DEV Community: Devanand Kushwaha

How to check if ChatGPT recommends your brand (a repeatable method, plus the math)

Devanand Kushwaha — Sun, 08 Nov 2026 05:49:04 +0000

Most founders check their AI visibility the same way: open ChatGPT, type their own brand name, watch it find the website and describe the product, and relax.

That test proves almost nothing. Your buyers don't ask "what is [your brand]?". They ask "what's the best tool for…", "which brand should I buy for…", "X vs Y" — and the real question is whether you are named in that answer.

It matters more every month. ChatGPT passed roughly 900 million weekly users in February 2026. Google says AI Mode passed 1 billion monthly users. And when Google shows an AI summary, Pew Research found people clicked a regular result in 8% of visits, against 15% without one. If the answer doesn't name you, the click often never happens.

Here's the method I use. It works by hand in a spreadsheet, and it's exactly what AI visibility tools automate.

TL;DR — Ask 20–30 real buyer questions (no brand names) in a clean session with web search on, three times each, on every engine you care about. Log whether you're named, your position, who's named instead and which URLs are cited. Visibility = answers naming you ÷ all answers. Share of voice = answers naming you ÷ answers naming any brand you track. Re-run weekly with the same questions.

Step 1: Build the question set

Write 20–30 questions a buyer asks before they know your name. Mix three types:

Type	B2B SaaS example	Consumer brand example
Category	"best invoicing software for freelancers"	"best protein powder for beginners"
Problem	"how do I stop late client payments"	"protein powder that doesn't cause bloating"
Comparison	"FreshBooks vs Wave"	"whey vs plant protein"

Leave out every question that contains your own brand name. You'll "win" those almost every time, and they inflate every number downstream. Track them separately if you want to see how AI describes you.

Step 2: Use a clean session, with web search on

Log out, or use a temporary chat with memory off. Personalisation and chat history change the answer.
Make sure the answer uses web search. For current products, ChatGPT, Claude, Gemini and Perplexity search the web, read a handful of pages, and summarise them. An answer from model memory can't reflect a page you published last week — and it isn't what most buyers see.
If you sell in several countries, ask from each one (or put the country in the question). Answers and sources differ by market.

Step 3: Ask each question at least three times

AI answers aren't deterministic. The same question can name four brands in the morning and a different three in the afternoon. One run is an anecdote; three runs on different days is a measurement.

Step 4: Log every answer

A flat CSV is enough:

date,engine,country,question,named,position,rivals_named,cited_urls
2026-11-08,chatgpt,US,best invoicing software for freelancers,N,,"FreshBooks; Wave; Zoho Invoice","https://example-review-site.com/best-invoicing; https://www.reddit.com/r/freelance/..."
2026-11-08,chatgpt,US,how do I stop late client payments,Y,3,"FreshBooks; Bonsai","https://example-blog.com/late-payments"

Count each brand once per answer, however many times the answer repeats it.

Step 5: Do the math

Three numbers matter:

Visibility (mention rate) = answers that name you ÷ all answers
Share of voice = answers that name you ÷ answers that name any brand you track
Position = where you appear when you are named

Worked example: 20 questions × 3 runs = 60 answers. You're named in 9 of them. Across all 60 answers, the brands you track are named 50 times in total (counting each brand once per answer).

Measure	Calculation	Result
Visibility	9 ÷ 60	15%
Share of voice	9 ÷ 50	18%
Position	average of your "position" column	e.g. 2.7

In Google Sheets, with the log above in columns A–H:

Visibility:        =COUNTIF(E2:E,"Y") / COUNTA(D2:D)
Average position:  =AVERAGEIF(E2:E,"Y",F2:F)
ChatGPT only:      =COUNTIFS(B2:B,"chatgpt",E2:E,"Y") / COUNTIF(B2:B,"chatgpt")

Pool 7–14 days of answers before you compare weeks. A single day is too noisy.

Step 6: Read the citations — that's where the fix is

The cited_urls column is the most useful thing you'll collect. It shows which pages the AI trusts for your category: usually "best X" lists, review sites, comparison articles, Reddit threads and YouTube videos. Several 2026 studies found third-party comparison content among the most-cited sources for commercial questions.

So the to-do list writes itself:

Sort the cited domains by how often they appear.
For the top ones, get included: pitch the article's author, claim your review-site profile, answer the Reddit thread honestly (and say who you are).
For questions where no page is cited at all, publish one page on your own site that answers the exact question in its first paragraph.

Every engine answers differently

Don't stop at ChatGPT. The same question regularly gets different brands from ChatGPT, Claude, Gemini and Google's AI Overviews, because each one searches differently and cites different pages. Track each engine separately and report the number per engine — a decent overall score can hide an engine where you're invisible.

Automating it

By hand, 30 questions × 3 runs × 4 engines is 360 answers a week. That's why AI visibility tools exist. The main ones, with published prices (November 2026):

Tool	Starts at	Engines
Otterly.ai	$29/mo · 15 prompts	ChatGPT, Perplexity, AI Overviews, Copilot; Gemini, AI Mode, Claude as add-ons
Peec AI	$95/mo · 50 prompts	3 models per plan; more on Enterprise
Semrush AI Visibility Toolkit	$99/mo per domain	inside Semrush
Aurvikon	$149/mo · 25 questions, daily	ChatGPT + AI Overviews on every plan; Gemini and Claude by plan
Ahrefs Brand Radar AI	from $199/mo + an Ahrefs plan	$699/mo for all platforms
Profound	free trial, then Enterprise by quote	up to 9 engines

Disclosure: I build Aurvikon, so I'm not neutral. Prices change often — check each vendor's pricing page.

Whatever you use, check three things: it collects answers with live web search, it leaves out questions containing your brand name, and it shows the cited URLs. A tool that can't do those can hand you a flattering number you can't act on.

Common mistakes

Checking once and treating it as the truth.
Counting questions that contain your brand name.
Only checking ChatGPT.
Reading the answer but ignoring the sources.
Changing the question list every week — then nothing is comparable.

If you want a quick look before you build the spreadsheet, our free check asks ChatGPT five buyer questions for your category with live web search and shows who gets named: aurvikon.com/?check=1. There's also a longer version of this method on our site: How to check whether ChatGPT recommends your brand.

How are you tracking this today — by hand, with a tool, or not yet? I'm happy to answer questions in the comments.

Your Brand Can Rank on Google and Still Be Invisible in AI Search

Devanand Kushwaha — Sun, 25 Oct 2026 08:43:18 +0000

Most businesses know how to track their Google rankings.

But what happens when a potential customer stops searching on Google and starts asking ChatGPT, Perplexity, or another AI assistant which product they should choose?

Your website might rank well. Your content might get traffic. Your competitors might even have fewer backlinks than you.

Yet when a customer asks an AI assistant for recommendations, your competitors appear while your brand is nowhere to be found.

That creates a new visibility problem: which questions are you losing in AI search?

I'm building Aurvikon to help answer that question.

The problem with measuring AI visibility

Traditional SEO gives us familiar metrics:

Keyword rankings
Impressions and clicks
Organic traffic
Backlinks
Search performance

These metrics remain valuable. But they don't fully explain how your brand appears inside AI-generated answers.

Imagine someone asking an AI assistant:

What are the best tools for improving AI search visibility?
Which platforms help SaaS companies monitor their brand in AI answers?
Compare three alternatives for tracking brand recommendations in AI search.
Which tool should a startup use to understand its visibility in ChatGPT?

Your brand might appear in one answer and disappear from the next.

A competitor might be recommended repeatedly. Another company might be described as the category leader. Your own product might be missing entirely.

Looking at a single AI-generated answer won't tell you the whole story.

You need to understand visibility across the questions your potential customers actually ask.

The shift: from keyword rankings to question-level visibility

I'm interested in a more useful way to think about AI search visibility.

Instead of asking only:

"Does my brand appear in AI search?"

Ask:

"For which relevant buyer questions does my brand appear, where do competitors appear instead, and what can I do about it?"

That second question leads to much more actionable work.

Consider three possible findings from an AI visibility audit:

1. You're missing from important questions.

People ask questions directly related to your product category, but the AI response doesn't mention your company.

2. Your competitors are being recommended instead.

Your product may be relevant, but competing brands are consistently appearing in the answers you're trying to win.

3. Your brand appears, but not in the way you want.

An AI response might mention your company without accurately communicating its core use case, differentiator, or value.

These are different problems. They shouldn't all receive the same recommendation to "publish more content."

What we're building with Aurvikon

Aurvikon is an AI visibility tool focused on helping businesses understand how their brands appear across AI answer platforms.

The core idea is straightforward:

Identify the questions where your brand is losing visibility, understand what appears instead, and turn those findings into an action plan.

A practical workflow looks like this:

Step 1: Identify the questions buyers ask

Start with questions your target customers might ask before choosing a product.

Don't limit the research to your brand name. Include category searches, comparisons, alternatives, use cases, and purchasing decisions.

Step 2: Test those questions across AI platforms

Run relevant questions across the platforms you want to monitor, such as ChatGPT, Perplexity, and Gemini.

Treat the responses as observations to analyze, not as permanent rankings.

Step 3: Identify where you're losing

Look for patterns:

Which questions mention your brand?
Which questions mention competitors but not you?
Where are you missing from relevant comparisons?
Which product capabilities or use cases aren't being communicated?
Which questions deserve attention first?

This is where an audit becomes more useful than a collection of screenshots.

Step 4: Turn findings into actions

Every visibility gap should lead to a hypothesis.

Maybe a product page doesn't explain a use case clearly. Maybe your comparison content is weak. Maybe your product lacks supporting content for a particular buyer question.

Investigate the evidence, make the relevant improvement, and test again.

The goal isn't to manufacture mentions. It's to make your brand easier to understand, evaluate, and recommend when it genuinely fits the user's needs.

The difficult part isn't generating an AI visibility report

AI answers change. Results can vary by wording, location, model version, and time.

That means a report saying "your brand was mentioned" is only a starting point.

A useful system should help you move from observation to diagnosis:

Buyer question → AI response → competitor comparison → visibility gap → action → retest.

That repeatable workflow is the problem space we're focused on at Aurvikon.

We're especially interested in making the results useful to founders and marketing teams who need to decide what to fix next, rather than simply collecting another dashboard of numbers.

What this means for SaaS founders

If you're building a SaaS company, think about the questions someone would ask immediately before buying your product.

Not just:

"What is [your brand]?"

But:

What is the best alternative to [competitor]?
Which tools solve [specific customer problem]?
Compare products that offer [important capability].
What should a small business use for [desired outcome]?
Which platform is best for [specific use case]?

These questions reveal how AI assistants represent your category and which products they surface for potential buyers.

They can also reveal gaps in your positioning and content strategy.

AI visibility isn't a replacement for SEO. It's an additional area to measure as customers discover products through AI-generated answers.

I'm building Aurvikon around one practical question

I believe the most useful starting point for AI visibility isn't another generic score.

It's knowing which valuable questions your company is losing, which competitors show up instead, and what you should investigate next.

That's the problem we're working on with Aurvikon.

I'd love to hear from other founders, SEO professionals, and SaaS marketers:

If you could see every important buyer question where AI assistants recommend your competitors instead of you, what would you want the tool to tell you first?

Would you prioritize the competing brands, the missing content, the sources behind the answers, or the specific actions most likely to improve visibility?

I'm interested in how people are approaching this problem in practice.

I Stopped Asking “Does AI Know My Brand?” and Started Measuring Something Harder

Devanand Kushwaha — Thu, 08 Oct 2026 07:03:34 +0000

I thought AI search visibility would be a simple number.

Something like:

Your brand appears in 38% of AI answers.

Easy.

Put it on a dashboard. Track it every week. Done.

Then I started building Aurvikon.

And I realized the percentage isn't the interesting part.

The interesting question is:

Why did the AI choose them instead of you?

That question changed how I started looking at AI search.

I stopped testing branded searches

The easiest test is to ask:

“Tell me about [company name].”

Of course the company is likely to appear.

That's not very useful.

A real buyer might never mention your company.

They might ask:

“What's the best CRM for a 10-person sales team?”

Or:

“What are good alternatives to Salesforce for a small company?”

Or:

“Which tools are best for automating outbound sales?”

That's where things get interesting.

So I started testing category questions instead of brand-name questions.

And I started seeing a very different picture.

A company can be visible without really being competitive

Imagine an AI system answers a category question and mentions your company.

At first glance:

Great. We're visible.

But then you look closer.

Your competitor was mentioned first.

Another competitor was recommended for the exact use case you target.

Your company was mentioned briefly but wasn't supported by any strong source.

And in another AI system, you weren't mentioned at all.

So what does “38% visibility” actually tell you?

Not much on its own.

That's when I started breaking visibility into smaller pieces.

Are you mentioned?

The most basic question.

Did the AI include your company in the answer?

Are you relevant?

Being mentioned isn't enough.

Was your company actually recommended for the problem you solve?

Who gets mentioned instead?

This is probably one of the most useful signals.

Because the real business question isn't:

“Does AI know us?”

It's:

“Who is getting the buyer that we wanted?”

What sources are influencing the answer?

This is where things get even more interesting.

The answer may be influenced by:

comparison pages
customer reviews
industry articles
directories
documentation
community discussions
other third-party sources

So your website isn't necessarily the entire story.

This created a weird problem

I started noticing situations where a company could have:

a decent website

good traditional SEO

lots of content

and still lose the AI recommendation.

Not because the product was necessarily worse.

But because the AI had a different picture of the category.

Maybe competitors had more third-party coverage.

Maybe there were more comparison pages mentioning them.

Maybe reviews were stronger.

Maybe the company's positioning was inconsistent across the web.

Maybe the AI simply had more evidence connecting the competitor with the use case.

That's a much harder problem than “rank higher.”

That's why I'm building Aurvikon

The idea behind Aurvikon became much simpler:

Ask the questions buyers actually ask.

Then measure what happens.

Which brands appear?

Which competitors appear?

What sources are cited?

Where does your company disappear?

Which questions are the biggest opportunities?

Instead of giving a company another generic SEO checklist, I want to show the actual discovery problem.

Something like:

Question: What are the best tools for automating outbound sales?

Then:

AI mentions: Apollo, Outreach, Hunter

Your brand: Not mentioned

Top cited sources: X, Y, Z

Biggest opportunity: Specific use case where competitors dominate

That's much more actionable than:

“Your AI visibility score is 42.”

I'm still figuring out the hard part

This is probably the part I find most interesting.

I don't think the industry has completely figured out what a good AI visibility measurement should look like yet.

I'm still testing questions like:

How many prompts are enough to make the measurement meaningful?

Should every AI engine have the same weight?

How much should citations matter?

Should a mention in position #1 count very differently from position #5?

How do you separate a random mention from an actual recommendation?

How stable are these answers over time?

And probably the biggest one:

Can an AI visibility score explain what to do next, or is it just another dashboard number?

I don't want Aurvikon to become another tool that gives you a colorful score and leaves you wondering what it means.

The useful part should be the explanation.

The metric isn't the product

That's the lesson I'm currently learning.

A number is interesting.

A reason is useful.

If an AI doesn't mention your company, I want to know why.

If it mentions your competitor, I want to know what evidence is behind that recommendation.

And if there's an opportunity to change the situation, I want the product to tell you where to start.

That's the problem I'm working on now.

I'm still early.

The methodology is still evolving.

And honestly, that's the fun part.

We're watching a new layer of search appear while we're still figuring out how to measure it.

If you're building SaaS, try one experiment today:

Ask an AI:

“What are the best [your category] tools for [your target customer]?”

Don't mention your company.

Then look at who gets recommended.

You might learn more from that answer than from another week of staring at your Google rankings.

How to check if ChatGPT recommends your brand (the method we use)

Devanand Kushwaha — Wed, 07 Oct 2026 16:28:32 +0000

Ask ChatGPT "What is [your product]?" and it will probably find your website and describe you nicely. That proves one thing: it can find you.

Your buyers don't ask that. They ask "what's the best tool for…" or "which one should I use for…". The real question is whether you're named in that answer.

Here's the manual method we use to get a real number, plus the mistakes that quietly inflate it.

The method

Write 20–30 real buyer questions. Mix category questions ("best accounting software for freelancers"), problem questions ("how do I stop late client payments") and comparisons ("X vs Y"). Leave out anything that contains your own brand name.
Use a clean session. Logged out, or a temporary chat with memory turned off. Personalisation and chat history change the answer.
Make sure web search is on. Recommendations for current products come from live search results, which is what most buyers see, not from the model's training memory.
Ask each question at least 3 times, ideally on different days. Answers vary from one run to the next, so one answer proves very little.
If you sell in several countries, ask from each one (or put the country in the question). Answers and sources differ by market.
Record four things for every answer: were you named (yes/no), your position in any list, which brands were named instead, and which websites were cited.

Turning the answers into numbers

Visibility = answers that name you ÷ all answers. Named in 6 of 60 answers = 10%.
Share of voice = answers naming you ÷ answers naming any brand you track.
Position = where you appear when you are named (first, third, last).
Sources = the websites cited most often. These are the pages the AI trusts for your category, and the places you most need to be mentioned.

Keep the question list fixed and re-run it every week. A change only means something if you measured the same questions the same way.

Common mistakes

Checking once and treating it as the truth. Run it several times.
Counting questions that contain your brand name. You will always "win" those, which inflates the number.
Checking only ChatGPT. Claude, Gemini, Perplexity and Google's AI Overviews often name different brands and cite different sources.
Reading the answer but ignoring the sources. The cited pages tell you what to fix.

FAQ

Does ChatGPT give everyone the same answer?
No. Answers vary between runs, users, countries, and whether web search is used. That's why you ask each question several times and keep the method fixed.

How often should I check?
Weekly is enough to see a trend by hand. Daily checks help once you're changing things and want to see the effect sooner.

Why does ChatGPT name my competitor instead of me?
Usually because the pages it reads for that question (lists, reviews, comparisons, forums) mention your competitor and not you. The cited sources show you which pages those are.

Doing it automatically

30 questions × 3 runs × 2 countries is 180 answers a week by hand. That's why we built Aurvikon: it asks your buyer questions every day with live web search, records who is named and which pages are cited, and turns the cited pages into a to-do list.

*Originally published at aurvikon.com

What Is AI Search Visibility?

Devanand Kushwaha — Mon, 05 Oct 2026 08:27:46 +0000

Search is changing.

For years, a buyer could search Google, open a few results, compare products, and make a decision.

Now they can ask:

“What are the best tools for this?”

“Which companies should I consider?”

“What are the alternatives?”

And an AI system gives them a shortlist.

That creates a new problem for SaaS companies:

What does AI say about your brand when a potential customer asks?

AI search visibility is the ability of a brand to be understood, mentioned, compared, and supported by relevant sources across AI-powered search experiences.

It's not simply about ranking.

There are several different things to measure:

Brand mentions
Is your company mentioned at all?
Competitor mentions
Which companies are being recommended instead?
Citations and sources
Which websites and pages are influencing the answer?
Category visibility
Does AI associate your company with the problem you actually solve?
Consistency
Does the web describe your company accurately and consistently?

A company can have strong traditional SEO and still be difficult for AI systems to surface for the questions that matter.

That's why we're building Aurvikon: to help businesses measure this new layer of discovery instead of guessing.

We're early, and we're documenting what we learn as AI becomes part of the buying journey****