AI Product Ads: From Your URL to Campaign Drafts

Paste the URL of your AI product, whether that is a consumer AI app, an AI tool sold to professionals, an AI feature inside a bigger product, or the whole company if you are AI-native, and Infinall reads it, researches who is most likely to buy, and drafts a campaign: a strategy, ad copy, and image ads and carousels. It writes around the word "AI" rather than leaning on it, because that word no longer sells anything on its own. Everything lands in an Approval Queue you edit and approve before you export it and run it in your own ad account.

Advertising an AI product has one problem that sits above all the others: the phrase "AI-powered" has stopped carrying information. Every product page, every app store listing, and every competitor ad now says some version of it, so a buyer reading your headline learns nothing they did not already assume. The word that felt like a differentiator two years ago is now the ad equivalent of saying your software runs on a computer. An ad that leads with it is spending its most valuable line on a claim the reader will skip. What replaces it is uncomfortably specific. The ad has to name the exact job the model does, for a named person, in language that person already uses. Not "AI writing assistant" but the particular thing it writes and the particular hour of work it removes. Not "AI for your photos" but the specific edit, on the specific kind of photo, that people currently pay someone else to do. That level of specificity feels risky, because it narrows the audience on paper, but it is the only thing that reads as real to someone who has already scrolled past a dozen generic AI ads today. The second problem is that your buyer is curious and sceptical at the same time, and both feelings are pointed at accuracy. People want the thing to work, and they have also used a model that confidently produced something wrong. So the objection is not "why would I need this" but "how often does it get it wrong, and what happens when it does". An ad that ignores that question loses the click to the competitor whose ad answers it. An ad that overclaims wins the click and loses the customer at first use, which is worse, because in this category refunds and one-star reviews travel fast. That is why showing beats describing. An AI product is easier to sell by displaying an actual input and the actual output next to it than by any adjective you could attach to the model. A real screen, a real prompt, a real result, even an unglamorous one, does more work than a stylised image of a glowing brain or a humanoid robot, and those images are so common now that they read as a signal the advertiser has nothing concrete to show. Creative that shows the product working also sets accurate expectations, which quietly protects retention after the click. The third problem is speed of category change. Positioning that was accurate in the spring can be stale by autumn, because a foundation model release, a platform adding your feature natively, or three funded competitors launching the same month can all move what counts as differentiated. Whatever gap you are advertising into needs rechecking on a much shorter cycle than most categories require, and the campaign you wrote once and left running is usually arguing against a version of the market that no longer exists. Put together, this is why so much AI advertising looks identical. The safe, abstract, technology-first ad is easy to write and gets ignored; the specific, output-first, honestly bounded ad is harder to write and is the one that converts. It also has to be rewritten more often than you would like.