What AI Actually Does in Your Store – E01

What AI Actually Does in Your Store

I’m allergic to AI gurus telling you AI will disrupt everything but never showing you how they actually use it.

At Luigi’s Box, we talk to e-commerce managers every day. I went through hundreds of recent conversations looking for patterns, then pressure-tested the findings with our product team. That’s what this series is built on.

We’re bullish on AI. It’s been in our products for years. But we’ve also seen what happens when teams chase hype instead of results.

This series is about what’s already working, what’s worth watching, and what’s actually coming next for e-commerce product discovery.

E01:Your Store Might Already be Running AI

A shopper searches for “running shoes” on your site. The results page loads. Products appear in a specific order.

Something decided that order. And it wasn’t you.

It was a machine learning model watching what shoppers before them searched for, clicked on, added to cart, and bought. It noticed that one product converts better for that exact query. It moved it up without you knowing.

That’s AI. And it’s been in our clients’ stores the whole time.

Not every team realizes that because it doesn’t announce itself. It just works in the background.

What’s actually been running

Luigi’s Box started as a search data company, not a search solution company. The first product was Analytics.

Understanding what worked on a site and what searches led nowhere.

The system collects behavioral data from every session and uses it to decide which products rank higher for each specific query. A product at position 8 for “running shoes” that converts better than the ones above moves up.

If you have historical transaction data, you can feed it in at the start to speed up the learning.

So what did LLMs actually change?

The behavioral layer, that’s the AI that’s been there for over a decade. LLMs are slightly different.

Behavioral AI learns from what shoppers do while LLMs understand what shoppers say.

That’s a difference, but it doesn’t mean there’s no magic in what’s already there. Shoppers have always typed more than single keywords.

Queries like “blue linen shirt L” or “coffee table oak 120cm” aren’t new. The model has been handling that kind of input for years.

LLMs add a layer on top. They make it possible to understand intent in conversational language, generate dialogue, interpret context across a full sentence. That’s what’s new.

But the behavioral layer has been handling more than most teams realize. We’ll look into it next week.