Most Shopify audit tools give you a polite report.
Vetta roasts your store instead and tells you exactly how to fix what's broken.
A few weeks ago, I built Vetta an AI-powered tool that audits Shopify stores for conversion issues.
Instead of a standard “here are some insights” report, it bluntly calls out where your store is bleeding money, then gives you actual solutions to fix it.
I built the first version in a single session using Emergent.sh.
The core functionality was there, the app was running, and I could get surprisingly far without building everything from scratch.
But there was one problem:
The interface worked, but I wasn't completely happy with where it was. I wanted more control over the design and the ability to iterate without constantly spending tokens.
So instead of continuing to spend tokens on changes I knew I could eventually handle myself, I decided to take a different route.
I deployed the code to Git and started working on it directly.
That shift has been interesting.
Emergent helped me get from idea → working product quickly.
Git and my own development workflow give me more control over what happens next which matters a lot for a project where the tone and design carry as much weight as the logic behind it.
I'm still learning the codebase and figuring things out as I go, but that's kind of the point of this project.
I'm not trying to build the perfect app on the first attempt.
I'm trying to understand what I can build, where AI can accelerate the process, and where I still need to get my hands dirty.
Vetta started as an AI-built project. Now I'm turning it into a project I actually understand.
Top comments (2)
Taking the tool back into your own hands is a good instinct when the output affects trust. AI can be useful for the first audit pass, but ecommerce recommendations need context: margin, catalog reality, brand voice, and which changes a store owner can actually implement this week.
This is the part of AI-assisted development I find most interesting: the moment you take ownership back from the tool. 👏
AI can get you from idea → working prototype incredibly fast, but understanding the codebase is what turns that prototype into something you can actually trust and evolve.
And I really like your point about design and iteration. Sometimes the bottleneck isn't what the model can generate, but how much control you're willing to give it over the decisions that matter.
AI can accelerate the build. Ownership still has to stay with the developer. 🚀