When someone asks ChatGPT, Claude, or Perplexity about products in your industry, does your brand appear in the response? For most companies, the honest answer is "I have no idea." This blind spot represents one of the biggest missed opportunities in modern marketing.
As AI-powered search tools handle millions of queries daily, brands that aren't tracking their presence in LLM responses are flying blind while competitors gain ground. Unlike traditional SEO where you can check Google rankings in seconds, monitoring how large language models discuss your brand requires a fundamentally different approach.
Here's the thing: LLMs don't have static rankings. They generate dynamic responses based on their training data, context, and the specific way users phrase their questions. The same brand might appear in one response and vanish completely in another, depending on how the question is asked.
This guide walks you through exactly how to systematically track your brand mentions across major AI platforms, from setting up your monitoring framework to analyzing sentiment and identifying content gaps that could boost your AI visibility. Think of it as building your own AI visibility radar system, one that reveals not just whether you're mentioned, but how you're positioned against competitors.
Step 1: Identify Which LLMs Matter Most for Your Industry
Not all AI platforms deserve equal attention. Your first step is figuring out where your target audience actually goes when they need AI-powered answers.
Map the major players: Start with the big five—ChatGPT, Claude, Perplexity AI, Google Gemini, and Microsoft Copilot. These platforms collectively handle the vast majority of AI search queries. But here's where it gets interesting: different audiences gravitate toward different tools.
Technical audiences often prefer Claude for its nuanced reasoning. Researchers lean toward Perplexity for its citation capabilities. Business professionals increasingly use Copilot since it's integrated into their Microsoft workflow. ChatGPT remains the most widely adopted across demographics. Understanding how to track Claude AI brand mentions alongside other platforms gives you comprehensive coverage.
Research industry-specific tools: Beyond the mainstream platforms, specialized AI tools may be more relevant to your space. If you're in healthcare, medical AI assistants might reference brands. In software development, coding assistants like GitHub Copilot or Cursor could mention tools and frameworks. Financial services companies should track AI advisors that discuss investment platforms.
The key question: Where do your potential customers go when they're evaluating solutions like yours?
Prioritize based on your buyer journey: Create a simple matrix. Which platforms do prospects use during awareness? During consideration? During decision-making? A B2B SaaS company might find that technical evaluators use Claude for deep analysis, while executives use ChatGPT for quick overviews.
Build your tracking priority list: Rank platforms into three tiers. Tier 1 platforms get daily or weekly monitoring. Tier 2 platforms get bi-weekly checks. Tier 3 platforms get monthly spot checks. This prevents monitoring fatigue while ensuring you catch important shifts.
Your priority list should document not just which platforms matter, but why they matter to your specific audience. This context becomes crucial when you're deciding where to focus content optimization efforts later.
Step 2: Build Your Brand Mention Query Library
This is where most companies stumble. They check a few obvious queries like "best [product category]" and call it done. But LLM tracking requires a comprehensive prompt strategy that mirrors how real users actually search.
Start with comparison queries: These are gold mines for understanding competitive positioning. Create prompts like "Compare [Your Brand] vs [Competitor]" and "What's the difference between [Your Brand] and [Competitor]?" Don't just focus on your top competitor—include emerging players and established leaders.
The responses reveal not just whether you're mentioned, but how AI models position you relative to alternatives. Are you the budget option? The premium choice? The innovative newcomer?
Add recommendation requests: These simulate buying intent. Try prompts like "What's the best [product category] for [specific use case]?" or "Recommend a [product type] for [specific need]." Vary the use cases to cover your full range of ideal customer profiles.
For example, if you sell project management software, test prompts for small teams, enterprise deployments, remote-first companies, and agencies. Each use case might surface different competitors and positioning.
Include problem-solution prompts: Frame queries around the problems you solve: "How can I [achieve outcome]?" or "What tools help with [specific challenge]?" These often reveal whether AI models associate your brand with solving particular problems—a critical factor in AI visibility.
Layer in competitor-focused queries: Ask about competitors directly: "Tell me about [Competitor Brand]" or "What are alternatives to [Competitor]?" These queries reveal your share of voice in competitor contexts. If someone researches a competitor, does your brand appear as an alternative?
Document exact wording: This is critical. LLMs are sensitive to phrasing. "Best CRM software" might yield different results than "Top CRM tools" or "Most popular CRM platforms." Save your exact prompt wording in a spreadsheet or document. Mastering LLM prompt engineering for brand visibility helps you craft queries that reveal the most actionable insights.
Aim for 20-30 core prompts that cover your key use cases, competitor landscape, and customer journey stages. This library becomes your consistent tracking framework. Every week or month, you'll run these same queries to spot changes in how AI models discuss your brand.
Step 3: Establish Your Baseline Brand Visibility Score
Now comes the detective work. You're about to discover exactly where you stand in the AI visibility landscape—and the results might surprise you.
Run your complete query library: Take each prompt from your library and test it across all your priority platforms. This is time-intensive but essential. Copy each AI response into a tracking document. Note the date, platform, exact prompt used, and the full response text.
You're looking for three things: Does your brand appear at all? If so, in what context? And what's the sentiment?
Create a mention matrix: Build a simple spreadsheet with prompts in rows and platforms in columns. Mark each cell with your mention status: "Featured" if you're prominently discussed, "Mentioned" if you appear in a list, "Absent" if you don't appear at all. This visual map reveals patterns instantly.
You might discover that you dominate certain query types but are invisible in others. Or that Claude consistently mentions you while ChatGPT doesn't. These patterns guide your content strategy. If you're finding gaps, our guide on why your brand not appearing in AI responses explains common causes and fixes.
Document competitive positioning: When competitors appear, note their placement. Are they listed first? Described in more detail? Associated with specific strengths? If an AI response lists five solutions and you're fifth, that's different from being second.
Pay special attention to how AI models describe competitive advantages. If Perplexity says "Competitor X is known for ease of use while Competitor Y offers advanced features," where does your brand fit in that narrative? Or are you missing from it entirely?
Calculate your baseline metrics: Create simple percentages. If you ran 25 prompts and appeared in 10 responses, your mention rate is 40%. Of those 10 mentions, if 7 were positive, 2 were neutral, and 1 was negative, you can track sentiment distribution.
Also calculate competitive share of voice. If a query mentions five brands including yours, you have 20% share of that response. Average this across all queries where competitors appear.
These baseline numbers become your benchmark. Three months from now, when you've published GEO-optimized content and improved your structured data, you'll measure progress against these starting metrics. Without this baseline, you're just guessing whether your efforts are working.
Step 4: Set Up Automated Monitoring and Alerts
Manual tracking works for establishing your baseline, but it doesn't scale. You need a system that continuously monitors your AI visibility without consuming hours each week.
Evaluate your tracking approach: You have two paths. The manual route involves scheduling recurring calendar blocks to re-run your query library. This works if you have limited queries and only track a few platforms. The automated route uses specialized software that continuously monitors LLM responses. Dedicated LLM brand tracking software can handle this at scale.
Manual tracking makes sense when you're just starting or have a small query set. But if you're tracking 20+ prompts across 4+ platforms, the time investment quickly becomes unsustainable. You'll also struggle with consistency—it's easy to skip a week or phrase prompts slightly differently.
Configure monitoring intervals: How often should you check? It depends on your competitive landscape and content velocity. If you're in a fast-moving market where competitors publish daily, weekly monitoring catches important shifts. If your industry moves slower, bi-weekly or monthly checks may suffice. For time-sensitive industries, real time brand monitoring across LLMs ensures you never miss critical mentions.
Set different intervals for different query types. High-intent commercial queries deserve more frequent monitoring than general awareness queries. Competitive comparison prompts should be checked more often than problem-solution queries.
Establish alert thresholds: You need to know when something significant changes. Set up notifications for major shifts: your brand appears in a response where it was previously absent, your mention rate drops by more than 15%, negative sentiment appears where you previously had positive mentions, or a new competitor starts appearing consistently.
These alerts prevent you from missing critical moments. If a competitor launches a major content campaign that boosts their AI visibility, you want to know immediately, not discover it in your monthly review.
Integrate with existing dashboards: AI visibility metrics shouldn't live in isolation. If you use marketing analytics tools, find ways to bring LLM tracking data into your existing reports. This might mean manual data entry initially, or using specialized AI visibility tracking software that offers integrations.
The goal is making AI visibility metrics as visible and actionable as your Google Analytics traffic or SEO rankings. When your team reviews marketing performance, they should see AI mention rates alongside organic traffic growth and conversion metrics.
Step 5: Analyze Sentiment and Context Quality
Appearing in an LLM response is just the starting line. What really matters is how you're positioned and what associations AI models make with your brand.
Categorize every mention: Go beyond simple "positive or negative" labels. Use a more nuanced framework. Positive mentions describe your brand favorably, highlight strengths, or recommend you for specific use cases. Neutral mentions list you without judgment, often in comparison tables or option lists. Negative mentions point out limitations, criticisms, or situations where competitors are preferable.
But here's the twist: "absent" is also a sentiment category. When you don't appear in a relevant query, that absence tells you something important about your AI visibility gaps.
Evaluate your positioning context: When you do appear, are you the hero or the footnote? Leader positioning means you're described as a top choice, market leader, or primary recommendation. Alternative positioning means you're mentioned as "another option" or "also consider." Afterthought positioning means you appear in a long list without specific commentary.
The positioning context often matters more than raw mention frequency. Being described as "the leading solution for enterprise teams" in three responses is more valuable than appearing in ten responses as an undifferentiated list item.
Map feature and attribute associations: What specific qualities do LLMs associate with your brand? Create a list of every attribute, feature, or characteristic mentioned across all responses. You might discover that AI models consistently describe you as "affordable" but never mention your advanced features. Or that they associate you with one use case while missing three others you support.
These associations reveal how AI models have learned to categorize you based on their training data. If the associations don't match your positioning strategy, you've identified a content gap. Understanding brand sentiment in AI responses helps you decode these patterns systematically.
Run competitive sentiment comparisons: Don't analyze your mentions in isolation. How does your sentiment profile compare to top competitors? If competitors consistently get "leader" positioning while you get "alternative" positioning, that gap represents an opportunity.
Create a simple competitive sentiment scorecard. Rank brands by positive mention percentage, leader positioning frequency, and feature association richness. This reveals not just where you stand, but how much ground you need to gain.
Step 6: Identify Content Gaps and Optimization Opportunities
Your tracking data is now revealing a treasure map of opportunities. Every absent mention, every weak positioning, every competitor advantage represents a content gap you can fill.
Map your visibility gaps: Create a priority matrix of queries where you should appear but don't. Focus especially on high-intent queries where competitors appear prominently. These represent immediate opportunities—people are asking questions that should surface your brand, but AI models aren't making the connection.
For each gap, ask why you're absent. Is it because you don't actually solve that use case? Or because you haven't published content that clearly establishes your relevance?
Analyze competitor content advantages: When a competitor appears in a query where you don't, investigate their content. What have they published that establishes their authority in that area? Often you'll find comprehensive guides, case studies, or comparison pages that clearly signal their relevance to AI training data.
This isn't about copying competitors. It's about understanding what content signals AI models are responding to, then creating your own authoritative content that establishes your expertise.
Prioritize by intent and volume: Not all content gaps deserve equal attention. Focus first on queries that indicate buying intent and represent your ideal customer use cases. A query like "best [product category] for [your target customer]" deserves higher priority than a general awareness query.
Also consider query patterns. If you're absent from five related queries about the same topic, creating one comprehensive piece of content might improve visibility across all five.
Plan GEO-optimized content: Generative Engine Optimization means creating content specifically designed to improve how AI models understand and reference your brand. This includes comprehensive product information pages, clear differentiation statements, expert-level educational content, and structured data markup that helps AI systems extract key facts. Learn proven strategies to improve brand mentions in AI responses through targeted content optimization.
Think about the questions AI models need answered to confidently recommend your brand. What makes you different? What specific problems do you solve? What results have customers achieved? Create content that answers these questions with clarity and authority.
Your content roadmap should directly map to your visibility gaps. Every piece you publish should target specific queries where you're currently absent or weakly positioned.
Step 7: Create a Reporting Cadence and Track Progress
Tracking without reporting is just data collection. You need a systematic way to measure progress and communicate insights across your team.
Build your monthly AI visibility report: Create a template that tracks your core metrics over time. Include your overall mention rate, sentiment distribution, competitive share of voice, and positioning quality. Add sections for biggest wins (new prominent mentions), biggest losses (queries where you disappeared), and emerging patterns.
Make the report visual. Use charts to show mention rate trends, sentiment breakdowns, and competitive comparisons. A line graph showing your mention rate climbing from 35% to 52% over three months tells a compelling story.
Correlate visibility with content efforts: This is where the magic happens. Track when you publish new content and watch for corresponding changes in AI visibility. If you published a comprehensive guide on a topic in March and started appearing in related queries by April, you've proven the connection.
Document these correlations in your report. They help justify continued investment in GEO-optimized content and prove that AI visibility isn't random—it responds to strategic content efforts.
Share insights cross-functionally: Your content team needs to know which topics boost AI visibility. Your product team should understand how AI models describe your features. Your competitive intelligence team wants to see how competitor positioning evolves in AI responses.
Create a monthly stakeholder summary that highlights the most actionable insights. Keep it concise—three key findings, two priority opportunities, one success story. Make it scannable for busy executives while linking to detailed data for those who want to dig deeper. The right brand sentiment tracking software can automate much of this reporting workflow.
Set quarterly improvement goals: Establish specific, measurable targets. Increase mention rate from 40% to 55%. Improve positive sentiment from 60% to 75% of mentions. Gain 10 percentage points in competitive share of voice. Achieve "leader" positioning in at least five high-priority queries.
These goals keep your AI visibility efforts focused and measurable. They also help you allocate resources—if you're not making progress toward your goals, you know you need to adjust your content strategy or increase investment.
Review goals quarterly and adjust based on what's working. If certain types of content consistently boost visibility, double down. If you're struggling to move specific metrics, investigate why and try new approaches.
Putting It All Together
Tracking your brand in LLM responses isn't a one-time audit—it's an ongoing discipline that separates AI-visible brands from invisible ones. The companies that start systematic tracking today will compound their visibility advantage as AI platforms become the default way people discover and evaluate products.
Use this checklist to maintain your monitoring discipline: ✓ Query library updated monthly with new prompt variations ✓ Weekly automated scans across priority platforms ✓ Monthly sentiment analysis and competitive comparison ✓ Quarterly content gap assessment and GEO content planning ✓ Dashboard tracking visibility score trends over time.
The patterns you discover through consistent tracking become strategic intelligence. You'll spot emerging competitors before they dominate AI responses. You'll identify content opportunities while they're still low-hanging fruit. You'll measure the ROI of your content investments with unprecedented clarity.
But here's what makes this approach powerful: you're not just tracking for tracking's sake. Every data point informs action. Every visibility gap becomes a content opportunity. Every sentiment insight shapes your messaging. Every competitive analysis reveals differentiation opportunities.
The brands winning in AI visibility aren't lucky—they're systematic. They track consistently, analyze deeply, and act decisively on what they learn. They understand that AI visibility compounds over time as more content reinforces their authority and relevance in training data.
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. Start tracking your AI visibility today and see exactly where your brand appears across top AI platforms.
The question isn't whether AI-powered search will matter to your business. It already does. The question is whether you'll track and optimize your presence systematically, or let competitors claim the visibility advantage while you operate blind.
