DEV Community: dbott23

How Booking.com anti-bot works — and how I built a scraper that gets past it

dbott23 — Thu, 20 Aug 2026 13:48:27 +0000

Booking.com serves over 28 million listings and is one of the most valuable sources of hotel review data on the internet. It's also one of the hardest sites to scrape. I spent a week figuring out why standard approaches fail and how to get the data anyway. Here's everything I learned.

Why headless Chromium fails immediately

The first blocker is AWS WAF sitting in front of Booking.com's CDN. It fingerprints incoming browser connections at the TLS handshake level — JA3/JA4 signatures, HTTP/2 header ordering, cipher suite preferences — and Chromium's fingerprint is on the blocklist. You get a 403 before the page even loads.

The fix is camoufox, a Firefox fork that patches the browser's fingerprinting surface. Firefox's TLS fingerprint passes the WAF check where Chromium's doesn't. But camoufox alone isn't enough — Booking.com's WAF also checks whether the browser is running in a headless environment. The solution is Xvfb (X Virtual Framebuffer): spin up a virtual display, launch Firefox inside it, and the browser believes it's rendering to a real screen.

import subprocess
display = subprocess.Popen(['Xvfb', ':99', '-screen', '0', '1280x720x24'])
os.environ['DISPLAY'] = ':99'
# now launch camoufox — it sees a real display

This combination passes the WAF challenge and gets you the page load.

The second blocker: the browser context kill switch

You've passed the WAF. The page starts loading. And then, about 18–30 seconds later, the browser context closes. Booking.com's client-side JavaScript runs fingerprinting checks after the page hydrates and closes the connection if it doesn't like what it sees.

My first approach was to wait for JS hydration and parse the DOM. That doesn't work — the window closes before hydration finishes. My second attempt was to intercept XHR/fetch calls for the review API. Those calls never fire either.

The key insight came from looking at what actually arrives in the HTTP response.

The real data is in the initial HTML response

Booking.com uses a micro-frontend architecture called Capla, built on Apollo Client. When the server renders the page, it embeds the full Apollo GraphQL cache as JSON inside <script type="application/json"> tags in the HTML. This includes hotel metadata, amenities, pricing, photos — and reviews.

The response body is about 1.7MB. Your browser downloads it in full the moment the server responds, before any JavaScript runs, before any anti-bot checks fire. The data you want is already there.

The trick is to capture it. Playwright's response.body() lets you read the raw HTTP response body from inside a network event listener:

raw_html_bodies = []

async def on_response(response):
    if "booking.com/hotel/" in response.url and response.status == 200:
        try:
            body = await response.body()
            body_str = body.decode("utf-8", errors="replace")
            if len(body_str) > 20_000:  # skip tiny redirect responses
                raw_html_bodies.append(body_str)
        except Exception:
            pass

page.on("response", on_response)
await page.goto(url)
# don't wait for page load — use the captured body immediately
html = max(raw_html_bodies, key=len)

You register the listener before navigation, collect the bodies as they arrive, then take the largest one. By the time the anti-bot kill switch fires, you already have everything you need.

Parsing the Apollo cache

The Apollo cache is a flat object where keys are GraphQL entity identifiers like FeaturedReview:123456 and values are the actual objects. References between objects use {"__ref": "TypeName:id"} pointers.

def _parse_capla_json_scripts(html: str) -> list:
    results = []
    pos = 0
    while True:
        start = html.find('<script', pos)
        if start == -1:
            break
        tag_end = html.find('>', start)
        tag_text = html[start:tag_end + 1]
        pos = tag_end + 1
        if 'application/json' not in tag_text:
            continue
        close = html.find('</script>', pos)
        blob = html[pos:close].strip()
        pos = close + 9
        try:
            results.append(json.loads(blob))
        except Exception:
            pass
    return results

def _build_apollo_cache(parsed_scripts: list) -> dict:
    cache = {}
    for blob in parsed_scripts:
        if not isinstance(blob, dict):
            continue
        for k, v in blob.items():
            # Apollo cache keys look like "TypeName:id"
            if isinstance(v, dict) and ":" in k and v.get("__typename"):
                cache[k] = v
    return cache

To find the actual reviews, filter cache entries by __typename. Booking.com's Capla schema uses FeaturedReview for the reviews embedded in the SSR response. Each object has positiveText, negativeText, score, roomType (as a __ref pointer), completedAt (Unix timestamp), and traveller info.

REVIEW_TYPENAMES = frozenset({
    "FeaturedReview", "GuestReview", "PropertyReview", "Review",
})

reviews = [
    v for k, v in apollo_cache.items()
    if v.get("__typename") in REVIEW_TYPENAMES
    and (v.get("positiveText") or v.get("negativeText"))
]

The roomType ref resolves by looking up the key in the same cache:

def resolve_ref(val, cache):
    if isinstance(val, dict) and "__ref" in val:
        return cache.get(val["__ref"])
    return val

room_obj = resolve_ref(review.get("roomType"), cache)
room_name = room_obj.get("name") if room_obj else None

What you get

Each hotel page embeds approximately 10 featured reviews — Booking.com's curated selection of the most recent and highest-quality reviews shown to prospective guests. Getting the full review history (thousands of reviews per hotel) would require hitting Booking.com's internal GraphQL API, which is a different problem.

For competitive intelligence, sentiment analysis, and reputation monitoring use cases, the featured reviews are the most useful anyway — they're the ones influencing booking decisions.

The full implementation is available as a ready-to-run actor on Apify: Booking.com Hotel Reviews Scraper. Pass one or more hotel URLs, get a clean dataset of reviewer name, score, positive/negative text, room type, trip purpose, and date. Priced at $1 per 1,000 reviews extracted.

Summary

Problem	Solution
AWS WAF blocks Chromium	Use camoufox (Firefox) + Xvfb virtual display
Anti-bot JS closes browser in 18–30s	Capture raw HTTP response body before JS runs
Data isn't in DOM, no API calls fire	Parse Apollo/Capla SSR JSON cache from `<script>` tags
`roomType` is a `__ref` pointer	Build a flat cache lookup and resolve references
`completedAt` is a Unix timestamp	`datetime.fromtimestamp(ts, tz=timezone.utc).strftime("%Y-%m-%d")`

The pattern — WAF bypass with a fingerprint-clean browser, capture before hydration, parse SSR cache — applies to any site built on Apollo Client with server-side rendering. More and more modern travel and e-commerce sites use this stack, so it's a pattern worth knowing.

How to Export All Google Maps Reviews to a Spreadsheet (2026 Guide)

dbott23 — Mon, 03 Aug 2026 02:18:41 +0000

How to Export All Google Maps Reviews to a Spreadsheet (2026 Guide)

If you've ever needed every review for a business on Google Maps — your own locations, a competitor's, or a whole list of them — you've probably discovered there's no "Export to CSV" button. Google gives you an infinite-scroll list on the page and nothing else.

This guide covers three ways to get those reviews into a spreadsheet, from most tedious to most painless, and is honest about the trade-offs of each.

What you can actually get per review

Before the how, here's the data that's publicly visible on each review and worth capturing:

Reviewer name and whether they're a Google Local Guide
Star rating (1–5)
Review text and the date
The business owner's response (if any)
The place's overall rating and total review count

That's enough for reputation tracking, competitor analysis, or sentiment work.

Option 1: Copy-paste by hand

For a handful of reviews on one business, just scroll and copy. It's free and takes zero setup.

It also falls apart fast: Google lazy-loads reviews as you scroll, dates are relative ("2 months ago"), owner responses are collapsed, and doing this across 10 locations — or 500 reviews — is a soul-crushing afternoon. Fine for a quick look, useless at any scale.

Option 2: Build your own scraper

If you write code, your instinct is to script it. Here's the honest reality of that path in 2026:

Google blocks datacenter IPs. A naive requests or Puppeteer script gets a challenge page or empty results within a page or two. You'll need residential proxies, which cost money and add complexity.
The reviews live behind an infinite-scroll feed that only loads as you scroll a specific container — and Google periodically changes the underlying markup, so your selectors break without warning.
The UI is localized to the proxy's country, so a proxy that exits in France hands you a French page and your English-text selectors silently return nothing.

None of this is insurmountable — it's just a genuine, ongoing maintenance job. If you scrape Google Maps for a living, build it. If you need reviews in a spreadsheet by this afternoon, it's overkill.

Option 3: Use a maintained scraper (no code)

The pragmatic middle ground is a hosted actor that already solves the proxy + anti-bot + maintenance problem, so you just hand it a URL and get structured data back. I built one on the Apify platform for exactly this: Google Maps Reviews Scraper.

Here's the full workflow:

Grab the place URL. Open Google Maps, search the business, click it, and copy the URL from your address bar.
Paste it into the actor (placeUrls). Add as many businesses as you want — it processes them in one run.
Set how many reviews per place (up to 500) and a sort order — newest, most relevant, highest, or lowest rating.
Run it. It uses residential proxies to load the real page in English, opens the reviews tab, scrolls the feed, and extracts every review.
Export the dataset as CSV, Excel, or JSON — one click.

A row of output looks like this:

{
  "place_name": "Eiffel Tower",
  "place_rating": 4.7,
  "reviewer_name": "John Smith",
  "is_local_guide": true,
  "rating": 5,
  "date": "2026-07-30",
  "text": "Absolutely stunning views from the top.",
  "owner_response": "Thank you for visiting!"
}

What it costs

It's pay-per-use at $4 per 1,000 reviews — no subscription. So pulling 500 reviews each from 3 locations (1,500 reviews) runs about $6. For a one-off competitive teardown or a monthly reputation export, that's cheaper than the hour you'd spend copy-pasting.

Which should you pick?

Need	Best option
10 reviews, one business, once	Copy-paste
You scrape Google daily and want full control	Build your own
A clean spreadsheet across several businesses, today	Hosted scraper

Common questions

Do I need a Google API key? No — Google's official Places API caps reviews at five per business, which is why scraping the public page is the only way to get them all. No key required.

Is this allowed? You're collecting publicly visible data. As always, use scraped data responsibly and in line with applicable terms and privacy law.

Can I track reviews over time? Yes — re-run on a schedule and diff the output to watch ratings and sentiment move week over week.

If you try the Google Maps Reviews Scraper, I'd genuinely value your feedback — it's new, and honest notes on what to add next help a lot.

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

dbott23 — Thu, 30 Jul 2026 15:10:48 +0000

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.

How to Check if ChatGPT Recommends Your Brand — and Track It Weekly

dbott23 — Thu, 23 Jul 2026 02:47:15 +0000

An estimated one in three purchase research sessions now starts with an AI assistant rather than Google. When your potential customer types "what's the best project management tool for startups?" into ChatGPT, your brand either shows up — or it doesn't.

Traditional SEO dashboards track your Google rankings. None of them track whether AI recommends you.

This post shows you how to check your AI visibility in under five minutes, and how to set up automatic weekly tracking so you're always in the loop.

Why AI recommendations are different from Google rankings

When someone searches Google, they get a list of ten blue links and decide for themselves. When someone asks ChatGPT or Perplexity, they get a single confident recommendation — and most of them follow it.

The mechanic is different too. Google ranks pages. AI assistants draw on everything they've been trained on and everything the web-search mode returns in real time. A brand that's well-documented across Reddit, blog posts, GitHub, and review sites gets recommended more often than one with a polished website but thin presence elsewhere.

There are two distinct things you can measure:

Brand mentions — Does the AI name your brand in response to buyer-intent queries?
Citation rank — Does the AI cite your website as a source in its footnotes?

Both matter. A brand mentioned but not cited is being recommended but not treated as authoritative. A site cited but not mentioned is being used as evidence for a competitor's recommendation. The goal is both.

Step 1: Run a manual spot check

Start with the simplest possible version: open ChatGPT and Perplexity and ask the questions your customers ask.

Choose 3–5 buyer-intent queries. Good examples:

"What's the best [category] tool for [use case]?"
"How do I [problem your product solves]?"
"What do companies use for [your space]?"

For a project management tool, you might ask:

"What project management software should a 20-person startup use?"
"Best free project management tool for remote teams"
"Alternatives to Jira for small teams"

Run each query on ChatGPT (with web search on), Perplexity, and Gemini. Note:

Was your brand mentioned at all?
How early was it mentioned? First recommendation, third, or buried in a list?
Was your website listed in the citations/sources?
Which competitors got mentioned instead?

This gives you a snapshot. The problem with doing it manually is it takes 20+ minutes per check, you can't easily track it over time, and you forget the results by next week.

Step 2: Automate it with an Apify actor

To track this systematically — across multiple queries, multiple engines, and week over week — I built an actor that does the heavy lifting: the AI Brand Visibility Tracker.

It runs real buyer-intent queries across ChatGPT, Perplexity, Gemini, and Claude, then returns structured data for each check:

{
  "brand": "Asana",
  "engine": "chatgpt",
  "query": "best project management tool for small teams",
  "brand_mentioned": true,
  "mention_count": 2,
  "position_score": 9,
  "share_of_voice": 0.4,
  "competitor_mentions": { "Trello": 2, "Monday.com": 1, "ClickUp": 0 },
  "cited_domains": ["g2.com", "blog.asana.com"],
  "response_snippet": "For small teams, Asana offers a generous free tier..."
}

The position_score is 1–10: 10 means your brand was named first, 1 means it was mentioned last, 0 means it wasn't mentioned at all. share_of_voice is your mentions divided by total brand mentions in the response — a direct measure of how dominant you are in that answer.

Setting it up takes about 2 minutes:

Go to the actor page on Apify and click Try for free (no credit card needed for your first runs)
Enter your brand name and up to 5 competitor names
Paste in 3–5 queries your customers actually use
Select which engines to check: ChatGPT, Perplexity, Gemini, Claude
Click Start

The run takes a few minutes and outputs a dataset you can download as CSV or connect to Google Sheets.

Step 3: Set up weekly tracking

A one-time check tells you where you stand today. Weekly tracking tells you whether your content and PR efforts are actually moving the needle.

In the Apify console:

Open Actors → Schedules → New schedule
Point it at the AI Brand Visibility Tracker with your saved input
Set trackTrends: true (this is the default)
Choose a weekly cadence — I run it every Monday morning

Every subsequent run automatically compares to the previous one and adds trend deltas to the SUMMARY output:

{
  "Asana": {
    "mention_rate": {
      "current": 0.67,
      "previous": 0.5,
      "delta": 0.17
    },
    "avg_position_score": {
      "current": 7.5,
      "previous": 6.0,
      "delta": 1.5
    }
  }
}

A rising mention_rate means AI is recommending you more often. A rising position_score means you're being named earlier — more prominently — in the response. A rising share_of_voice means you're taking ground from competitors.

Step 4: Also check if your site is being cited

Brand mentions and citation links are different signals — and you want both.

The companion actor is the AI Citation Auditor. Instead of tracking brand names, it tracks whether your domain appears in the structured citation list that engines like Perplexity and ChatGPT return alongside their answers.

The input is domain names instead of brand names:

{
  "domains": ["yourdomain.com", "competitor1.com", "competitor2.com"],
  "queries": ["best project management tool for small teams"],
  "engines": ["chatgpt", "perplexity", "gemini"]
}

It tells you:

Whether your domain was cited at all
What rank your citation appeared at (rank 1 = first source listed)
Which specific URL was cited (so you know which content is earning citations)
How that changes week over week

One useful property: multiple domains share the same API call. Auditing 5 domains against 3 queries × 3 engines costs the same as auditing 1 domain — the actor checks all domains against each response.

What actually improves your AI visibility?

Based on what I've seen from running these checks across various brands:

What helps:

Being discussed across multiple sources. AI assistants synthesise from many inputs. A brand with Reddit threads, G2 reviews, blog posts, and GitHub repos mentioning it in context gets pulled into more responses than a brand with only a polished website.
Being cited by authoritative domains. G2, Capterra, Trustpilot, and niche industry blogs that get cited by AI are worth being listed on.
Clear, specific positioning. Brands that are easy to describe in a sentence ("the Notion alternative built for engineers") get recommended more precisely than brands with vague positioning.
Being named in comparison content. "X vs Y" articles, "best tools for Z" lists — AI engines love this format because it's already structured as a recommendation.

What doesn't move the needle as much as you'd hope:

Having a well-optimised website alone
Meta descriptions and title tags (these are Google signals, not AI signals)
Domain authority in the traditional SEO sense

Tracking this for clients

If you're an SEO or GEO agency, this is a straightforward addition to your monthly reporting. Run the actor with a unique trendKey per client:

{
  "brands": [{ "name": "ClientBrand", "competitors": ["Competitor1", "Competitor2"] }],
  "queries": ["..."],
  "trendKey": "client-acme-july-2026"
}

Each client's trend series is stored separately. You get a clean week-over-week delta report you can paste into any client deck.

Quick start

Tool	What it tracks	Link
AI Brand Visibility Tracker	Brand mentions, position score, share of voice	apify.com/dbott23/ai-brand-visibility-tracker
AI Citation Auditor	Domain citations, citation rank, cited URLs	apify.com/dbott23/ai-citation-auditor

Both are pay-per-check — no subscription. First runs are free on Apify's free tier.

Questions? Drop them in the comments. I'm also tracking which queries and engines tend to surface brands most reliably — happy to share what I've found.

How to Check if ChatGPT Recommends Your Brand — and Track It Weekly

dbott23 — Thu, 23 Jul 2026 02:43:44 +0000

Traditional SEO dashboards track your Google rankings. None of them track whether AI recommends you.

This post shows you how to check your AI visibility in under five minutes, and how to set up automatic weekly tracking so you're always in the loop.

Why AI recommendations are different from Google rankings

There are two distinct things you can measure:

Brand mentions — Does the AI name your brand in response to buyer-intent queries?
Citation rank — Does the AI cite your website as a source in its footnotes?

Step 1: Run a manual spot check

Start with the simplest possible version: open ChatGPT and Perplexity and ask the questions your customers ask.

Choose 3–5 buyer-intent queries. Good examples:

"What's the best [category] tool for [use case]?"
"How do I [problem your product solves]?"
"What do companies use for [your space]?"

For a project management tool, you might ask:

"What project management software should a 20-person startup use?"
"Best free project management tool for remote teams"
"Alternatives to Jira for small teams"

Run each query on ChatGPT (with web search on), Perplexity, and Gemini. Note:

Was your brand mentioned at all?
How early was it mentioned? First recommendation, third, or buried in a list?
Was your website listed in the citations/sources?
Which competitors got mentioned instead?

This gives you a snapshot. The problem with doing it manually is it takes 20+ minutes per check, you can't easily track it over time, and you forget the results by next week.

Step 2: Automate it with an Apify actor

To track this systematically — across multiple queries, multiple engines, and week over week — I built an actor that does the heavy lifting: the AI Brand Visibility Tracker.

It runs real buyer-intent queries across ChatGPT, Perplexity, Gemini, and Claude, then returns structured data for each check:

{
  "brand": "Asana",
  "engine": "chatgpt",
  "query": "best project management tool for small teams",
  "brand_mentioned": true,
  "mention_count": 2,
  "position_score": 9,
  "share_of_voice": 0.4,
  "competitor_mentions": { "Trello": 2, "Monday.com": 1, "ClickUp": 0 },
  "cited_domains": ["g2.com", "blog.asana.com"],
  "response_snippet": "For small teams, Asana offers a generous free tier..."
}

Setting it up takes about 2 minutes:

Go to the actor page on Apify and click Try for free (no credit card needed for your first runs)
Enter your brand name and up to 5 competitor names
Paste in 3–5 queries your customers actually use
Select which engines to check: ChatGPT, Perplexity, Gemini, Claude
Click Start

The run takes a few minutes and outputs a dataset you can download as CSV or connect to Google Sheets.

Step 3: Set up weekly tracking

A one-time check tells you where you stand today. Weekly tracking tells you whether your content and PR efforts are actually moving the needle.

In the Apify console:

Open Actors → Schedules → New schedule
Point it at the AI Brand Visibility Tracker with your saved input
Set trackTrends: true (this is the default)
Choose a weekly cadence — I run it every Monday morning

Every subsequent run automatically compares to the previous one and adds trend deltas to the SUMMARY output:

{
  "Asana": {
    "mention_rate": {
      "current": 0.67,
      "previous": 0.5,
      "delta": 0.17
    },
    "avg_position_score": {
      "current": 7.5,
      "previous": 6.0,
      "delta": 1.5
    }
  }
}

Step 4: Also check if your site is being cited

Brand mentions and citation links are different signals — and you want both.

The input is domain names instead of brand names:

{
  "domains": ["yourdomain.com", "competitor1.com", "competitor2.com"],
  "queries": ["best project management tool for small teams"],
  "engines": ["chatgpt", "perplexity", "gemini"]
}

It tells you:

Whether your domain was cited at all
What rank your citation appeared at (rank 1 = first source listed)
Which specific URL was cited (so you know which content is earning citations)
How that changes week over week

What actually improves your AI visibility?

Based on what I've seen from running these checks across various brands:

What helps:

Being discussed across multiple sources. AI assistants synthesise from many inputs. A brand with Reddit threads, G2 reviews, blog posts, and GitHub repos mentioning it in context gets pulled into more responses than a brand with only a polished website.
Being cited by authoritative domains. G2, Capterra, Trustpilot, and niche industry blogs that get cited by AI are worth being listed on.
Clear, specific positioning. Brands that are easy to describe in a sentence ("the Notion alternative built for engineers") get recommended more precisely than brands with vague positioning.
Being named in comparison content. "X vs Y" articles, "best tools for Z" lists — AI engines love this format because it's already structured as a recommendation.

What doesn't move the needle as much as you'd hope:

Having a well-optimised website alone
Meta descriptions and title tags (these are Google signals, not AI signals)
Domain authority in the traditional SEO sense

Tracking this for clients

If you're an SEO or GEO agency, this is a straightforward addition to your monthly reporting. Run the actor with a unique trendKey per client:

{
  "brands": [{ "name": "ClientBrand", "competitors": ["Competitor1", "Competitor2"] }],
  "queries": ["..."],
  "trendKey": "client-acme-july-2026"
}

Each client's trend series is stored separately. You get a clean week-over-week delta report you can paste into any client deck.

Quick start

Tool	What it tracks	Link
AI Brand Visibility Tracker	Brand mentions, position score, share of voice	apify.com/dbott23/ai-brand-visibility-tracker
AI Citation Auditor	Domain citations, citation rank, cited URLs	apify.com/dbott23/ai-citation-auditor

Both are pay-per-check — no subscription. First runs are free on Apify's free tier.

Questions? Drop them in the comments. I'm also tracking which queries and engines tend to surface brands most reliably — happy to share what I've found.

DEV Community: dbott23

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Which should you pick?

Common questions

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Step 1: Run a Manual Check First

Step 2: Automate with the AI Brand Visibility Tracker

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Running It on a Schedule

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Links

How to Check if ChatGPT Recommends Your Brand — and Track It Weekly

Why AI recommendations are different from Google rankings

Step 1: Run a manual spot check

Step 2: Automate it with an Apify actor

Step 3: Set up weekly tracking

Step 4: Also check if your site is being cited

What actually improves your AI visibility?

Tracking this for clients

Quick start

How to Check if ChatGPT Recommends Your Brand — and Track It Weekly

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Step 1: Run a manual spot check

Step 2: Automate it with an Apify actor

Step 3: Set up weekly tracking

Step 4: Also check if your site is being cited

What actually improves your AI visibility?

Tracking this for clients

Quick start