AI chatbots and search assistants are now answering millions of questions about products, services, and brands every day. When someone asks ChatGPT, Claude, or Perplexity about solutions in your industry, is your brand being mentioned? More importantly, what are these AI models actually saying about you?
This emerging channel—AI visibility—represents a significant shift in how potential customers discover and evaluate brands. Unlike traditional search where you can check rankings, AI mentions happen inside conversations you can't see. Someone could be asking an AI assistant right now about the best tools in your category, and you have no idea if you're part of that conversation.
Think of it like this: imagine if Google search results were completely private, and you had no way to know where your website ranked or what appeared in the snippets. That's essentially where we are with AI-powered search today.
This guide walks you through the exact process of tracking how AI models mention your brand, from setting up your first monitoring system to analyzing sentiment and uncovering opportunities to improve your AI presence. By the end, you'll have a working system to monitor brand mentions across major AI platforms and actionable insights to strengthen your position in AI-generated responses.
Step 1: Identify Which AI Platforms Matter for Your Industry
Before you start tracking anything, you need to understand where the conversations about your industry are actually happening. Not all AI platforms are created equal, and your audience isn't using all of them with the same frequency.
The major players in AI-powered search and conversation currently include ChatGPT (OpenAI), Claude (Anthropic), Perplexity AI, Google Gemini, Microsoft Copilot, and Meta AI. Each platform has different strengths, user bases, and use cases.
ChatGPT dominates general-purpose queries and has the largest user base. If your customers are asking broad questions about solutions, they're likely starting here. Understanding how to track ChatGPT brand mentions should be a priority for most marketers.
Perplexity AI positions itself as an answer engine with citations, making it popular for research-oriented queries. B2B buyers often gravitate toward Perplexity when evaluating vendors.
Claude has gained traction among technical and professional users who value nuanced, detailed responses. If you're in SaaS, consulting, or complex B2B services, tracking Claude AI mentions matters.
Google Gemini and Microsoft Copilot integrate with existing ecosystems, meaning users already embedded in Google Workspace or Microsoft 365 will naturally use these tools.
Here's how to prioritize: Start by surveying your existing customers about which AI tools they actually use. Run a simple poll or add a question to your next customer interview. You're looking for patterns, not perfect data.
Next, consider your industry dynamics. B2B software buyers often research extensively before making decisions, making them more likely to use multiple AI platforms for vendor comparisons. Consumer-focused brands might find their audience concentrated on ChatGPT and Google Gemini.
Document your baseline understanding right now. Open each platform and ask a simple question: "What are the best [your category] tools?" Note which brands get mentioned, in what order, and with what context. This snapshot becomes your starting point for measuring progress.
For most brands, focusing on three to four platforms provides comprehensive coverage without overwhelming your tracking capacity. A typical starting combination might be ChatGPT, Perplexity, and Claude, with periodic checks on the others.
The key insight: your competitors are already being mentioned on these platforms, whether they're actively tracking it or not. The question is whether you'll know about it.
Step 2: Build Your Brand Mention Query Library
The queries you track determine the insights you'll uncover. A comprehensive query library captures the full spectrum of ways potential customers might discover your brand through AI conversations.
Start with direct brand queries—these are the easiest to track but often the least interesting. Someone asking "What is [Your Brand]?" already knows you exist. The real opportunity lies in queries where your brand could be mentioned but isn't guaranteed.
Category Questions: These queries ask about your industry without mentioning any specific brand. "What are the best project management tools?" or "How do I choose marketing automation software?" If you're not appearing in these responses, you're invisible to potential customers at the awareness stage.
Problem-Solution Prompts: Users often describe their challenge and ask for solutions. "I need to track my team's time across multiple projects" or "My website isn't ranking well in search results." These queries reveal whether AI models connect your brand to the specific problems you solve.
Competitor Comparison Queries: "What's better than [Competitor]?" or "[Competitor] alternatives" are high-intent searches where buyers are actively evaluating options. Learning how to track competitor AI mentions helps you understand your positioning in these critical conversations.
Feature-Specific Questions: "Which tools offer automated reporting?" or "Best platforms with API integrations" target users who have specific requirements. If your product has these features but AI models don't mention you, there's a content gap to fill.
Organize your query library by funnel stage. Awareness queries help you understand top-of-funnel visibility. Consideration queries show how you're positioned against alternatives. Decision queries reveal whether AI models recommend you when buyers are ready to choose.
Create variations for each core query. AI models respond differently to subtle phrasing changes. "Best email marketing tools" might yield different mentions than "Top email marketing platforms" or "Which email marketing software should I use?"
A solid starting library includes 20 to 30 queries across these categories. That's enough to reveal patterns without becoming unmanageable. You can always expand later as you identify gaps or new opportunities.
Document each query with its category, priority level, and expected mention outcome. This structure makes it easier to analyze results systematically rather than getting lost in individual responses.
Step 3: Set Up Systematic Monitoring Across AI Models
Consistent tracking beats sporadic checking every time. The goal is to establish a repeatable system that captures changes in how AI models mention your brand over time.
You have two main approaches: manual tracking or automated platforms. Manual tracking means systematically querying each AI platform yourself and documenting the responses. It's time-intensive but gives you deep qualitative insights into exactly what's being said and how it's framed.
If you're going the manual route, create a simple spreadsheet with columns for date, platform, query, whether your brand was mentioned, positioning (first, second, third mention or not mentioned), sentiment, and competitor mentions. Set aside dedicated time each week to run through your query library.
The challenge with manual tracking: it doesn't scale well beyond a few queries and platforms. If you're monitoring 20 queries across four platforms, that's 80 individual checks per tracking session. It adds up quickly.
Automated AI brand visibility tracking tools solve the scale problem by querying multiple AI models simultaneously and tracking changes automatically. Tools like Sight AI monitor how different AI platforms respond to your target queries, track sentiment shifts, and alert you when mention patterns change significantly.
Regardless of your approach, establish clear tracking frequency based on query priority. High-value queries—those that drive significant traffic or represent major customer decision points—deserve daily or every-other-day monitoring. Broader category queries can be checked weekly. Competitive comparison queries fall somewhere in between.
Set up alerts for significant changes. If you suddenly stop appearing in responses where you were previously mentioned consistently, that's a red flag. Similarly, if a competitor starts dominating mentions in your category, you need to know immediately.
Create a simple notification system. This could be as basic as a weekly email to yourself summarizing changes, or as sophisticated as automated alerts when mention frequency drops below a threshold. Many marketers are now implementing brand mentions automation to streamline this process.
The critical success factor: consistency matters more than perfection. Tracking 10 queries religiously every week provides more valuable insights than sporadically checking 50 queries whenever you remember.
Start small and build momentum. Begin with your top five queries on your two priority platforms. Once that rhythm is established, expand your coverage gradually.
Step 4: Analyze Mention Quality and Sentiment
Getting mentioned isn't enough. What AI models say about your brand matters just as much as whether they mention you at all.
Think about the difference between these two mentions: "Brand X is a popular option in this space" versus "Brand X is the leading solution for teams that need advanced automation and robust integrations." The first is a neutral acknowledgment. The second is a strong endorsement with specific value propositions.
Start your analysis by categorizing sentiment. Positive mentions include recommendations, praise, or positioning as a top choice. Neutral mentions acknowledge your existence without endorsement. Negative mentions highlight limitations, criticisms, or position you as inferior to alternatives. Understanding how to track brand sentiment online is crucial for this analysis.
Pay close attention to the context surrounding your mention. Are you listed first, suggesting primary consideration? Mentioned in the middle of a list as one of several options? Or appearing as an afterthought—"you might also consider Brand X"?
Evaluate the specificity of mentions. Generic references like "Brand X offers project management features" provide less value than detailed descriptions: "Brand X excels at cross-functional collaboration with real-time updates and customizable workflows." Specific mentions indicate the AI model has substantial information about your capabilities.
Compare your mention context against competitors in the same response. If an AI model describes Competitor A with detailed features and benefits but mentions you with only a brief acknowledgment, that disparity reveals a content gap you need to address.
Look for what information sources AI models cite when discussing your brand or category. Perplexity often includes citations, giving you direct insight into which content pieces are influencing AI responses. Learning how to track Perplexity AI citations can reveal exactly which sources are shaping your brand perception.
Track sentiment trends over time. A single negative mention isn't necessarily concerning, but a pattern of declining sentiment signals a reputation issue that needs attention. Similarly, improving sentiment indicates your optimization efforts are working.
Document specific phrases AI models use to describe your brand. These become valuable insights for understanding your perceived positioning. If AI consistently describes you as "budget-friendly" but you're trying to position as "premium," there's a messaging disconnect to address.
The bottom line: mentions without context are vanity metrics. Understanding what's being said and how you're positioned transforms raw data into actionable intelligence.
Step 5: Identify Content Gaps and Optimization Opportunities
The real value of tracking AI mentions emerges when you identify patterns in where you're not being mentioned—and figure out why.
Start by mapping competitor mentions against your own. For each query where competitors appear but you don't, ask: what information do they have that we're missing? Often, the answer is straightforward: they have content directly addressing that query, and you don't.
Let's say AI models consistently mention Competitor B when users ask about integration capabilities, but your product has equally robust integrations. The issue isn't your product—it's that you haven't created clear, accessible content about your integration features that AI models can reference.
Analyze the information sources AI models cite. If they're pulling from competitor blog posts, help center articles, or third-party reviews, those content types are working. You need similar content covering your strengths. Understanding how AI models choose brands to recommend helps you create content that gets noticed.
Look for queries where no one in your category gets mentioned consistently. These represent content voids—opportunities to create authoritative resources that AI models will reference. Being the first comprehensive resource on a topic gives you significant advantage in AI visibility.
Pay attention to the questions AI models struggle to answer about your category. When responses are vague or generic, it indicates insufficient information exists. Creating detailed, authoritative content filling these gaps positions you as the go-to source.
Create a prioritized action list based on three factors: query volume (how often this question gets asked), business impact (how valuable this mention would be), and gap size (how far behind competitors you currently are).
High Priority: High-volume queries with significant business impact where you're currently absent. These deserve immediate content development.
Medium Priority: Queries where you're mentioned but poorly positioned, or lower-volume queries with high business impact.
Lower Priority: Queries where you're already well-represented, or very low-volume queries with minimal business impact.
For each content gap, define what needs to be created. This might be a comprehensive guide, a comparison page, a detailed feature breakdown, case studies demonstrating specific use cases, or FAQ content addressing common questions. The goal is to improve brand mentions in AI responses through strategic content creation.
The pattern you'll often discover: AI models favor clear, structured, authoritative content that directly answers questions. Dense marketing copy and vague descriptions don't get referenced. Specific, helpful content does.
Step 6: Create Your AI Visibility Reporting Dashboard
Tracking data without structure leads to information overload. A simple reporting dashboard transforms raw monitoring into strategic insights.
Define your core metrics first. Mention frequency tracks how often your brand appears across your query library. If you're monitoring 25 queries and appear in 15 responses, your mention frequency is 60 percent. Track this metric over time to see if your visibility is improving.
Sentiment score provides a qualitative measure of how AI models talk about you. Create a simple scale: positive mentions get +1, neutral get 0, negative get -1. Your average sentiment score reveals whether mentions are helping or hurting your brand.
Share of voice compares your mention frequency against competitors. If AI models mention three brands when answering category queries, and you're one of them 40 percent of the time while competitors each get 30 percent, you're leading in share of voice.
Trend direction matters more than absolute numbers. A brand moving from 10 percent to 25 percent mention frequency shows strong momentum, even if competitors still lead. Declining trends signal problems that need immediate attention.
Build a simple reporting template you can update weekly. Include sections for overall metrics, notable changes from the previous period, new queries where you gained or lost mentions, and sentiment shifts worth investigating. Many teams are now using dedicated LLM brand tracking software to automate this reporting.
Schedule two types of reviews: weekly snapshots and monthly deep dives. Weekly reviews take 15 to 20 minutes and focus on significant changes requiring immediate response. Monthly reviews involve comprehensive analysis of trends, competitive positioning shifts, and strategic adjustments.
Connect AI visibility metrics to business outcomes whenever possible. Track whether increased mentions correlate with traffic growth, lead generation, or brand search volume. This connection helps justify continued investment in AI visibility optimization.
Share insights with relevant teams. Your content team needs to know about mention gaps. Your product team benefits from understanding how AI models describe your features. Your executive team cares about competitive positioning trends.
Keep your dashboard simple. Three to five key metrics tracked consistently beat ten metrics tracked sporadically. You can always add complexity later as your tracking matures.
Your Next Steps in AI Visibility
Tracking AI mentions of your brand is no longer optional—it's becoming as essential as monitoring search rankings. The difference is that AI-powered search is growing rapidly while traditional search behavior is evolving.
Start with Step 1 today by identifying your priority AI platforms. Don't overthink it—choose the three platforms where your target audience is most likely to be asking questions about your category. Then systematically work through building your query library and establishing consistent monitoring.
The brands that understand their AI visibility now will have a significant advantage as AI-powered search continues to grow. Every week you wait is another week of conversations happening without your knowledge, mentions you're missing, and opportunities going to competitors who are paying attention.
Your quick-start checklist: Identify your top three AI platforms. Create 20 initial tracking queries covering category questions, problem-solution prompts, and competitor comparisons. Set up your first monitoring system, whether that's a simple spreadsheet or an automated platform. Run your baseline analysis to understand where you stand today. Schedule your first weekly review and commit to the cadence.
The most important insight: your AI presence is being shaped right now, whether you're actively managing it or not. Every piece of content you publish, every review customers write, every mention across the web contributes to how AI models understand and represent your brand.
The question isn't whether AI will influence how customers discover and evaluate your brand—it already does. The question is whether you'll have visibility into those conversations and the ability to improve your positioning within them.
Start tracking your AI visibility today and see exactly where your brand appears across top AI platforms. Stop guessing how AI models like ChatGPT and Claude talk about your brand—get visibility into every mention, track content opportunities, and automate your path to organic traffic growth.
