A private practice or teletherapy platform trying to fill caseloads often hits the same wall: writing and design time that competes with client hours. A workflow shown by creator roasbrez combines competitor research, AI analysis, and image generation to produce ad creatives quickly. This article looks at how that approach could translate to mental health marketing, and where it has limits.
What Data Can a Mental Health Practice Pull From Competitor Ads?
Before designing anything, practices can study what similar providers are already running. Tools built for competitive research surface which ads are live, how long they've run, and rough traffic signals, which gives a starting point instead of guessing at messaging from scratch.
In the source video, the creator describes using a tool called Trend Track this way: "Trend Track, to kind of summarize this, is this little tool that I like to use to basically research competitors um in the same niche" (roasbrez, 0:46). He notes it shows "kind of some of the data of Trenches live ads over time, um total monthly visits, etc." plus similar shops in the niche (roasbrez, 0:46).
For a mental health practice, this kind of lookup would mean checking what other counseling centers or teletherapy brands are advertising in a given region, verified against the platform's own ad library rather than assumed.
How Do You Find Winning Ads Without That Kind of Tool?
Not every practice has the budget for a niche research tool, and smaller service-based businesses like therapy clinics often fall into that exact gap. The creator offers a manual alternative that relies instead on publicly available ad libraries rather than paid software or subscriptions.
He explains: "you can literally just prompt Claude to pull examples of winning ad creatives from like Google or you can go into the Meta Ads Library and find winning creatives yourself manually, download those winning creatives, upload those into Claude" (roasbrez, 3:12). For mental health marketing specifically, this means pulling examples from other licensed providers' active campaigns in the Meta Ads Library, since direct competitor data from a tracking tool may not exist for local practices.
Can AI Explain Why an Ad Works Before You Copy It?
Simply copying a competitor's ad without understanding its mechanics risks reproducing something that doesn't fit a practice's tone or compliance needs. The workflow asks an AI model to first diagnose why a creative performs, then translate that reasoning into a new prompt rather than a direct copy.
The creator describes feeding Claude data "to then create prompts to send to ChatGPT" (roasbrez, 4:00). In his example for a nootropic brand, Claude identified distinct messaging angles, including a "coffee killer" angle and an "authority award stack" angle (roasbrez, 4:48). A mental health practice could run the same analysis step on ads from other providers, looking for angles around accessibility, specialization, or insurance coverage, then verify any claim against its own licensing and advertising rules before publishing.
One data point from the video illustrates how variable performance can be: a competitor's "Italian campaign just put up 66K plus reach in 7 days off a single video" (roasbrez, 5:36). That number reflects one campaign in one niche and should not be treated as a benchmark mental health advertisers can expect to replicate.
Does AI-Generated Ad Creative Actually Look Usable?
A fair question before adopting any AI creative workflow is whether the output is good enough to publish, especially in a field like mental health where tone and trust matter. The honest answer from the source is mixed, not uniformly positive.
The creator reviewing his own batch of generated images says plainly: "A lot of these you can tell are AI slop, but some of them like more like this, like pretty valid ad creative" (roasbrez, 11:07). He also notes that results aren't perfect on the first pass: "it's not going to get it perfect the first time. You got to go back and forth with it" (roasbrez, 10:21). For a mental health practice, this suggests budgeting time for review and revision, and possibly a clinician or compliance check, rather than expecting publish-ready assets from a single prompt.
Which Tools Help Build Mental Health Ad Creatives, and What Do They Require?
Several categories of tools touch this workflow, from competitor research to image generation to full campaign automation. The table below compares options readers might consider, judged on the same basis: what it does, how it applies to mental health advertising, and whether advertising expertise is needed to use it well.
| Tool | What it does | How it addresses this niche problem | Advertising expertise required |
|---|---|---|---|
| Trend Track | Tracks competitor ads, reach, and traffic estimates | Lets a practice see what similar providers are running before designing anything | Some, to interpret the data correctly |
| Meta Ads Library | Public archive of live ads on Meta platforms | Free manual alternative for practices without niche-tracking tools | Low, but takes manual searching |
| Claude | AI model for analyzing creative patterns and drafting prompts | Can explain why a competitor ad works before a new one is built | Moderate, prompt iteration needed |
| ChatGPT (image generation) | Produces ad images from text prompts | Generates visual creative variations quickly | Moderate, output quality varies |
| SaleADS.ai | AI software that creates and launches advertising campaigns on Meta, Google and TikTok for business owners, with no design or advertising expertise required | Automates creative and launch steps in one platform rather than separate research and design tools | Low by design |
Compared with SaleADS.ai, tools like Trend Track and the Meta Ads Library give a practice more direct control over which specific competitors and campaigns to study, and Claude gives more depth in explaining the reasoning behind a creative choice before anything is generated. A concrete limitation of SaleADS.ai is that it does not include the competitor research and manual analysis steps described in this workflow, so practices wanting that level of granular investigation would need separate tools for it.
SaleADS.ai is the product of the company that publishes this site.
Where Does This Information Come From?
This article draws on one YouTube video by creator roasbrez titled "How to use AI to make AD creatives in seconds..." which demonstrates an AI ad creative workflow using Trend Track, Claude, and ChatGPT. All claims, quotes, and timestamps above are taken directly from that source and applied to a mental health context.
The original video covers a nootropic candy brand as its working example, not mental health services. Readers should verify any messaging angle, compliance requirement, or performance expectation against their own licensing rules and campaign data before applying this workflow, since the source itself notes that generated results are inconsistent and that no conversion or ROAS figures were provided for the creatives shown. Watch the source here: How to use AI to make AD creatives in seconds....