I built a visual twin of Shopify stores to find where customers get stuck

I’ve been working on something called StoreTwin.

The idea came from doing Shopify CRO and technical SEO work at Shugert.

When we audit a store, we normally bounce between analytics, crawling tools, the storefront, navigation, collection pages, product pages, and a bunch of spreadsheets trying to answer a simple question:

What path is the customer actually taking, and where is the store making that path harder than it needs to be?

So I started building a different way to look at it.

StoreTwin takes a public Shopify URL and creates an interactive visual twin of the storefront.

It:

• discovers the store structure and connections between pages

• maps likely shopper journeys

• lets you explore the store visually instead of as a list of URLs

• uses GPT-6 Astra to investigate a specific friction point

• proposes a possible improvement

• lets you replay the current journey vs. the proposed one before touching the live store

The important part for me is that the AI isn’t just producing a generic CRO audit.

It has the actual structure of the store as context and is supposed to investigate one evidence-backed opportunity at a time.

Right now it’s still an MVP.

There’s no Shopify write access, billing, or complicated onboarding. You give it a public Shopify store and it builds the twin.

I initially built it as an internal tool for our agency, but while working on it I started wondering if this could be useful as a standalone product for merchants, ecommerce teams, and other agencies.

I’d especially love feedback from people here who work with ecommerce:

Would you use something like this before making CRO/UX changes to a store?

And what would be more valuable to you: finding problems, simulating fixes, or being able to compare customer journeys before and after a change?

You can try it here https://storetwin.shugert.com.mx/