AI design feedback tools come in two kinds: attention predictors that turn a screen or ad into a heatmap and clarity score, and written critique tools, often Figma plugins, that flag UX problems and suggest fixes. Designers polishing UI usually want critique; marketers testing creative usually want heatmaps.
It's eleven at night, the stakeholder review is at nine, and you've just dragged your new pricing screen into a critique tool to see whether anything obvious jumps out before someone else spots it. That's the job these tools do well: a fast second opinion on a UI, landing page or ad, from either a model that predicts where people will look or one that writes you a list of problems and fixes.
Which of those two you need matters far more than which brand you choose, because they answer different questions. A heatmap tells you what a viewer is likely to notice. A written critique tells you what's wrong with the layout and what to change. An easy mistake is buying one when you needed the other.
What an AI design critique can judge, and what it can't
The attention tools are built on prediction. You upload an image or a URL and get back a heatmap of where eyes are likely to land, sometimes a scan path showing the order they'll move in, and scores for clarity or focus. Some go further and claim to predict emotion and memory, or benchmark one piece of creative against others. For a hero banner, a social ad or a product page above the fold, that's useful: you'll see straight away if the eye goes to the stock photo and skips the headline.
The written critique tools work on structure instead. They read a frame and come back with UX issues to look at, often with a suggested fix attached, so you're reading notes rather than colours.
Where it all runs out is meaning. A heatmap predicts a typical viewer; it doesn't know your brief, so it can't tell you whether the thing people look at first is the thing you wanted them to look at. That judgement stays with you. And a prediction is still a prediction. If the question is whether people can complete a task, such as finding the cancel button or getting through checkout, you need people. That's the territory of usability testing tools that put real users in front of your screens, and nothing on this list replaces it.
I'd treat AI critique as a pre-flight check. It catches the embarrassing stuff before a human reviewer does, and it gives you something concrete to point at when a stakeholder says the page "feels busy".
Critique while you're still designing
UX Pilot
A Figma plugin and web app whose AI review flags UX issues, suggests a fix and can apply it to the frame for you.
DesignCrit.ai
Lives inside Figma and gives quick AI feedback on whatever you're working on, without leaving the canvas.
Uizard
For teams who'd rather generate and test UI in one place, this AI design tool includes a predictive heat map of where users will look.
Attention heatmaps for finished screens, ads and landing pages
Attention Insight
Upload an image or URL and get an attention heatmap, clarity and focus scores, a contrast map and AI recommendations.
Clueify
Smaller hosted service returning attention maps, areas of interest and a clarity score from an upload or a plugin.
Brainsight
Predictive eye-tracking heatmaps, scan paths and AI creative audits, aimed at ad teams and UX designers checking work before launch.
Feng-GUI
A visual analytics service that predicts attention, clarity and excitement across websites, ads and video.
Dragonfly AI
An enterprise creative testing platform: it predicts visual attention and benchmarks how effective a piece of creative is likely to be.
Neurons
Built for designers and marketers who want predicted attention, emotion and memory responses to their creative assets.
What a second opinion on a screen costs
Before you weigh up the forks, know how the bill is built, because it decides more than the feature list. Attention tools tend to charge by volume: some sell credits where one credit tests one image, others give you a set number of analyses each month.
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On the vendors' own pricing pages this month, the tools that show a price run from the low tens of dollars a month, billed annually, up to a little over two hundred euros a month for a team plan built around ad and UX heatmaps. Feng-GUI, Dragonfly AI and Neurons don't list a figure, so you'll be talking to sales. At the cheap end, check what the allowance covers: a plan built on a handful of tests a month disappears fast once you're comparing variants.
Three choices that shape your design review setup
Heatmap or written critique. If you're judging creative where the only question is whether the eye lands on the right thing, such as an ad, a banner or a hero section, an attention predictor is the faster read. If you're working on product UI with forms, navigation and states, written critique is more useful, because it tells you what to change rather than where the eye went.
Inside Figma or a separate upload. A plugin keeps critique in the place you design, so you'll use it on every frame rather than saving it for the end. A standalone upload tool takes an exported image, and some take a live URL too, so it'll work on a competitor's page or a Canva export. Marketers who don't open Figma will want the upload route; product designers will mostly prefer the plugin.
Pay per test or pay per seat. Credit and per-analysis pricing suits a freelancer or a small team testing a few hero images before a launch. Once several people are testing variants every week, an allowance-based plan runs dry and a team subscription starts to make sense.
Two pitfalls sit across all three choices. The first is reading a heatmap as a verdict. A bright patch on your logo isn't a failure if the logo is meant to be seen; the score only matters against what you wanted the screen to do, so write that intent down before you upload. The second is testing a single version. A clarity score on its own tells you very little. Run the original and one rival layout through the same tool and compare them, because the difference between two scores is far more telling than either number alone.
Here's how I'd run that comparison before a review, and it fits comfortably inside an hour. Write one line saying what a viewer should notice first, say the headline and then the sign-up button. Export your current layout and a rival where you've changed one thing, such as moving the button up or cutting the background image. Put both through the same tool, then check which element leads on each. Fix whatever the losing version got wrong, run it again, and take the winner into the meeting with both results side by side.
While you're doing it, judge the critique itself. A useful note names the element, says what's wrong with it and why, and points to a change you can make in minutes: the price and the button compete for attention, so drop the price's weight and let the button lead. A useless one could sit on any screen ever made: "improve the visual hierarchy", "consider the user's needs", "make the call to action stand out". If a tool keeps giving you the second kind on your real work, it won't earn a place in your routine, however nice the heatmap looks.
And if the feedback you're after comes from clients and colleagues rather than a model, that's a different kind of tool entirely: visual annotation and bug reporting. These visual feedback tools cover that side.
Do you make AI design feedback or critique tools? Tool makers can ask to be considered for a featured place on this list.
Where I'd start with AI design critique
If you're a product or UX designer living in Figma, start with a critique plugin and run it on every key frame before review. It's the habit that pays off, because you'll fix hierarchy and spacing issues while they're still cheap to fix.
If you're a marketer or creative lead signing off ads and landing pages, go for a hosted attention predictor that takes uploads and URLs, and use it to compare two versions rather than to grade one. Credit pricing is fine while you're testing occasionally; move to a team plan once testing is a weekly routine.
If you're a larger brand team that wants attention, emotion and memory predictions benchmarked across a whole campaign, the enterprise creative testing platforms are built for that, and pricing there starts with a conversation.
And if you're a founder who also wants help making the screens in the first place, a design tool with a heat map built in covers both jobs at a starter level. When you outgrow it, my guide to other AI UI design tools is the next stop.
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