A DTC founder I talked to last month runs a $4M/year skincare brand. Great product, decent SEO, growing paid social. She'd noticed her direct traffic was flatlining while her category was growing, and she had a hunch: buyers were asking ChatGPT what to buy, and ChatGPT wasn't saying her name.
So she typed the obvious prompt: "What are the best clean skincare brands for sensitive skin?"
ChatGPT gave her seven brands. Hers wasn't one of them. She tried three more variations. Same result. One of the brands ChatGPT kept naming had one-tenth her review count on Sephora.
That's the moment most ecom operators find AI visibility. It's not a strategic epiphany. It's a punch in the stomach.
The good news: checking whether AI recommends you is easier than fixing it. The bad news: the way you check dramatically changes what you learn. Type one prompt and you'll get a wildly misleading picture. Automate 500 prompts across four models and you'll get something usable.Here are the five methods, ranked from free-and-flawed to paid-and-precise. Pick the one that matches how seriously you're treating this.
Method 1: Just Ask ChatGPT Yourself
Cost: $0. Accuracy: low. Time: 10 minutes.Open ChatGPT. Type the query a buyer would type. Read the answer. That's it.
This is where every founder starts, and honestly, you should start here too. Not because it's accurate, but because it's visceral. Seeing your brand missing from an answer to "best running shoes under $150" hits different than reading a dashboard.
The prompts that matter are the ones a real buyer would type. Not "tell me about [My Brand Name]" — of course ChatGPT knows about you if you name yourself. The real question is whether it names you when you don't. So use category prompts:
- "What are the best [category] for [use case]?"
- "I'm looking for a [product] under $[price]. What do you recommend?"
- "Compare the top [category] brands in 2025."
- "What's a good alternative to [big competitor]?"
First, ChatGPT's output is non-deterministic. Ask the same question three times and you'll get three different lists. Miss on one prompt doesn't mean you're not being recommended. Hit on one prompt doesn't mean you are.
Second, your ChatGPT sees you. If you're logged in, memory is on, and you've ever mentioned your own brand, its answers are contaminated. Use a logged-out session or incognito.
Third, you're testing one model. ChatGPT has ~60% of the AI assistant market, but Gemini, Perplexity, Claude, and Google's AI Overviews all handle queries differently. A brand that dominates on ChatGPT can be invisible on Perplexity, which weights recent citations far more heavily.
Manually checking one model is like checking your Google ranking by typing one query while logged into your own account. It's a vibe check, not data.Use this method to confirm you have a problem. Don't use it to measure progress.
Method 2: Systematic Manual Prompting (The Spreadsheet Method)
Cost: $0. Accuracy: medium-low. Time: 3-5 hours per pass.Same idea as Method 1, but disciplined. You build a list of 25-50 prompts a buyer might actually type, run each of them 3 times across ChatGPT, Gemini, and Perplexity (all logged out), and record who gets mentioned in a spreadsheet.
Structure it like this:
| Prompt | Model | Run 1 | Run 2 | Run 3 | You mentioned? | Competitors mentioned |
|---|---|---|---|---|---|---|
| "best clean skincare for sensitive skin" | ChatGPT | ❌ | ❌ | ✅ | 1/3 | Drunk Elephant, Krave, Youth to the People |
| "best clean skincare for sensitive skin" | Gemini | ❌ | ❌ | ❌ | 0/3 | Cetaphil, Vanicream, La Roche-Posay |
| "best clean skincare for sensitive skin" | Perplexity | ✅ | ✅ | ❌ | 2/3 | Krave, Tower 28, Ilia |
The downside is obvious: this takes half a day, and it decays instantly. The models update constantly. Perplexity re-crawls the web in near real-time. A snapshot from three weeks ago tells you nothing about today.
Do this once to establish a baseline. Then don't do it again. Automate.
Method 3: Browser Extensions and Free "AI Rank Checker" Tools
Cost: $0-$29/month. Accuracy: medium. Time: 30 minutes setup.There are now a dozen free-or-cheap tools that scrape ChatGPT for brand mentions. Some are browser extensions that sit on top of your ChatGPT interface. Others are lightweight web apps where you paste in prompts and get back a mention count.
They're better than the spreadsheet method for two reasons: they run the same prompt multiple times automatically (usually 5-10), and they parse the output to detect brand mentions without you having to eyeball it. You get a "mention rate" percentage instead of a manual tally.
But most of them only cover ChatGPT. A few add Perplexity. Almost none cover Gemini or Google AI Overviews, which is a real problem because Google AI Overviews now appear on roughly a quarter of US search queries and directly cannibalize the organic clicks you used to get.The other issue is source visibility. When ChatGPT mentions you, why did it mention you? Which sources did it cite? A good tool tells you the mention came from a Reddit thread on r/SkincareAddiction and a listicle on Byrdie. A cheap tool just tells you "you were mentioned." The first is actionable. The second is trivia.
Some free tools worth trying if you're curious: the free tier of most AI visibility platforms will let you run a scan on a handful of prompts before asking for money. Use them as a supplement to methods 1 and 2, not a replacement.
A mention count without a source is a score without a scoreboard. You know the number, but you don't know how to change it.Method 4: Custom Scripts with the OpenAI, Anthropic, and Perplexity APIs
Cost: ~$50-200/month in API credits. Accuracy: high. Time: 1-3 days to build, ongoing maintenance.If you have a developer on your team (or you're technical enough yourself), you can build this. The APIs are cheap. GPT-4o costs fractions of a cent per query. Running 500 prompts across four models every week costs less than a coffee subscription.
The setup:
- Build a prompt library of 100-500 buyer queries in your category.
- Loop each prompt through the OpenAI API, Anthropic API, Google Gemini API, and Perplexity API. Run each 5x for statistical significance.
- Parse the responses for brand mentions using regex or an LLM call.
- Log everything to a database with timestamps.
- Build a dashboard on top (Retool, Metabase, whatever).
You also have to handle the parsing problem. When ChatGPT says "brands like Drunk Elephant and similar clean beauty labels," does "similar clean beauty labels" count as a mention of you? Different parsing rules give you different numbers. Consistency matters more than accuracy, but you have to pick a rule and stick to it.
If you're a technical founder who genuinely enjoys building infrastructure, do this. You'll learn a lot. If you'd rather spend that time on your business, skip to Method 5.
Method 5: Purpose-Built AI Visibility Platforms
Cost: $50-500/month depending on scale. Accuracy: high. Time: 15 minutes setup.This is the category Foxish sits in, so treat this section with the appropriate salt. But there are real reasons this is where serious operators land, and it's worth being honest about what the category does and doesn't do.
A good AI visibility platform gives you five things you can't easily get anywhere else:
One, coverage. Not just ChatGPT. Gemini, Perplexity, Claude, and Google AI Overviews, all measured on the same prompt library, so you can compare apples to apples. If you're only measuring ChatGPT, you're missing the model that's growing fastest (Perplexity) and the one that's already touching your existing SEO traffic (AI Overviews). Two, source attribution. Every mention comes with the sources the model cited. This is what turns "you got mentioned 40 times this month" into "you got mentioned 40 times, and 28 of them cited a single Reddit thread." Now you know what to double down on. Three, competitor benchmarking. Your mention rate in isolation is meaningless. Your mention rate versus the three brands you actually lose customers to is the entire game. You need to see the gap and watch it close. Four, longitudinal tracking. AI models change constantly. A snapshot is worthless. What you need is a chart showing your mention rate trending up (or down) over weeks and months, with annotations for what changed. A platform gives you that automatically. Five, click-side data. Being mentioned by AI isn't the finish line. Getting the click, and eventually the sale, is. A good platform correlates AI mentions with the referral traffic you're actually receiving from ChatGPT, Perplexity, and Gemini, so you know whether visibility is translating to revenue. Being recommended by AI isn't the finish line. Getting the click is.The trade-off is money. You're paying $50-500/month for something you could theoretically build yourself. For most brands doing $500k+/year, that math is trivially good. For a brand doing $50k/year, it's harder to justify — start with Method 1 or 2 and graduate up.
The Comparison, In One Table
| Method | Cost | Setup Time | Ongoing Time | Models Covered | Source Attribution | Reliable for Tracking? |
|---|---|---|---|---|---|---|
| 1. Just ask ChatGPT | $0 | 0 min | 10 min/check | 1 | No | No |
| 2. Spreadsheet method | $0 | 30 min | 3-5 hrs/pass | 3 | No | Weakly |
| 3. Free tools / extensions | $0-29/mo | 30 min | 30 min/check | 1-2 | Sometimes | Weakly |
| 4. Custom API scripts | $50-200/mo | 1-3 days | 2-5 hrs/week | 4+ | If you build it | Yes |
| 5. Purpose-built platform | $50-500/mo | 15 min | ~0 | 4+ | Yes | Yes |
What Most Founders Actually Do
They start with Method 1, panic, then jump straight to Method 5. Methods 2, 3, and 4 exist in theory, but in practice most founders don't have the discipline for the spreadsheet, the patience for the free tools, or the engineering appetite for the API build.That's fine. What matters is not which method you use, but that you're measuring the right thing on a consistent schedule. Measuring ChatGPT mentions once, in a panic, on a Tuesday afternoon, tells you almost nothing. Measuring mention rate across four models on 200 prompts every week for six months tells you exactly what to do next.
The pattern to watch for: mention rate slowly climbing, tied to specific content investments. You publish a comparison article on Reddit → three weeks later, ChatGPT starts citing it → your mention rate on "alternatives to [competitor]" goes from 12% to 34%. That's the feedback loop. Everything else is vanity.
One more thing worth saying: don't optimize for mentions alone. Mentions without links are half a win. When Perplexity names you, does it link to your site? When ChatGPT recommends you, does it cite a source that sends traffic? A rising mention rate with flat referral traffic means the model is talking about you but not sending buyers your way. That's a different problem than invisibility, and it needs a different fix.
Foxish tracks all four major models (ChatGPT, Gemini, Perplexity, Google AI Overviews) on your prompt library, gives you source attribution for every mention, benchmarks you against the competitors you actually care about, and pairs mention data with the referral traffic your site is receiving. Setup takes about 15 minutes. The free plan runs your first scan and shows you where you stand — no credit card. Start a free scan →