploads in diverse formats like CSV, TXT, or spreadsheets needed a flexible backend that could normalize and validate the data efficiently.

Scraping Accuracy

Scrapping the live data from Amazon at scale without missing product details or triggering the security blocks required some robust engineering.

User Experience Design

Creating the quizzes that felt interactive, intelligent and relevant to brands required balancing simplicity with personalization.

Easy Web Integration

To make sure that the quiz widget could be copied and integrated anywhere without breaking layouts or functionality was again challenging

Intent Driven Categorization

Using the QAI framework and ChatGPT style prompts, the system asks users what kind of product they are looking for and categorizes as per the inputs.

Flexible File Support

The upload system parses both the URL based files and seller inventory spreadsheets, which supports wide usage.

Live Scraping Engine

A Python-based scraper pulls product name, pricing, ratings, and category data with speed and accuracy

Interactive Quiz Builder

Generates user friendly quizzes, placing the recommendations directly below the Q&A interface.

HTML Embed Export

One-click HTML copy buttons let users export the quizzes instantly that can be embedded on any client platform.

How an AI powered product recommendation engine boosted engagement for eCommerce visionaries?

Tech stack

QuantumAI Framework

Python

ChatGPT

Custom Amazon Web Scraper

HTML

The Result

Guidalor in Action …

Got an AI project in mind? Get estimation.