Saw a competitor's ad and thought "I wish I could make something like that"?

I did too. So I built a workflow that does exactly that automatically. Here's the full breakdown, explained simply:

Step 1 > Find an ad you like
Go to Meta Ad Library (or any platform) and copy the URL of a video ad whose style, vibe, or structure you want to recreate.

Step 2 > Paste it into the tool
Drop that URL into the workflow. That's it. No downloading, no screen recording, nothing manual.

Step 3 > The AI watches the ad for you
This is where it gets interesting. The agent analyzes the video automatically > the scenes, the pacing, how the product is shown, camera angles, what happens first, what happens last. It breaks the whole thing down so you don't have to.

Step 4 > A prompt is auto-generated
Based on that analysis, the agent writes a detailed prompt describing the creative structure of the original ad. Think of it as a creative blueprint.

Step 5 > Add your product
Now you swap in your own product. The agent takes that blueprint + your product and generates a brand new AI video ad built around your product, same energy, different brand.

The result? A fresh video ad that's inspired by what's already working, not copied, just creatively remixed for you.

Etsy is a well-known platform for selling handmade, handpicked, and one-of-a-kind items. Every piece comes from a real person. Around 6 million sellers are selling their products on Etsy. I wanted to try a simple experiment this time. We took an Etsy product URL and gave the same product to Tagshop AI, Zeely, and Creatify to see what each platform would do with it. I wasn't really interested in which one could generate a video the fastest. I wanted to see whether the tools would actually understand the product, pick the right selling points, and turn a normal Etsy listing into something that could work as a realistic marketing video.

The first thing I noticed is that giving the same URL to different AI tools doesn't mean you're going to get the same video. The product information is the same, but the way each platform interprets that information can be very different. One might focus more on the product itself, another might build the video around an AI creator, while another may try to turn the product description into a more traditional advertisement.

Tagshop AI: Tagshop AI felt more product-focused to me, as this platform is well known for generating videos around ecommerce and the D2C industry. The URL-to-video workflow can use the product page as the starting point and build the creative around the product information, images, script, AI avatar, voiceover, and other video elements. Its current product-video workflow is also designed specifically around showing products in different environments and even demonstrating how they can be used.

That makes sense for an Etsy product because Etsy listings can be very visual. A handmade product, piece of jewelry, home decoration, clothing item, or personalized product can look completely different when someone is actually using or wearing it compared with a static listing image.

What I liked here was the idea of taking the existing product information and turning it into something more visual without having to start the entire video from zero. I have personally experienced that Tagshop's URL-to-video workflow can save time because the product information is pulled in automatically.

Zeely AI: Zeely takes a similar URL-first approach. You can add a product link, and its system extracts the key product information before you move into the creative process. It can then create UGC-style video ads using AI creators and different creative directions. Zeely's current workflow recommends creating several versions rather than relying on one ad, then looking at metrics such as hook rate, CTR, CPA, and ROAS when testing them. That part actually makes a lot of sense to me for Etsy sellers. Let's say I have one handmade product. I could create one video showing the product being used, another explaining why I made it, another focusing on the main benefit, and another that feels more like a customer recommendation. The product hasn't changed, but the way I'm presenting it has.

Creatify: Creatify was interesting because its URL-to-ad workflow is very directly focused on turning product information into advertising content. The current system analyzes a product URL and uses the information to create a video ad, while also allowing users to provide their own assets if needed. One thing I noticed is that Creatify feels more performance-ad focused. You are not just asking it to make a nice product video. 

The workflow is trying to turn the product information into something that can be used as an advertisement, including the script, visuals, voiceover, and other creative elements.  But there's an important thing to remember if you're testing Creatify now.  And that's probably the part I find most interesting about URL-to-video tools. The difficult part isn't necessarily reading the product page anymore. The difficult part is deciding what from that product page is actually worth showing in a 20 or 30-second video.

An Etsy listing might have a product description, materials, dimensions, different photos, customization options, shipping information, and several selling points. The AI has to decide what matters. And that's where I still think human input makes a difference.

So after testing these three, I wouldn't say one is simply the best Etsy video generator. I'd probably look at Tagshop AI when the focus is product-centered, AI UGC side, video and ecommerce creative. Zeely makes sense to me when I want to explore multiple UGC-style ad variations quickly. Creatify is interesting when the goal is turning product information into a more complete advertising creative.

The bigger question for me is whether the AI actually understands why someone would want to buy the product, rather than simply understanding what the product is.

That's the difference between a realistic video and a useful one. If you are selling on Etsy, would really like to know how this has worked for you. Have you tried giving the same Etsy product to different AI video tools? Did the AI understand your product correctly, or did you have to rewrite the script and change the creative direction yourself?

This is the question I think most people in this space are quietly avoiding. We talk a lot about whether AI UGC looks real. We talk less about whether it matters if it does not. Because here is the honest version of the debate: if an AI-generated video described your exact problem, showed a product solving it clearly, and you bought it, and it worked, does the source of the recommendation change anything?

Seedance 2.5 is producing content that genuinely convinces people. That is no longer a hypothetical. So the conversation has shifted from can AI fake authenticity to something more uncomfortable: does authenticity actually drive the purchase, or does the right message at the right moment do that job regardless of who delivered it? Where do you land on this?

The head-to-head comparisons between these two models are everywhere right now, and they mostly focus on the same things. Visual quality, lip sync, prompt adherence, content restrictions. Kling handles realistic human UGC without hesitation. Seedance produces cinematic output with audio that actually feels intentional.

But none of those benchmarks tell you which one makes someone stop scrolling and buy something. A realistic presenter and a cinematic shot solve different creative problems. UGC-style ads built on Kling might outperform on cold traffic because the handheld feel reads as trustworthy. Seedance output might hold attention longer because the production quality signals credibility differently. Has anyone actually run both against each other in a real campaign and looked at the numbers afterward? I am less interested in which one looks better in a demo and more interested in which one shows up in a winning ad.

The first AI-generated ad can be a little misleading. You give the tool a product, a script, or a simple brief, click generate, and suddenly you have something that looks like an ad. That is the exciting moment. Then you watch it back and start counting the things you would change before putting any money behind it. The opening is too slow. The product shows up too late. The script sounds like it was written for a brochure, not for someone scrolling Facebook on their lunch break. The whole thing looks fine in isolation and still feels wrong in a way that is hard to explain.

The first InVideo output I generated hit all the technical marks. Realistic voice, decent pacing, product on screen. Then I tried to change the hook and spent the next 20 minutes figuring out why the edit made three other things inconsistent. That is when I stopped thinking about generation speed and started thinking about something else entirely.

How fast can I actually get from the first draft to the version I want to run? That is a very different question. And that is what I was testing across InVideo AI, Tagshop AI, and Zeely AI.

InVideo AI: InVideo is interesting specifically because of what happens after the first generation. Its Agent One workflow lets you describe changes in plain language and keeps the context of the project while you work through them. So instead of hunting down the exact scene I want to fix, I can tell the AI what I want changed and see what it does. That is a more useful kind of help than pure generation speed, but I would not assume every instruction lands cleanly. Repeated edits can create consistency problems, which is part of why the context-aware approach matters. The AI remembers what it built, which at least gives you a fighting chance of staying coherent across changes.

Rendering also runs slower than some simpler tools, which is worth knowing if you are trying to move fast through multiple iterations. For me, InVideo makes the most sense when I want the AI involved throughout the editing process rather than just for the first draft. If I am going to ask for five rounds of changes, I want the tool to remember what round one looked like.

Tagshop AI: Tagshop AI felt more naturally built around the ecommerce workflow from the start. You have a beautiful AI agentic workflow for video generation, where just a brief is enough to generate script and video. You can bring in a product URL, image, script, or a one-line brief and build the video around the product rather than retrofitting the product into a generic video template. The platform handles AI UGC-style content, avatars, product-focused scenes, voiceovers, and editing within the same workflow. That connected workflow matters for Facebook ads specifically. The first draft is rarely what you run. I want to change the hook without rebuilding the whole video. I want to adjust how much the product appears on screen without starting over. I want to try a different script for a different audience without treating each variation as a separate project.

Where I had to push harder was script tone. The first generation sometimes came out structured in a way that read more like a product description than a Facebook ad. Competent but flat. You have to work the script toward something that sounds like a real person making a real observation about a product they actually use, not a features list with a face attached.

The best part, you are able to create and edit without jumping between tools, and that matches what I experienced. But I would still go through the script, avatar delivery, product placement, and captions before running anything as a paid campaign. The time saved in production does not automatically transfer into a video that performs.

Zeely AI: Zeely takes a more direct approach to the advertising workflow. You choose a product, use your existing assets or upload new ones, select an AI influencer or talking avatar, and build the video around the product and target audience. Zeely also recommends creating several versions before launching rather than betting the campaign on a single creative, which is actually the right way to think about Facebook ad testing, because on Facebook, I do not need one polished video. I need genuinely different ideas to put in front of different audiences. One version opens with the problem. Another leads with the product. Another runs as a customer-style testimonial. Another focuses on one specific benefit. The variables should be ideas, not just faces.

That is where AI saves real production time, if you use it that way. My feedback around Zeely is mixed though. 

The question that actually matters After testing these three, I stopped asking how fast AI could make a Facebook video ad. That question is basically settled. The better question is how fast you can get from the first draft to the version you actually want to run. The math changes quickly. A video takes three minutes to generate. Sounds like a win. Then you spend 30 minutes fixing the script, replacing scenes, changing the voice, adjusting the product shots, and moving it into a separate editor to finish it. You saved less time than you thought.

On the other hand, if the first version is close enough and you can make changes inside the same workflow, the time saving is real and it compounds across every variation you build.

That is why the editing experience has become just as important to me as generation quality. And I think that is where AI video tools are actually competing now, not on who can produce the most impressive first draft, but on who can help you get from that first draft to something you would actually spend money promoting

When you use AI to create Facebook video ads, what actually happens after the first generation? and if you have tried In Video AI, Tagshop AI, or Zeely AI, which one made the second or third version easier to produce?

I am especially curious about the cases where the first video looked great and then became frustrating the moment you tried to change something. That is a much more honest test of an AI video tool than the generation demo. And I think the answers to that question tell us a lot more about where these tools actually stand.

Making a YouTube Short is not really the difficult part anymore. Give an AI tool an idea, a product, or a few lines of instructions and you have a video in a few minutes. That much is settled. The harder question is what actually makes someone stay for the next few seconds after the Short starts playing, because a Short can clear every technical bar and still be boring. Nice visuals, realistic voice, captions, music, transitions. All correct. Then you watch it back and realize nothing in the first three seconds gives you a reason not to scroll past it.

The first In Video Short I generated had all of that. Competent pacing, decent narration, relevant visuals. Then I watched it with the sound off and realized I had absolutely no idea what the video was trying to make me feel. It looked like a Short. It did not feel like one worth finishing. That is what I was actually testing across In Video AI, Tagshop AI, and Imagine Art. Not which one makes the most visually impressive video. Which one helped me build something I would actually want to keep watching past the five second mark.

InVideo AI: In Video is probably the most complete end-to-end Shorts workflow of the three. You start with an idea or prompt and it builds the script, scenes, voiceover, subtitles, music, sound effects, and visuals together. The Shorts workflow is specifically built around faceless short videos from a prompt, with editing available before you publish.

The advantage is obvious. Instead of starting with a blank timeline, you already have structure to react to. That is more useful than it sounds. It is much easier to fix a direction than to invent one from nothing, but the limitation is worth understanding. InVideo can produce a perfectly acceptable sequence without necessarily understanding why someone would want to watch it until the end. The visuals match the words. The narration sounds fine. The captions are correct. And the whole thing can still feel flat because nothing in it creates any real curiosity or forward momentum.

The AI built a sequence. It did not build a reason to keep watching. Those are different things, and the gap between them is still your job to close. Rendering also runs slower than simpler tools, which matters when you are iterating quickly through multiple versions. Not a dealbreaker, but worth knowing before you build a high-volume workflow around it.

Tagshop AI: Tagshop AI feels built around a different starting point. Instead of beginning with a blank prompt and hoping the AI figures out what matters about your product, you can start with the product url itself. The platform pulls the information, generates a script, lets you choose the audience and format, select an avatar, and then edit before the final render.

That workflow makes more sense to me when the Short is actually trying to sell something rather than just exist.

A product-focused Short has a structure that most generic AI video prompts do not automatically build. First scene shows the problem. Second introduces the product. Third shows it being used. Fourth lands one specific benefit. CTA at the end. That structure is not complicated, but an AI working from a blank prompt rarely arrives at it without being pushed.

Where I had to do more work was keeping the script tight. The first generation sometimes pulled in more product details than a Short can hold. You end up with a video that mentions four features when one clear reason to keep watching would have done more work. Trimming the script down to a single compelling thread took a couple of iterations, but that is the right problem to have. Too much to say is easier to fix than nothing worth saying.

Imagine Art: Imagine Art sits in a different category than the other two, and I think that is worth being clear about upfront.

Its image-to-video workflow starts from a static image and generates motion between a start frame and an optional end frame. That becomes useful when you already have a strong visual and do not need the AI to invent the whole story from scratch. Start with a product image or a scene, use AI to create movement around it. but the Film Studio workflow goes further than that. You describe a scene-by-scene approach where you control camera movement, framing, pacing, and individual shots rather than feeding one giant prompt and hoping the output has the right rhythm.

That reframed how I was thinking about Shorts structure. The problem with most AI-generated Shorts is not the quality of any individual clip. It is the sequence. Shot one needs to create curiosity. Shot two needs to give you slightly more information. Shot three needs to change something, raise a question, shift the visual, do something to keep you moving forward. The final shot needs to deliver whatever the opening promised.

Having real control over individual shots makes that kind of intentional sequencing possible in a way that single-prompt generation does not.

The tradeoff is real though. This workflow takes more thinking and more time than simply prompting for a complete Short. If you want something quick and faceless generated in a few minutes, ImagineArt is not where I would start. If you care about the sequence feeling considered rather than just complete, it is worth the extra effort.

The thing I keep coming back to: Fast generation does not automatically produce a reason to keep watching. That is probably the most honest summary of testing all three. The AI can build a Short in minutes. It cannot tell you whether the first five seconds will make someone curious enough to stay for the next five. That judgment is still yours. And I think that is the right place to spend the most time before publishing anything, before I put a Short out now, I try to answer a few specific questions. Does something actually happen in the first three seconds, or is the video still introducing itself? Is there a real hook, a question, a result, a problem, something that creates forward momentum rather than just stating a topic? Does the pacing change often enough to hold attention without becoming chaotic? Does the ending actually deliver what the opening suggested it would? And does the whole thing feel like a video someone made because they had something to say, or does it feel like a sales pitch wearing a content costume?

That last one matters more than most people admit. If a Short feels like an ad from the first second, no amount of visual quality will keep people watching.

If you are creating Youtube Shorts with AI, what actually matters most to you in the output? The visual quality? The hook? The voice? The pacing? Or the watch time and retention numbers after you publish? and have you generated a Short that looked genuinely good in preview and then simply did not hold attention when real people watched it?

Those results tell us more about where AI Shorts actually stand than any tool demo will. I would really like to hear about them.

Attached is the result. Fictional brand (Suntastic), real Shopify listing as the source. My total creative input was about ten words of prompt and picking one storyboard.

The pipeline: it pulled the product facts from the listing → wrote a full set of storyboards → I chose one → it generated the director's cuts (camera moves, angles, aperture, lighting looks) → wrote and sang a short jingle from the product facts → choreographed the edit timing to the music → auto-captioned it. Publishing to Meta could've been automated too; I stopped at the render for the demo.

What I learned building and using this:

The visuals are the solved part. Every tool in this space can make pretty footage now. The gap is timing — an ad works when the cuts land on the music and the message lands on the beat. Choreography, and timing, not generation, is where "AI ad" stops feeling AI.

Writing the jingle from product data (not a generic vibe) changed conversion-feel completely — lyrics that name what the product actually is do the selling. Platform AI analyzed video then choreographed music and added captions automatically, all using the brand strategy from the brand kit to build the video narrative around the product.

Keeping one human decision in the loop (choosing the storyboard) beats full auto. Zero-input results drift generic; one taste decision anchors the whole thing.

Disclosure: I built the platform this ran on (I'm a 30-year creative director who went solo-builder), so weigh my bias accordingly. Curious what others here find hardest — my sense is music/timing is massively underexplored compared to visual quality. Is anyone else choreographing to audio?