How to Track Your Brand's Visibility in ChatGPT & Top LLMs

Do not index

It's no longer enough to just track your brand on Google and social media. You need a clear picture of how you show up in conversations with ChatGPT, Claude, and Gemini. This means systematically asking these AIs questions related to your brand, products, and industry to see what they say. It's about turning those AI-generated answers into hard data you can actually use.

Why LLM Brand Visibility Is Your New Competitive Edge

The ground has completely shifted. For years, we all focused our reputation management efforts on social media feeds, review sites, and the classic Google search results page. But a powerful new channel has forced its way into the picture: Large Language Models (LLMs).

These platforms are no longer just tech novelties; they're becoming the go-to source of information for millions of people.

Users are now leaning on AI for everything—from asking for the "best project management software" to getting help solving complex business problems. Every single answer an LLM generates is a chance for your brand to be seen, recommended, or completely ignored. This isn't some far-off trend. It's happening right now and it's fundamentally changing how people discover and decide.

The Real-World Economic Impact

The influence these AIs have on buying decisions is already massive, and it's growing fast. We're not talking about a small, niche audience. This is a major force shaping the global economy.

Just look at the projections. By 2025, it’s estimated that ChatGPT could be involved in up to 18% of customer journeys in the travel and hospitality sector. That translates to an influence over a staggering 1.11 trillion in sales. You can dig into the specifics in this detailed report on ChatGPT usage statistics.

These numbers send a clear message: if you're ignoring how your brand is portrayed in LLMs, you're essentially walking away from a critical marketing channel.

Adapting to the New Rules of Discovery

Getting a handle on your brand's visibility in ChatGPT is the first real step toward building a modern, resilient strategy. When you start monitoring your presence, you can immediately:

Spot and fix bad information. LLMs get things wrong. They might misrepresent your product features or company history. Without tracking, those errors spread quietly.

See where you stand against the competition. You can finally get an unbiased look at how you stack up when a user asks for recommendations in your niche. Are you even in the running?

Find glaring content gaps. Monitoring reveals the questions people are asking that you haven't answered yet. This is a goldmine for creating new blog posts, FAQs, and guides that AI models can learn from.

Measure how you're perceived. Quantify how often your brand gets mentioned and in what light—positive, negative, or neutral. This is a key part of calculating your overall AI Visibility Score.

Backlinks were the currency of traditional SEO. In the new world of LLMs, the currency is the contextual mention. It’s about being part of the right conversations in the places where these models get their training data.

At the end of the day, showing up in these AI-powered conversations directly shapes perception, builds trust, and drives revenue. This is the new competitive high ground, and it demands a dedicated strategy, starting now.

Building Your Foundational Brand Tracking System

If you really want to track your brand’s visibility in ChatGPT and other LLMs, you have to move beyond just running a few random checks here and there. That's not a strategy. What you need is a real system that turns those one-off observations into measurable, hard data.

The whole point is to build a central dashboard—this could be in a purpose-built tool like Attensira or even a well-organized spreadsheet. This becomes your single source of truth, giving you a consistent, actionable picture of how your brand is showing up in AI conversations.

But where do you start? The process kicks off by figuring out what actually matters. Just searching for your brand name is a decent first step, but honestly, it barely scratches the surface. To do this right, you need to track a much wider set of queries that mirror how real people actually look for solutions.

Defining Your Core Tracking Parameters

Before you can measure anything, you have to know what you’re looking for. A solid tracking setup isn't just one type of query; it’s a portfolio of queries, each designed to shed light on a different piece of your brand's visibility puzzle.

Think like your customer. Your tracking parameters should follow their entire journey, from identifying a problem to comparing solutions.

Direct Brand Mentions: This is the obvious one. You’re tracking your company name, all your product names, and any common misspellings or variations people might use. Easy enough.

Competitor Comparisons: This is where it gets interesting. You want to see what happens when users ask things like, "Brand X vs. Brand Y" or "what are some alternatives to Competitor Z." Are you even in the conversation? And if so, how are you framed?

Problem-Solution Queries: Here’s where you find high-intent users. Track prompts like "how do I solve [customer pain point]" or "best tool for [specific job-to-be-done]." The goal isn't just to be mentioned; it's to be the recommended solution.

Category Leadership Queries: These are the broad, non-branded searches like "best accounting software for freelancers" or "top CRM for small businesses." This is your share-of-voice metric, telling you if LLMs see you as a leader in your category.

By keeping an eye on these different query types, you get a much richer picture. You're no longer just asking if you're mentioned, but uncovering the critical context of how and why. That's how you turn raw data into an actual strategy. You can dive deeper into the nuts and bolts of tracking AI brand mentions and why they matter over in our glossary.

Setting Up Your Monitoring Dashboard

Once you've nailed down your tracking parameters, it's time to put them to work. Whether you’re using automated software or grinding it out with manual checks, the single most important thing is consistency. A good dashboard doesn't just show a snapshot; it visualizes trends over time, helping you spot problems before they blow up or jump on new opportunities.

Imagine a SaaS company is tracking "best accounting software for freelancers." One month, they see that ChatGPT only recommends them 30% of the time. After spotting this, they beef up their on-site content to speak directly to freelancers' needs. The next month, they check again and see that number has jumped to 50%. That’s the kind of tangible feedback loop a good system creates.

A well-structured tracking system isn't just about collecting data; it's about creating a feedback loop. The insights from your monitoring should directly inform your content, SEO, and product marketing strategies, creating a cycle of continuous improvement.

It's also smart to set up alerts for any major shifts. A sudden nosedive in sentiment or a new competitor popping up in your key category queries is something you need to know about immediately. These alerts turn your dashboard from a passive report into an active defense system for your brand, which is exactly what you need in the fast-moving world of LLMs.

What to Do When LLMs Don't Agree on Your Brand

Here’s a situation I see all the time. You ask ChatGPT about the best software in your space, and it serves up a perfect, glowing recommendation for your brand. High-fives all around. But then you run the exact same prompt through Google’s AI Overviews or Claude, and… crickets. Your company doesn't even get a mention.

This isn't a glitch. It's just how the AI world works right now.

Each LLM is its own walled garden. They’re built on different data sets, fine-tuned with unique algorithms, and often swayed by specific partnerships. We know, for example, that Google's AI models have a soft spot for Reddit content. Another model might lean heavily on academic papers or legacy news sites. This is precisely why a "set it and forget it" strategy for AI visibility is doomed from the start.

Don't just take my word for it. A 2025 BrightEdge analysis revealed that Google’s AI Overviews and ChatGPT gave conflicting brand recommendations nearly 62% of the time for the same prompts. That’s a massive disagreement rate. It’s also concrete proof that you can’t just track your visibility in one place; you need to monitor each major LLM as a separate channel. You can dig into the research on AI brand recommendations here if you want to see the full breakdown.

Running a Comparative LLM Analysis

To get a real handle on your brand's standing, you have to systematically test and compare your performance across the board. This isn’t about a few random spot-checks. It’s about creating a multi-platform scorecard that shows you where you’re strong and, more importantly, where you're invisible.

First, you need a consistent set of prompts. I recommend starting with 15-20 high-value queries that cover your brand name, your top competitors, and the core problems your customers are trying to solve. Then, you methodically run these identical prompts through every LLM you care about—think ChatGPT, Gemini, Perplexity, and Claude.

For each LLM, you're looking to measure two key things:

Brand Density: What percentage of relevant prompts actually mention your brand? If you show up in 6 out of 10 category-specific queries, your brand density is 60% on that platform.

Silence Rate: This is just the flip side. What percentage of the time are you completely absent? A high silence rate, especially on queries with commercial intent, is a huge red flag.

Doing this turns a vague feeling of "we're not showing up" into hard data you can actually work with.

Building Your Multi-Platform Scorecard

All this analysis should feed into a central scorecard. It can be a simple spreadsheet to start, or you can visualize it in a purpose-built tool like Attensira. The whole point is to see, at a glance, which platforms you're dominating and which ones are ignoring you.

A multi-platform scorecard doesn't just tell you if you're visible; it tells you where your content and SEO efforts are actually paying off. Think of it as a diagnostic tool that shows you where to focus your time and money.

For instance, your scorecard might show that your technical deep-dive blog posts are killing it with one AI, giving you great brand density for problem-solution prompts. At the same time, it could reveal a total dead zone on another platform that prioritizes community forums. That's your signal to get more active on places like Reddit. This kind of comparative insight is what separates a basic approach from a sophisticated, winning AI visibility strategy.

Advanced Prompting for Deeper Brand Insights

Basic mention tracking is your starting point, but if you really want to understand how LLMs see your brand, you have to dig deeper. Simple queries like "best software for X" will only ever give you a surface-level snapshot. The real goal is to shift from passive monitoring to active intelligence gathering, effectively turning the AI into a powerful analysis partner.

This is where you start asking more strategic, nuanced questions. Instead of just looking for your name, you can simulate specific user scenarios to uncover hidden biases, find competitive weaknesses, or gauge market perception. Think of it as the difference between asking if your brand is in the race versus asking how it performs on different parts of the track.

The magic happens when you craft prompts that force the LLM to think critically. For example, you can have it role-play as a specific customer persona, giving you a direct window into how your target audience might perceive your brand's value proposition.

Eliciting Strategic Intelligence with Sophisticated Prompts

To get this right, you need to think beyond simple questions. The objective is to design prompts that pull out detailed, comparative, and sentiment-rich responses. This takes a bit of creativity and a very clear idea of what you're trying to find.

This whole process is the core of prompt engineering, a skill that’s quickly becoming non-negotiable for marketers. If you want to get a better handle on the basics, our guide on what prompt engineering is is a great place to start.

Here are a few ways I like to structure more advanced prompts:

Role-Playing Scenarios: Tell the AI to adopt a specific persona. For instance, "You are the CFO of a mid-sized tech company. Compare HubSpot and Salesforce based on long-term scalability and total cost of ownership." This gets you much richer, more specific insights than a generic comparison ever could.

Sentiment Summaries: Ask the LLM to analyze and synthesize public opinion for you. A prompt I've used effectively is, "Summarize the sentiment on Reddit and Twitter about our latest feature launch from last month. Pull out the top three points of praise and the top three criticisms."

Competitive Gap Analysis: Use the AI to find weaknesses in a competitor's product that your brand solves. Try something like, "Based on user reviews for [Competitor's Product], what are the most common feature requests or complaints? How does my product offer a better solution?"

Treat the LLM like a research assistant, not a search engine. This simple shift in mindset unlocks a whole new layer of strategic insight. You’re no longer just tracking brand visibility; you're actively using the AI to find your next competitive advantage.

To help you get started, I've put together a few examples of more complex prompts you can adapt for your own brand analysis.

Sample Advanced Prompts for Brand Analysis

Prompt Goal	Example Prompt Structure
Persona-Based Comparison	"Acting as a freelance graphic designer, compare Adobe Creative Cloud and Canva. Focus on which tool is better for quick social media assets versus complex branding projects."
Feature Gap Identification	"Analyze user reviews for Slack and Microsoft Teams. What are the top three features users wish Slack had that Teams currently offers?"
Negative Sentiment Analysis	"Find and categorize the most common negative complaints about [Your Brand's] customer service on Trustpilot and G2 from the last six months."
Market Positioning Check	"Describe [Your Brand] as if you were explaining it to a venture capitalist. What is its core value proposition, key differentiators, and biggest market threat?"

These are just templates, of course. The real power comes from tailoring them to your specific industry, product, and the questions you're trying to answer.

Automating Analysis at Scale

Running these kinds of complex prompts manually across multiple LLMs is a huge time-sink and simply won't scale. This is where using APIs becomes a total game-changer. By connecting directly to the APIs from platforms like OpenAI or Anthropic, you can automate the entire process.

Imagine sending thousands of targeted prompts and systematically collecting every response. This large-scale testing lets you build a robust dataset of how your brand is perceived in countless different scenarios.

You can then track trends over time, measure how a PR campaign affected AI-generated sentiment, or even A/B test brand messaging to see how LLMs respond to different angles. This approach turns what feels like qualitative insight into hard, quantitative data, giving you a much clearer picture of your brand's health in the AI ecosystem.

Turning LLM Monitoring Into an Actionable Strategy

Collecting data on how your brand shows up in LLMs is just step one. Raw numbers look nice on a dashboard, but they don't actually move the needle. The real magic happens when you turn those insights into a concrete plan that shapes your content, your SEO, and even your product development.

This is all about closing the loop. The data you get from a tool like Attensira can't just sit in a folder; it needs to become a direct feed for your marketing and comms teams. Every incorrect fact, every weird bit of negative sentiment—think of each one as a new ticket for your content team to resolve.

How to Prioritize Your Action Plan

Let's be realistic: you can't tackle everything at once. Not all LLM mentions carry the same weight, so you need a smart way to prioritize. A solid framework helps you focus your efforts where they'll make the biggest difference.

I’ve found the best approach is to triage issues based on potential damage versus potential opportunity. This way, you're putting out the most dangerous fires before you start building new bridges.

Priority 1: Fix Misinformation and Negative Sentiment. This is your red alert. If an LLM is spitting out wrong information about your pricing, features, or company history, you're losing customers. Period. The same goes for consistently surfacing negative reviews—that's a reputation crisis that needs immediate attention.

Priority 2: Fill High-Intent Content Gaps. Next up are the money-making queries. I’m talking about prompts like "best software for X" or "[Your Brand] vs. [Competitor]." If you're invisible in these conversations, you are actively leaving revenue on the table for someone else to grab.

Priority 3: Plug General Knowledge Gaps. Finally, look for those informational queries where your brand should be the definitive source. This could be as simple as updating your website's FAQ to directly answer common questions that LLMs are currently fumbling.

Weaving LLM Insights Into Your Broader Strategy

Once you have your priorities straight, the real work starts. Your LLM monitoring shouldn't be a side project; it needs to be woven directly into the fabric of your entire digital presence. Think of it as a powerful, real-time feedback engine.

And the scale here is just staggering. As of June 2025, ChatGPT alone boasts nearly 800 million weekly users worldwide. With over 60% of the AI market share, its influence on public perception is massive. This isn’t just another channel; it's a dominant force. You can dig deeper into ChatGPT's market dominance and user statistics to really grasp the numbers.

Your LLM visibility report should be treated just like your SEO audit. It's a living document that exposes weaknesses, shines a light on opportunities, and helps guide your content calendar for the next quarter.

This constant cycle of monitoring, analyzing, and acting is how you stay ahead. It ensures your brand’s story is told accurately, positively, and right where your customers are asking their most important questions. We've moved beyond simply tracking mentions; it’s now about actively shaping the conversation.

Got Questions About Tracking Your Brand in LLMs?

When you start digging into how your brand shows up in AI chats, a lot of questions pop up. It's a new frontier, after all. Can you actually change what an AI says about you? How often do you even need to look? Let's get into some of the practical things I hear most often.

One of the first questions is always, "Can I just correct the AI if it's wrong about my company?" The short answer is no, not directly. You can't just log in and edit an LLM's response like it's a Wikipedia article.

The real strategy is to play the long game. You influence the AI by publishing accurate, high-quality content on authoritative websites—the kind of places these models use for their training data. You're basically feeding the source, not editing the summary.

So, How Often Should I Be Checking This Stuff?

This is a big one. You don't want to get obsessive, but you can't just set it and forget it. For most brands, checking in every week or two is a great rhythm. It’s frequent enough to catch developing trends without drowning in the tiny, insignificant daily changes.

Of course, that all changes when something big is happening. You'll want to ramp things up significantly during key moments.

Launching a New Product? You should be checking daily. You need to see how the AI is interpreting the initial buzz and correct any misinformation before it spreads.

Running a Major PR Campaign? Keep a close eye on mentions to see if your key messages are actually landing and shaping the AI's understanding.

A Competitor Makes a Big Move? Check right away. You’ll want to know how their announcement impacts how you’re positioned side-by-side.

The point here isn't just a vanity check to see if your name pops up. You're trying to understand the context and sentiment of those mentions. Regular monitoring gives you a clear baseline, so when a real shift happens, you'll spot it immediately.

This is what a proactive approach to how to track your brand's visibility in ChatGPT & other top LLMs looks like. You stop reacting to problems and start shaping the narrative where it's increasingly being formed.

Ready to stop guessing and start measuring your brand’s AI presence? Attensira provides the tools you need to monitor, analyze, and optimize your visibility across all major LLMs. Get your actionable insights today at https://attensira.com.