Behind LinkedFull: The AI Technology Powering Smarter Shopping

In an era where AI-driven shopping assistants are transforming the way we discover and purchase products, LinkedFull stands out as an intelligent, AI-powered shopping companion. But what exactly is behind LinkedFull? How does it understand your needs, find the best recommendations, and make shopping seamless?

The secret lies in cutting-edge AI technologies like Generative AI, Agentic AI, and Retrieval-Augmented Generation (RAG). These innovations work together to provide precise product recommendations, accurate search results, and a truly personalized shopping experience.

Let's break down the core technology that makes LinkedFull a powerful AI shopping engine.

AI Agents: The Digital Personal Shopper

At the core of LinkedFull are AI-powered agents that act as a digital shopping assistant, capable of:

  • Understanding user preferences based on searches, purchases, and browsing behavior.
  • Filtering through millions of e-commerce listings in real-time to find the best product matches.
  • Learning from interactions to improve recommendations over time.

These AI agents work autonomously, simulating how a human shopping assistant would analyze your taste, compare options, and make personalized suggestions—all in seconds.

Large Language Models (LLMs): Making Shopping Conversational

LLMs like GPT-based models power the conversational capabilities of LinkedFull. Instead of traditional keyword-based searches, LinkedFull allows users to:

  • Describe what they need in natural language (e.g., "I want a casual summer dress under $50")
  • Ask follow-up questions (e.g., "Show me similar dresses in blue")
  • Receive AI-generated explanations (e.g., "This dress is popular because it's trending in summer collections")

By integrating LLMs, LinkedFull goes beyond search—it understands context, intent, and nuanced preferences, making shopping feel more natural and intuitive.

Retrieval-Augmented Generation (RAG): AI That Thinks and Retrieves

One of the biggest challenges in AI shopping assistants is ensuring up-to-date and relevant results. This is where Retrieval-Augmented Generation (RAG) comes in.

What is RAG?

RAG is an AI framework that combines generative AI with real-time information retrieval. Instead of relying solely on a pre-trained language model, RAG pulls fresh and accurate data from external sources (such as live product listings and reviews).

How does LinkedFull use RAG?

  • Finds the most current products from multiple e-commerce websites.
  • Prevents hallucinations (i.e., generating products that don't exist).
  • Ensures factual accuracy when summarizing reviews, pricing, or trends.

Visual AI: Searching with Images, Not Just Words

Many shoppers struggle to describe exactly what they're looking for. Keywords alone can be limiting—what if you don't know the exact name of a fashion style, a furniture trend, or a specific product type? This is where multimodal AI comes into play, allowing users to search using a combination of images, text, and even voice inputs.

How LinkedFull Uses Visual AI & Multimodal Models

With multimodal AI, LinkedFull takes product discovery beyond traditional text-based search by integrating multiple input types:

  • 📸 Image-Based Search – Upload a picture of an outfit
  • 🛍️ Find Visually Similar Products – AI scans thousands of online stores to find identical or closely matching items
  • 💡 Discover Variations & Alternatives – If an exact match isn't available, AI suggests similar styles, colors, and price options
  • 🗣️ Voice + Image Search (Coming Soon) – Users will be able to describe what they want while uploading an image, making search even more intuitive