I Built CouponLab with AI Help. The Coupon Codes Still Have to Survive Checkout.

There’s a very specific kind of optimism involved in copying a coupon code.

You see “20% off.”

You picture the cheaper total.

You paste the code.

“Coupon cannot be applied to this order.”

Apparently, the optimism was the free part.

That gap between finding a discount and actually being able to use it is why I’m building CouponLab.

The approach is straightforward: I use AI to help discover offers, then try the coupon codes myself.

CouponLab is also on Product Hunt https://www.producthunt.com/products/couponlab, so I wanted to share what I’m working on, where AI fits, and the part I’m still trying to get right.

Spoiler: it involves more fine print than I would have chosen for myself.

The problem sounded simple

People looking for coupons want to save money on something they’re buying.

They probably aren’t looking for a new hobby called “paste mysterious strings into a checkout form.”

So the goal for CouponLab is to help people find offers worth trying and understand the conditions that might affect them.

That second part matters more than it initially sounds.

A code can be valid and still be useless for your particular purchase. Maybe it only applies to new customers. Maybe your cart is below the minimum spend. Maybe the item you want is excluded.

The coupon exists. The discount exists. Your eligibility is currently under investigation.

Where AI helps

I use AI as an assistant for discovering offers.

That gives me a starting point for the work. I then try the codes myself.

I care about keeping those steps distinct because finding information about an offer doesn’t tell me what will happen at checkout.

A convincing description is still a description. The cart has its own opinion.

For this project, the useful question is: can AI help me find something that I can then check?

That’s a narrower job than “please automate all of online shopping,” but it gives me something concrete to work with.

I’m comfortable with AI helping me look. I still want to see what happens when the code meets an actual cart.

“It worked” needs a little more explanation

This is the awkward bit.

If I test a coupon and it works, what exactly can I say about it?

I can say it worked in that test.

I can’t reasonably promise that it will work for every shopper, every product, and every possible combination of items.

For example, imagine an offer that takes a percentage off an eligible order over a certain amount. Someone with a smaller cart might get no discount. Someone buying an excluded product might get the same result.

Neither person particularly wants a lecture on eligibility. They just want to know why the total hasn’t changed.

So I’m careful about what a successful test means. The conditions belong alongside the result.

Otherwise, “tested” risks becoming a reassuring word that still leaves the shopper doing all the detective work.

And nobody should need a corkboard and red string to understand a coupon.

The design problem I’m still working through

How much information should appear next to an offer?

Show too little, and an important restriction becomes a surprise at checkout.

Show too much, and browsing coupons starts to feel like reviewing a rental agreement.

I’m still figuring out that balance.

As a shopper, I’d want to quickly understand:

What is the discount?

What do I need to buy or spend?

Is it limited to certain customers?

What does the test result actually tell me?

Those are the questions I want the experience to answer clearly.

The challenge is making that information easy to scan without hiding the detail someone needs.

It’s a small interface decision with a very practical consequence: whether someone spends time trying an offer that was never applicable to them.

Why I’m sharing it on Product Hunt

I wanted to put CouponLab in front of people who haven’t been staring at it while building it.

When you work on something, you know what you meant. Visitors only know what you put on the page.

That seems obvious until you catch yourself mentally explaining your own interface.

“The user will understand this because—”

Because what? They attended the meetings in my head?

Sharing the project gives me a chance to hear where the explanation is unclear and what people actually care about when choosing a coupon.

I’m especially interested in feedback about trust. A polished page can make an offer look appealing, but what information makes someone feel it’s worth trying?

What I’d love your feedback on

If you have a minute, you can check out CouponLab or visit its Product Hunt page.

The question I’d most like to hear your answer to is:

When you see a coupon, what helps you decide whether to try it: a recent test date, clear restrictions, reports from other shoppers, or something else?

And if you’ve ever abandoned a coupon because understanding it felt like more work than the discount was worth, I’d be interested in that story too.

That’s the experience I’m trying to improve.

Ideally before anyone opens a spreadsheet to save $4.