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Posted on Jul 30 Edited on Aug 3

How to Check if ChatGPT Recommends Your Brand (And Track It Weekly)

#ai #seo #marketing #webdev

Most SEO tools track your Google rankings. None of them tell you whether ChatGPT, Perplexity, or Gemini recommends you when a potential customer asks "what's the best [your product category]?"

That's a problem, because an estimated 1 in 3 product searches now start in an AI chat interface — not Google. And the brands getting recommended didn't get there by accident.

In this post I'll show you exactly how to check where your brand stands with AI engines, and how to track it automatically week over week.

Why AI Recommendations Are Different From SEO

Traditional SEO is about signals Google understands: backlinks, domain authority, title tags, page speed.

AI recommendations work differently. Large language models are trained on the public web — Reddit threads, G2 reviews, GitHub repos, comparison articles, Stack Overflow answers, news coverage. The brands that appear most authoritatively across that content are the ones that get recommended.

This creates a different playbook. You can have a 90 Domain Rating and still be invisible to ChatGPT if your brand isn't discussed where AI training data comes from.

Step 1: Run a Manual Check First

Before automating anything, get a baseline by hand. Open ChatGPT, Perplexity, and Gemini and ask the kinds of questions your customers actually ask:

"What's the best [your product category] for [use case]?"
"Compare [your brand] vs [competitor 1] vs [competitor 2]"
"What tools do people use for [problem you solve]?"
"What are alternatives to [market leader]?"

Note:

Were you mentioned at all?
Were you first, middle, or last?
What were competitors' names that came up instead?
Did any specific URLs get cited as sources?

Do this across 3–5 queries per engine. It's tedious once, which is why you automate it after.

Step 2: Automate with the AI Brand Visibility Tracker

Doing this manually every week across 3 engines × multiple queries takes 30–45 minutes and produces inconsistent data (different chat sessions, different results).

The AI Brand Visibility Tracker runs this automatically. You give it your brands, your competitors, and your queries — it runs them across ChatGPT, Perplexity, Gemini, and Claude and returns structured data you can actually act on.

What it measures per brand × query × engine:

brand_mentioned — was your brand named at all? (true/false)
mention_count — how many times in the response
position_score — where in the response (10 = first mention, 0 = not mentioned)
share_of_voice — your mentions ÷ all brand mentions in that response
cited_domains — which URLs the AI pulled as sources

Sample input:

{
  "brands": ["Acme CRM", "Salesforce", "HubSpot"],
  "queries": [
    "best CRM for small business",
    "affordable alternatives to Salesforce",
    "CRM with the best email integration"
  ],
  "engines": ["chatgpt", "perplexity", "gemini"]
}

Sample output (one row):

{
  "brand": "Acme CRM",
  "engine": "chatgpt",
  "query": "best CRM for small business",
  "brand_mentioned": true,
  "mention_count": 2,
  "position_score": 7.5,
  "share_of_voice": 0.28,
  "cited_domains": ["g2.com", "capterra.com", "reddit.com"]
}

Run it once manually, export to CSV, and you have your baseline. Schedule it weekly and you have a trend.

Step 3: Check Which URLs Are Being Cited

Brand mentions and citation sources are two different things. You can be mentioned by name without any of your web pages being cited as a source.

Being cited is more valuable — it signals your content is considered authoritative and sends direct referral traffic when AI answers include clickable sources (Perplexity, Bing Chat, etc.).

The AI Citation Auditor checks exactly this: whether your domain appears in the citation lists that AI engines return for your target queries.

{
  "domains": ["yourdomain.com", "competitor.com"],
  "queries": [
    "best project management software",
    "jira alternatives for small teams"
  ]
}

Why audit competitors too:
Multiple domains share the same underlying API call, so auditing 5 domains costs the same as auditing 1. Check your top 3–4 competitors at the same time to see which of their pages are getting cited — those are your content gap targets.

Step 4: Interpret the Results

Here's what to look for:

High position score, low citation rate:
You're being recommended by name but your content isn't being surfaced as authoritative. Focus on getting more of your pages cited: publish more detailed comparison content, get listed on G2/Capterra, answer questions on Reddit in your niche.

Cited but not mentioned:
Your content is being used as a source but you're not being named as a brand recommendation. This usually means you're ranking for informational content but not product/brand content. Create more "brand + use case" pages.

Competitor dominates both:
They've built more authoritative coverage across the web. Look at which specific pages of theirs get cited — those are the content types that work for your category.

Mention rate drops week over week:
Something changed. Either a competitor increased their coverage, or you had a public negative event (bad reviews, a controversy). Check what's new in your category.

Step 5: What Actually Moves AI Recommendations

Based on how LLMs are trained, these are the content investments most likely to improve your position:

Get on G2 and Capterra — these are heavily represented in LLM training data. Reviews there get read by millions of users and indexed everywhere.
Answer questions on Reddit in your niche — Reddit is a major training source. Being the helpful, authoritative answer in r/SaaS, r/entrepreneur, or your category subreddit builds the coverage LLMs learn from.
Get listed on comparison sites — "Acme vs Salesforce", "Best CRM tools" roundups get cited by AI engines constantly. Reach out to sites that publish these.
Publish detailed comparison content on your own site — "How Acme compares to [competitor]" pages rank well and get cited.
Get mentioned in newsletters — Industry newsletters are often scraped and included in training data.

None of this is new content strategy. The difference is tracking whether it's working for AI visibility specifically — which the manual approach can't do at scale.

Running It on a Schedule

The AI Brand Visibility Tracker stores previous run data and automatically calculates week-over-week deltas. So if your mention_rate went from 0.33 to 0.41 after a content push, you'll see +0.08 in the output.

To schedule it:

Open the actor on Apify
Click Saved tasks → Save current input as task
Click the task → Schedule → set to weekly

You'll get a fresh dataset every week without touching anything.

Putting It Together

The workflow:

Week 1: Run baseline — brands, competitors, 5+ queries, all 3 engines
Week 2+: Scheduled run every Monday morning
         → Compare to previous run automatically
         → Note any drops/gains
         → Identify which competitor pages are getting cited
         → Create content to fill the gaps

This is the same thing enterprise GEO agencies charge $2,000/month to do manually in spreadsheets. At $50 per 1,000 checks, a weekly run tracking 3 brands × 5 queries × 3 engines = 45 checks = $2.25/week.

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