Every day, millions of people ask ChatGPT, Claude, Perplexity, and other AI assistants about products, services, and companies in your industry. When someone types "What's the best CRM for small businesses?" or "Which marketing agency should I hire?", an AI model generates a response—and your company may or may not be mentioned.
More critically, if you ARE mentioned, you likely have no idea what's being said.
Are AI models positioning you as an industry leader or a budget alternative? Are they accurately describing your services, or spreading outdated information? The reality is that most companies have zero visibility into their AI reputation, even as these platforms increasingly influence purchase decisions and brand discovery.
This guide walks you through exactly how to uncover what AI models are saying about your company right now. You'll learn how to craft effective prompts, systematically query multiple AI platforms, analyze the sentiment and accuracy of responses, and track changes over time. Whether you're a founder wanting to understand your AI reputation, a marketer looking for content gaps, or an agency helping clients navigate AI visibility, these steps will give you actionable intelligence you can use immediately.
Step 1: Map Your AI Discovery Landscape
Before you can discover what AI models say about your company, you need to identify which platforms actually matter for your business. Not all AI models are created equal, and each one serves different user bases with varying levels of influence on your target audience.
Start by prioritizing the major players: ChatGPT (OpenAI), Claude (Anthropic), Perplexity, Google Gemini, Microsoft Copilot, and Meta AI. These platforms collectively handle hundreds of millions of queries daily, making them the most likely places where potential customers encounter information about your brand.
Industry-Specific Considerations: If you're in B2B software, ChatGPT and Claude dominate among tech-savvy professionals. For local businesses, Google Gemini integration with Search means it heavily influences discovery. E-commerce brands should prioritize Perplexity, which explicitly cites sources and often surfaces product recommendations. Understanding how AI models cite sources helps you optimize for each platform's unique approach.
Here's what makes this landscape complex: each AI model operates with different training data cutoffs and knowledge bases. ChatGPT's knowledge might be current through mid-2024, while Claude's training data has different recency characteristics. Perplexity supplements its base model with real-time web search, meaning it can surface very recent information about your brand. This variation means a single query can produce wildly different results across platforms.
Create accounts or access points for each platform you'll monitor. Most offer free tiers sufficient for discovery work, though you may want paid accounts for higher usage limits. Document your access credentials in a secure password manager—you'll be returning to these platforms repeatedly.
Establish Your Baseline: Before you start querying, document what these models SHOULD say about you. Write down your core value proposition, key products or services, target audience, and primary differentiators. This baseline becomes your reference point for evaluating accuracy and identifying gaps in how AI models represent your brand.
Create a simple tracking spreadsheet with columns for: AI Platform, Date Queried, Prompt Used, Response Summary, Sentiment, Accuracy Score, and Notes. This structure will prove invaluable as you systematically document findings across multiple platforms and time periods.
Step 2: Craft Strategic Discovery Prompts
The questions you ask AI models determine what you'll discover. Generic queries produce generic insights, while strategic prompts reveal exactly how these platforms position your brand in different contexts.
Direct Brand Queries: Start with the basics. Query "What is [Your Company Name]?" and "Tell me about [Your Company Name]" across all platforms. These direct questions establish whether AI models have any knowledge of your brand at all. Many smaller companies discover they simply don't exist in AI model knowledge bases—a critical insight that shapes your entire GEO strategy. If you're experiencing this, learn why AI models might be ignoring your company.
Follow up with more specific variations: "What does [Your Company Name] do?", "Who uses [Your Company Name]?", and "What are the main features of [Your Company Name]?" These variations often trigger different response patterns, revealing inconsistencies in how AI models understand your offering.
Competitive Comparison Prompts: This is where discovery gets strategically valuable. Ask "Compare [Your Company] vs [Top Competitor]" and "What's the difference between [Your Company] and [Competitor]?" These prompts reveal how AI models position you relative to established players in your space.
Try reverse competitive queries too: "Best alternatives to [Major Competitor]" and "Companies similar to [Competitor]." If you're not appearing in these responses when you should be, you've identified a significant visibility gap.
Category-Based Prompts: These reveal whether you appear in broader market conversations. Query "Best [your product category] tools", "Top [your industry] companies", and "Leading [your niche] solutions." Position matters here—being mentioned fifth versus first dramatically impacts potential customer awareness.
Test different category framings. If you're a project management tool, try "best project management software", "top team collaboration tools", and "leading productivity platforms." AI models may categorize you differently than you categorize yourself, revealing positioning opportunities or confusion.
Problem-Solution Prompts: These are gold for understanding purchase-intent visibility. Frame prompts around the problems your product solves: "How do I [problem your product addresses]?", "What's the best way to [user goal]?", and "I need to [specific use case], what should I use?"
These problem-solution queries mirror how real users actually search, making them the most valuable for understanding whether you appear in high-intent discovery moments.
Why Variation Matters: AI models are sensitive to phrasing, context, and query structure. "What's the best CRM?" versus "Which CRM should I choose?" versus "Top CRM recommendations" can produce meaningfully different results. The same core question, asked three different ways, might mention different companies or rank them in different orders.
Build a prompt library of 10-15 varied queries covering direct brand mentions, competitive comparisons, category positioning, and problem-solution scenarios. This comprehensive approach ensures you understand your AI visibility across the full customer journey.
Step 3: Execute Systematic AI Queries
With your platforms mapped and prompts crafted, it's time to systematically execute queries and document what you find. This step requires discipline—resist the urge to cherry-pick interesting results. Comprehensive data collection is what transforms this from casual research into actionable intelligence.
Open your first AI platform and start with your direct brand queries. Copy each prompt exactly as written in your prompt library, paste it into the platform, and capture the complete response. Don't summarize yet—you want the full text for later analysis.
Documentation Structure: For each query, record the AI platform name, exact prompt used, timestamp, and complete response. In your tracking spreadsheet, note whether your company was mentioned at all, what position you appeared in (if it's a list), what context surrounded the mention, and the overall tone of the description.
Move through all your prompts on one platform before switching to the next. This platform-by-platform approach helps you spot patterns in how individual AI models represent your brand versus jumping between platforms randomly. Understanding how AI models reference companies will help you interpret what you find.
Account for Personalization: AI models increasingly personalize responses based on user history and preferences. To check for this effect, run key prompts from different accounts or in private/incognito sessions. If responses vary significantly based on account, you're seeing personalization at work—and you need to understand both the personalized and non-personalized versions of your AI reputation.
Test the same prompts at different times of day or days of the week if you have the bandwidth. Some teams have reported that AI model responses shift based on server load, model version updates, or other temporal factors. While this level of testing isn't essential for initial discovery, it becomes valuable for ongoing monitoring.
Timestamp Everything: AI model knowledge bases update over time, and response patterns evolve as models are retrained or fine-tuned. Every entry in your tracking system should include the date and time of the query. This temporal data becomes crucial when you establish ongoing monitoring and need to identify when changes occurred.
Budget adequate time for this step. Querying 15 prompts across 6 platforms means 90 individual queries, each requiring careful documentation. Many teams find this takes 3-4 hours of focused work. Consider breaking it into multiple sessions rather than rushing through in one sitting—accuracy matters more than speed.
Step 4: Analyze Sentiment and Accuracy
With responses documented, you can now analyze what AI models are actually saying about your company. This analysis reveals not just whether you're mentioned, but how you're characterized—and whether that characterization aligns with reality.
Sentiment Categorization: Review each mention of your company and categorize it as positive, neutral, negative, or absent. Positive mentions highlight strengths, recommend your solution, or position you favorably. Neutral mentions acknowledge your existence without endorsement. Negative mentions cite limitations, criticisms, or unfavorable comparisons. Absent means you weren't mentioned when you should have been.
Be honest in your assessment. It's tempting to interpret neutral mentions as positive, but accurate categorization is what makes this analysis valuable. A mention that simply lists you among ten alternatives without context is neutral, not positive.
Accuracy Verification: Compare AI-generated descriptions against your actual offerings. Check product descriptions, pricing information, feature lists, target audience characterizations, and company background. Many companies discover significant inaccuracies here—outdated pricing, discontinued features still being mentioned, or services you've never offered being attributed to you. Learning how AI models verify information accuracy explains why these errors occur.
Create a simple accuracy score for each platform: Highly Accurate (95-100% correct), Mostly Accurate (80-94% correct), Partially Accurate (50-79% correct), or Inaccurate (below 50%). This scoring helps prioritize which inaccuracies matter most and where you need to focus correction efforts.
Positioning Pattern Analysis: Look across all responses to identify how AI models position your brand. Are you consistently described as premium or budget? Enterprise-focused or SMB-friendly? Feature-rich or simple? Established or emerging?
Compare this AI-perceived positioning against your intended brand positioning from Step 1. Gaps here reveal fundamental misalignments between how you present yourself and how AI models interpret and communicate your value proposition.
Flag Critical Issues: Some findings require immediate attention. Outdated information that misrepresents your current offerings, confusion with competitors (being described as the wrong company), damaging mischaracterizations (inaccurate criticisms or limitations), or complete absence from high-value category queries all represent critical issues that should drive your GEO strategy.
Document these critical issues separately with specific examples. "ChatGPT describes our pricing as $99/month when we changed to $79/month eight months ago" is actionable. "Pricing is wrong" is not.
Step 5: Benchmark Against Competitors
Understanding how AI models describe your company means nothing without competitive context. This step reveals whether you're winning or losing the AI visibility game in your market.
Take your prompt library and systematically substitute your top three competitors' names into each query. Run "What is [Competitor Name]?" across all platforms. Execute "Compare [Competitor A] vs [Competitor B]" for each competitor pair. Test "[Problem] solution" prompts to see which competitors appear.
Mention Frequency Analysis: Count how often each competitor appears across all responses compared to your own mentions. If you appear in 40% of category queries while your main competitor appears in 85%, you've quantified a significant visibility gap. Understanding how AI models select brands to mention helps explain these disparities.
Pay special attention to list positioning. Being mentioned first versus fifth in a "Best [category] tools" response dramatically impacts click-through and consideration. Track average position for you and each competitor across similar queries.
Competitive Positioning Insights: Analyze how AI models differentiate you from alternatives. What unique attributes or use cases do they associate with each competitor? If AI models consistently position Competitor A as "enterprise-grade" and Competitor B as "user-friendly," where do you fit in that spectrum?
Look for positioning inconsistencies. If one AI platform describes you as a premium solution while another positions you as budget-friendly, you've identified a messaging consistency problem that's confusing the AI landscape. Explore how AI models rank brands to understand the factors driving these differences.
Feature and Capability Gaps: Note what competitors are being credited for that you also offer but aren't recognized for. If AI models mention Competitor X's "advanced analytics" but never mention yours despite having similar capabilities, you've found a content gap to address.
This competitive intelligence extends beyond AI visibility—it reveals how the broader market perceives and differentiates players in your space, which should inform your overall positioning strategy.
Document Strategic Insights: Create a competitive summary that captures key findings: Who dominates AI visibility in your category? What positioning do they own? Where are the gaps you can exploit? Which competitors are overrepresented relative to their actual market position (suggesting strong GEO efforts)?
Step 6: Establish Ongoing Monitoring
AI visibility isn't static. Models get retrained, knowledge bases update, and your competitors' GEO efforts shift the landscape. One-time discovery tells you where you stand today—ongoing monitoring tells you whether you're gaining or losing ground.
Set a regular cadence for re-running your discovery prompts. Monthly monitoring works for most companies, providing enough time between checks to detect meaningful changes without overwhelming your team. Fast-moving companies or those actively investing in GEO might monitor weekly. Learn the best practices for how to track brand mentions in AI models systematically.
Tracking Changes Over Time: Each monitoring cycle, re-run your core prompt set across all platforms and document responses in your tracking system. Compare new responses against previous results to identify changes: Did you appear in a new category query? Did your position improve in competitive lists? Did descriptions become more or less accurate?
Create a simple change log noting significant shifts: "ChatGPT now mentions us in 'best [category]' queries where we were previously absent" or "Claude's description updated to reflect our new pricing model." These logs help you understand what's working and what isn't.
Correlate with Your Activities: Track AI visibility changes alongside your content publishing, PR activities, and GEO efforts. If you published a comprehensive guide about your product category and two weeks later start appearing in related AI queries, you've identified a successful tactic worth repeating.
Similarly, if visibility drops after a major website redesign or content restructuring, you've likely introduced technical or content issues that are hurting your AI representation. Discover how to optimize content for AI models to prevent these issues.
Scale with AI Visibility Tracking Tools: Manual monitoring works for initial discovery and small-scale efforts, but it doesn't scale efficiently. As you expand monitoring to more prompts, platforms, and competitors, automation becomes essential.
AI visibility tracking tools can query multiple platforms simultaneously, track mention frequency and positioning automatically, alert you to significant changes, and provide historical trending data. This automation transforms monitoring from a time-intensive manual process into a scalable system.
Create Alert Thresholds: Define what constitutes a significant change worth immediate attention. Dropping out of a high-value category query, being newly mentioned in a competitive comparison, or a shift from positive to negative sentiment all warrant alerts. Set up your monitoring system—whether manual or automated—to flag these threshold-crossing events.
Your AI Discovery Action Plan
You now have a systematic process for understanding exactly how AI models describe your company. Start by mapping your AI landscape and identifying which platforms matter most for your business. Craft strategic prompts covering direct brand queries, competitive comparisons, category positioning, and problem-solution scenarios. Execute queries systematically across all platforms, documenting responses with careful attention to sentiment and accuracy. Analyze what you find, benchmark against competitors to understand your relative visibility, and establish ongoing monitoring to track changes over time.
Quick-Start Checklist: Create accounts on ChatGPT, Claude, Perplexity, Google Gemini, Microsoft Copilot, and Meta AI. Develop 10-15 varied discovery prompts covering the categories outlined in Step 2. Run your initial query set across all platforms and document complete responses. Score sentiment and accuracy for each response using the frameworks from Step 4. Compare your results against your top three competitors using identical prompts. Schedule your first monthly monitoring review and set calendar reminders.
The insights you gather will reveal content gaps to fill, misconceptions to correct, and opportunities to strengthen your AI visibility. If ChatGPT consistently describes your pricing incorrectly, update your website's structured data and publish fresh content with accurate information. If you're absent from category queries where competitors appear, create authoritative content targeting those discovery moments. If sentiment is neutral when it should be positive, identify what's missing from your online presence that would strengthen AI model understanding of your value proposition.
For teams wanting to scale this process beyond manual tracking, AI visibility tracking tools automate monitoring across multiple platforms and alert you to changes in real-time. Start tracking your AI visibility today and see exactly where your brand appears across top AI platforms, uncover content opportunities based on competitive gaps, and publish SEO/GEO optimized articles that help AI models accurately represent your company.
The AI visibility landscape is still emerging, which means early movers gain disproportionate advantages. Companies that systematically monitor and optimize their AI presence now will dominate discovery moments as AI-powered search continues its rapid growth. Start with Step 1 today—your future customers are already asking AI models about solutions in your category, and you need to know what answers they're getting.
