Cleaner text on AI wrap mockups: a better model behind the fix step

A small change in the spot that matters most

No AI image tool reliably draws a full set of text — brand name, tagline, phone, website, email — perfectly in one shot. So after the wrapped vehicle is generated, a second step reads the text back and fixes anything that's wrong.

That fix is itself an image edit. We tell the AI: change this one word, leave everything else exactly as it is. The hard part is the "leave everything else alone." A weak model repaints the whole area and breaks the text that was already correct.

We upgraded the model that does this edit to gpt-image-2. It sits under every pipeline in the tool — generating a wrap, applying a design, refining an image, fixing text — so the whole product gets better. The clearest way to show it is our instant-mockup pipeline, so that's the example below.

How we tested it

We used a made-up business — Pampered Paws Mobile Grooming — so nothing real is involved.

Everything about "Pampered Paws" is made up — brand, logo, phone, website, email and tagline. No real business was used.

We ran the instant pipeline several times. Each time, once the wrapped van was generated, we ran the same fix step on the same design two ways: once with the previous model, once with gpt-image-2. Same picture, same instruction. Only the model is different — so anything you see is down to the model, not luck.

One run, up close

Asked to fix one piece of text, the previous model repainted the whole area and scrambled everything — "Pampered Paws" became "PAMPENED PAWS", the tagline turned into nonsense, and the contact details went unreadable.

gpt-image-2 fixed the wording and left the rest of the wrap alone — clean brand name, readable details, design untouched.

Every run that needed a fix

That wasn't a lucky pick. Here is every run where the first attempt had a text mistake, so the fix step actually ran. Same story each time: the previous model breaks the surrounding text, gpt-image-2 corrects it cleanly.

When nothing needed fixing

Often the first attempt already had the text right. When there's nothing to fix, the step leaves the image alone — so both models produce the same thing, and gpt-image-2 never makes a good design worse.

The honest version

This isn't magic, and we want to be straight about it:

• When the first try is already correct, nothing changes. No fix, no harm.
• The win shows up when a fix is actually needed. That's where the old model used to make things worse, and the new one reliably gets it right.
• It's the quiet kind of improvement — fewer ruined concepts from a scrambled phone number or tagline. Over real use, that's what makes the tool dependable.

It still can't rescue a weak design — a clean text fix won't turn a bland wrap into a great one. And this is a small demo: one made-up brand, one van. But the pattern was consistent across every run.

If you work with wraps and have a tough example you think would trip this up, send it our way — those are the most useful tests we get.