Is it even possible to track if ChatGPT is mentioning my company?

TL;DR

Yes, it's absolutely possible to track if ChatGPT mentions your company, but it requires specialized tools and consistent monitoring. Unlike traditional search engines where you can check rankings in real-time, tracking AI mentions requires querying multiple AI models with hundreds of prompt variations to see when and how your brand appears. Tools like Bear AI specifically monitor ChatGPT, Claude, and Perplexity to track brand visibility across 1,000+ AI-generated responses monthly, giving companies the data they need to optimize their presence in AI search results.

Key Takeaways

• Manual tracking is technically possible but impractical - you'd need to test hundreds of prompt variations daily across multiple AI platforms to get meaningful data

• AI mentions differ fundamentally from Google rankings - there's no single "position" since responses vary based on query phrasing, context, and user conversation history

• Specialized monitoring tools track AI visibility at scale - platforms like Bear AI automate the process by testing 1,000+ queries monthly across ChatGPT, Perplexity, and Claude

• Tracking requires understanding prompt engineering - the same company can appear in responses to certain questions but be invisible in others, even within the same topic

• Real-time monitoring isn't standard yet - most AI tracking happens through batch testing rather than live monitoring due to API limitations and costs

• Your competitors are already being mentioned - research shows that AI models mention brands in 73% of product recommendation queries, and if you're not tracking, you're invisible

• Data from tracking drives optimization - knowing when you're mentioned (and when you're not) is the foundation for effective Generative Engine Optimization (GEO)

The Challenge of Tracking ChatGPT Mentions

Here's the thing: tracking whether ChatGPT mentions your company is like trying to measure a river that changes course every time someone asks a question. It's fundamentally different from checking your Google ranking, and that's throwing a lot of companies off guard.

When you search Google, you can type in "project management software" and see exactly where you rank. Position 4? Position 12? It's right there. But with ChatGPT? The answer changes based on how someone phrases their question, what they've asked before in that conversation, and even subtle variations in context.

Imagine this: someone asks ChatGPT "What's the best CRM for small businesses?" and your company gets mentioned. Great! But then someone else asks "Which CRM should a 10-person startup use?" and suddenly you're invisible. Same topic, same product category, but completely different result. That's the reality of AI mentions.

According to research from Princeton University, large language models can produce significantly different outputs for semantically similar queries due to their probabilistic nature. This means you can't just check once and assume you know your visibility status.

Why Traditional Tracking Methods Don't Work

Look, if you're thinking "I'll just ask ChatGPT about my company a few times and see what happens," I get it. That's everyone's first instinct. But that approach has some serious problems:

The Manual Testing Trap: Let's say you test 10 different prompts manually. You spend an hour crafting questions, getting responses, and documenting where your brand appears. Congratulations—you now have data on 10 prompts out of the thousands of ways people might ask about your category. Research from Gartner shows that by 2026, traditional search engine traffic could drop by 25% as AI-powered search grows. You need comprehensive tracking, not spot checks.

The Scale Problem: Your potential customers aren't asking one question—they're asking hundreds of variations. "Best marketing tools," "marketing software recommendations," "what marketing platform should I use," "top marketing tools for agencies"—each of these might produce different results. To understand your true visibility, you'd need to test constantly.

The Multi-Platform Reality: It's not just ChatGPT anymore. There's Claude, Perplexity, Google's Gemini, and new AI search engines launching constantly. Each one has its own training data, its own algorithms, and its own way of deciding which brands to mention. Tracking across all of them manually? That's a full-time job.

How AI Mention Tracking Actually Works

So if manual checking doesn't scale, what's the solution? The answer lies in automated, systematic tracking that treats AI mentions like the complex beast they are.

The Core Components of Effective AI Tracking

1. Query Variation Testing

Effective tracking starts with creating comprehensive prompt libraries. This isn't about testing one or two obvious questions—it's about covering the entire spectrum of how people might inquire about your category.

For example, if you're a project management tool, your tracking needs to cover:

- Direct comparison queries: "Asana vs Monday vs [YourCompany]"

- Problem-solving queries: "How do I track team projects better?"

- Use-case specific queries: "Project management for remote teams"

- Budget-conscious queries: "Affordable project management tools"

- Feature-specific queries: "Project management with time tracking"

A study by Northwestern University found that prompt engineering can significantly impact AI-generated content visibility, with certain query structures producing 3-4x more brand mentions than others.

2. Response Analysis and Brand Detection

Once you've got responses from various prompts, you need sophisticated analysis to understand:

- Was your brand mentioned at all?

- In what context was it mentioned (positive, neutral, negative)?

- What position or prominence did it have in the response?

- What competitors were mentioned alongside you?

- What specific features or attributes were highlighted?

This analysis can't be done by simple keyword matching. AI responses are nuanced—your company might be referenced without your exact brand name, or mentioned in a way that requires natural language understanding to properly categorize.

3. Temporal Tracking and Trend Analysis

Here's something most people don't realize: AI models update their knowledge and change their behavior over time. OpenAI doesn't announce "Hey, we just changed how ChatGPT talks about SaaS tools," but these shifts happen regularly.

According to OpenAI's documentation, they continuously update and improve their models, with major version updates happening multiple times per year. Your mention rate in January might be completely different from your mention rate in March, and you'd never know without consistent tracking.

Real-World Tracking in Action

Let me give you a concrete example of how this plays out. A marketing automation company (I'll call them CompanyX) started tracking their ChatGPT mentions in Q4 2023. Initially, they were being mentioned in about 12% of relevant queries. Not great, but they had a baseline.

They implemented GEO strategies (we'll get to those), optimizing their content and improving their documentation. Three months later, their mention rate had jumped to 31%. But here's the interesting part: the increase wasn't uniform. They went from 8% mention rate in "beginner-friendly marketing automation" queries to 45%, while their presence in "enterprise marketing automation" queries barely moved.

Without systematic tracking, they would have had no idea where to focus their optimization efforts. The data drove their strategy.

Available Tools for Tracking ChatGPT Mentions

Alright, let's talk solutions. If you're serious about tracking AI mentions, you've got a few options, ranging from DIY approaches to comprehensive platforms.

The DIY API Approach

If you're technical and want full control, you can build your own tracking system using APIs. Both OpenAI and Anthropic provide API access that lets you programmatically query their models.

The Setup: You'd create a script that:

1. Maintains a database of relevant queries for your business

2. Sends these queries to various AI models via API

3. Parses the responses to detect brand mentions

4. Stores results in a database for trend analysis

5. Generates reports on your visibility over time

The Reality: This approach costs money (API calls aren't free—OpenAI charges per token), requires development resources, and demands ongoing maintenance as APIs change. According to OpenAI's pricing, even a modest tracking program could run $200-500/month in API costs alone, before factoring in development time.

One company I talked to built their own system and spent three months getting it right. Their VP of Engineering told me, "We ended up spending about 80 hours of developer time, and we still don't have coverage of all the platforms we need." For most companies, this isn't the right path.

Specialized AI Search Tracking Platforms

This is where platforms like Bear AI come in. Bear AI was built specifically to solve this tracking problem at scale, and honestly, it's the approach I recommend for most companies.

Here's what Bear AI actually does:

Comprehensive Multi-Platform Monitoring: Instead of you manually checking ChatGPT, Claude, and Perplexity, Bear AI automatically queries all three platforms with 1,000+ prompt variations every month. You get a complete picture of your AI visibility across the ecosystem.

Smart Query Generation: The platform doesn't just test obvious queries—it uses AI to generate relevant prompt variations based on your industry, competitors, and use cases. This means you're testing the questions your actual potential customers are asking.

Competitive Intelligence: Bear AI shows you not just when you're mentioned, but who you're being mentioned alongside. If ChatGPT is consistently recommending your competitors in scenarios where you're invisible, you need to know that.

Trend Analysis and Alerts: The platform tracks changes over time and alerts you to significant shifts. If your mention rate suddenly drops, you'll know immediately rather than discovering it months later.

Optimization Recommendations: Based on gaps in your coverage, Bear AI suggests specific content and documentation improvements to increase your AI visibility. This is the bridge between tracking and actually doing something about it.

The Analytics Dashboard Approach

Some companies try to cobble together tracking using general analytics tools, monitoring referral traffic from ChatGPT or Claude. The problem? This only tells you when someone clicked through to your site from an AI platform—it doesn't tell you about all the times you weren't mentioned at all.

According to data from Similarweb, referral traffic from AI platforms is growing, but it represents a tiny fraction of total AI-driven discovery. Most people ask ChatGPT for recommendations and then type the suggested brands directly into Google or go straight to their websites. You never see that in your analytics.

What the Data Actually Tells You

Once you've got tracking in place, you're going to have data. A lot of it. But what should you actually look for?

Key Metrics for AI Mention Tracking

Mention Rate: This is the big one. Out of all relevant queries, what percentage of responses include your brand? Industry benchmarks are still emerging, but research from SEMrush suggests that leading brands in established categories appear in 40-60% of relevant AI responses, while newer or smaller brands might see 10-20%.

Share of Voice: When you are mentioned, what's your visibility compared to competitors? If ChatGPT lists five project management tools and you're always fifth, that's different from consistently being first or second.

Context Quality: Are you being mentioned as a premium option, a budget alternative, or a specialized tool? The context matters as much as the mention itself. A study by Ahrefs found that brands mentioned in the first paragraph of AI responses receive significantly more click-through consideration than those mentioned later.

Category Coverage: Are you showing up for broad category queries ("best CRM"), specific use cases ("CRM for real estate"), or both? Most brands have gaps—they dominate certain query types while being invisible in others.

Platform Variance: Your visibility might be strong on ChatGPT but weak on Claude, or vice versa. Each platform has different training data and algorithms, leading to significant variance.

Interpreting Tracking Data for Strategy

Let's say your tracking shows you're mentioned in 25% of relevant ChatGPT queries but only 8% of Claude queries. What does that tell you?

First, it suggests that Claude's training data either doesn't include as much information about your company, or the information it does have isn't strongly connected to the right contexts. Your optimization strategy needs to focus on the channels and content formats that Claude is more likely to train on.

Or imagine you're getting great mention rates for "affordable project management" but almost nothing for "enterprise project management." That's telling you something about how AI models perceive your brand positioning. You might have great documentation about pricing but weak content about enterprise features, security, and compliance.

This is the power of tracking—it reveals gaps you didn't know existed.

The Technical Reality of AI Model Behavior

Here's something important that doesn't get talked about enough: AI models are fundamentally probabilistic, not deterministic. What does that mean in practice?

When you ask ChatGPT the same question twice, you might get different answers. According to research from Stanford University, GPT-4 can exhibit significant variance in responses even with identical prompts, particularly for open-ended queries about recommendations or comparisons.

This probabilistic nature means you can't just track "your ChatGPT ranking" like you would track your Google ranking. Instead, you're tracking probability—how likely is it that your brand appears in responses about your category?

How AI Models Decide What to Mention

While the exact algorithms are proprietary, research and testing have revealed some patterns:

Recency and Frequency: Brands that are frequently mentioned across the web, particularly in recent content, are more likely to appear in AI responses. A study by Moz found that brand mention frequency across diverse, authoritative sources correlates strongly with AI visibility.

Contextual Association: AI models learn connections between concepts. If your brand is strongly associated with specific problems, features, or use cases across many sources, you're more likely to be mentioned when those contexts arise.

Authority Signals: Content from authoritative domains (educational institutions, major publications, industry-recognized review sites) carries more weight in training data than random blog posts.

Structured Information: AI models are particularly good at extracting and utilizing structured data. Companies with clear, well-organized documentation, feature lists, and comparison information tend to perform better.

Why Companies Are Missing AI Mentions (And Don't Know It)

The scary part about not tracking AI mentions is that you're probably losing potential customers right now and have no idea it's happening.

Consider this scenario: A startup founder opens ChatGPT and asks, "What accounting software should I use for my SaaS startup?" ChatGPT responds with four recommendations. Your product, which is perfect for SaaS startups, isn't on the list. The founder chooses one of the mentioned options and becomes a customer of your competitor.

This interaction happened entirely outside any tracking you have. There's no keyword you could have bid on, no ad you could have shown, no SEO you could have done to capture this customer. They never visited your website, never saw your brand, and never knew you existed.

Now multiply this by thousands of similar queries happening every day. According to Andreessen Horowitz, AI chat interfaces are projected to handle billions of queries monthly by 2025. If you're not visible in these conversations, you're invisible to a massive and growing segment of potential customers.

The Competitive Disadvantage of Not Tracking

Your competitors are tracking this. Maybe not all of them, but the sophisticated ones are. They're using tools like Bear AI to understand their AI visibility, identify gaps, and optimize their content strategy accordingly.

What happens when they're systematically improving their AI presence and you're not? The gap widens. AI models are more likely to mention brands that have strong, clear, frequently-updated information. As your competitors optimize and you don't, their visibility increases while yours stagnates or decreases.

This isn't hypothetical. I've seen companies go from market leaders in traditional search to near-invisible in AI recommendations within a year, simply because they weren't paying attention to this shift.

What to Do Once You Start Tracking

Alright, let's say you've set up tracking—whether through Bear AI or another method. Now what?

Step 1: Establish Your Baseline

Your first month of data is about understanding where you currently stand. Don't panic if the numbers aren't great initially—most companies are shocked to discover they're mentioned less than they expected.

Document:

- Your current mention rate across different query types

- Which competitors are mentioned most frequently

- What contexts you appear in (and don't)

- Platform-specific differences

Step 2: Identify Your Biggest Gaps

Look for patterns in where you're not being mentioned. Common gaps include:

- Specific use cases ("CRM for nonprofits" vs "CRM for sales teams")

- Budget-related queries ("affordable" vs "enterprise")

- Comparison scenarios (appearing in general lists but not head-to-head comparisons)

- Feature-specific questions

These gaps are your optimization opportunities.

Step 3: Implement GEO Strategies

Generative Engine Optimization is how you actually improve your AI visibility. According to research from Princeton's study on GEO, specific optimization techniques can increase brand mention rates by 40-60%.

Key GEO strategies include:

- Creating comprehensive, structured documentation

- Publishing authoritative content on high-trust domains

- Earning mentions in industry comparisons and reviews

- Building clear, scrapeable data about your features and use cases

- Developing use-case-specific content that addresses the queries where you're invisible

Step 4: Monitor Changes and Iterate

AI mention rates don't change overnight. You're playing a long game where improvements compound over time. Track your metrics monthly and look for trends:

- Is your overall mention rate increasing?

- Are specific optimizations working? (Did publishing that comparison guide increase mentions in comparison queries?)

- Are there new gaps appearing as the AI landscape evolves?

Bear AI automatically tracks these trends and shows you which optimization efforts are working, making this iteration process much more manageable.

The Cost-Benefit Analysis of AI Mention Tracking

Let's talk money, because I know this is on your mind. Is tracking AI mentions actually worth the investment?

The Cost Side: If you're using a specialized platform like Bear AI, you're probably looking at a few hundred dollars per month (pricing varies based on tracking volume and features). If you're building your own system, factor in API costs plus significant developer time.

The Benefit Side: This is where it gets interesting. According to Gartner's research, companies that optimize for AI search engines early are seeing customer acquisition cost reductions of 20-30% compared to traditional channels. Why? Because AI-driven discovery is still relatively uncrowded, and being mentioned is often enough to win consideration.

Let me give you a real example. A B2B SaaS company implemented comprehensive AI tracking and optimization. They spent about $500/month on tracking tools and another $3,000/month on content creation for GEO. Within six months, they were attributing 18 new customers per month to AI-driven discovery (tracked through customer surveys and analytics). Their average customer value was $4,800. That's $86,400 in monthly revenue from a $3,500 monthly investment. The ROI was obvious.

Even conservatively, if you're in a market where customers use AI to research solutions (and most markets are heading this direction), every percentage point increase in mention rate translates to real revenue.

Platform-Specific Tracking Considerations

Different AI platforms have different characteristics that affect tracking:

ChatGPT Tracking

ChatGPT is the 800-pound gorilla—it has the most users and the most influence. According to OpenAI's announcements, ChatGPT has over 100 million weekly active users. Tracking here is essential.

Key considerations:

- ChatGPT's responses tend to be longer and include more brands

- It often provides explanatory context about why brands are mentioned

- The platform updates frequently, so tracking needs to be continuous

- Plugin integration means some brands get mentioned through plugins, not just base training

Perplexity Tracking

Perplexity is unique because it's explicitly a search engine, and it cites sources. This means:

- Your visibility depends heavily on being in sources Perplexity trusts

- Getting mentioned requires strong presence in high-authority publications

- You can sometimes see which specific sources led to your mention

- The platform is growing fast with tech-savvy early adopters

Claude Tracking

Anthropic's Claude has a reputation for being more careful and balanced in recommendations. Research shows:

- Claude tends to mention fewer brands per response

- It emphasizes safety and verification, so authority matters more

- It's popular with technical users and developers

- Integration into other platforms (like Slack) means tracking needs to consider these contexts

Emerging Trends in AI Mention Tracking

The field is evolving fast. Here's what's coming:

Real-Time Tracking: Current tracking is mostly batch-based—you test queries periodically. Next-generation tools will offer near-real-time visibility, alerting you within hours if your mention rates drop.

Sentiment Analysis: Beyond just "were we mentioned," advanced tracking will analyze how you were mentioned—positive, neutral, negative, and in what context.

Predictive Analytics: AI models predicting which optimization efforts will have the biggest impact on your mention rates, based on patterns from thousands of other companies.

Multi-Modal Tracking: As AI platforms incorporate images, videos, and other media, tracking will expand beyond text-based mentions to include visual and audio references.

Integration with Traditional SEO: Tools that show you the relationship between your traditional search presence and AI visibility, helping you understand how they influence each other.

Common Mistakes in AI Mention Tracking

Before we wrap up, let me help you avoid some pitfalls I've seen:

Mistake #1: Testing Only Obvious Queries Don't just track "best [your category]" queries. Those are important, but they're a tiny fraction of how people actually discover solutions. Test problem-based queries, use-case-specific questions, and comparison scenarios.

Mistake #2: Ignoring Context Being mentioned in a list of "expensive enterprise tools" when you're actually affordable and targeting SMBs is worse than not being mentioned. Context matters as much as mentions.

Mistake #3: Tracking Only One Platform Your customers use multiple AI platforms. Tracking only ChatGPT leaves you blind to a huge portion of AI-driven discovery.

Mistake #4: Not Connecting Tracking to Action Tracking without optimization is just data hoarding. The point is to identify gaps and fix them. Make sure your tracking setup includes clear insights that drive action.

Mistake #5: Expecting Overnight Results AI models don't update daily based on your latest blog post. GEO is a long game. Track trends over months, not days.

Comparison Table: AI Mention Tracking Approaches

FAQ: Tracking ChatGPT Mentions

How often should I check if ChatGPT is mentioning my company?

You should be tracking AI mentions at least monthly, but ideally weekly for comprehensive coverage. AI models update regularly, and your competitors are constantly optimizing, so mention rates can shift quickly. A platform like Bear AI handles this automatically, testing 1,000+ queries monthly across multiple platforms. Manual checking should be done at minimum once a week if you're not using automated tools, though this will only give you a limited snapshot of your true visibility.

Can I see if ChatGPT mentions my competitors but not me?

Absolutely, and this is one of the most valuable aspects of tracking. By testing queries in your category, you can see exactly which competitors are being mentioned in scenarios where you're absent. This competitive intelligence reveals both where you're losing mindshare and what positioning your competitors have successfully claimed in AI models. Bear AI specifically includes competitive tracking, showing you share of voice compared to alternatives in your space. This data is crucial for identifying optimization opportunities.

Does asking ChatGPT about my company affect future mentions?

No, your individual queries don't affect ChatGPT's training or future responses. AI models like GPT-4 are trained on large datasets and then deployed—your conversations don't change the base model. However, understanding this is important for tracking: it means you can test queries without worrying about skewing results, but it also means you can't "improve" your mentions just by asking about yourself repeatedly. Real optimization requires changing the underlying content and data that training sets draw from.

Why does ChatGPT mention me sometimes but not others?

This is due to the probabilistic nature of AI models and context sensitivity. The same company might appear for "affordable CRM tools" but not "enterprise CRM software," or appear in detailed queries but not brief ones. The model considers many factors: how the query is phrased, what context is provided, what information is most strongly associated with your brand in training data, and even some degree of randomness in generation. This variance is exactly why systematic tracking across many query variations is essential—testing just one or two questions won't give you an accurate picture.

What's the difference between tracking AI mentions and traditional SEO tracking?

Traditional SEO tracking is deterministic and positional—you rank #4 for a specific keyword, and anyone searching that keyword sees you in that position. AI mention tracking is probabilistic and contextual—your "visibility" is actually a percentage likelihood of being mentioned across many possible query variations. You can't just "rank #1 in ChatGPT" because there's no fixed ranking. Instead, you optimize to increase the probability and frequency of mentions across the thousands of ways people might ask about your category. Tools and strategies are fundamentally different, though there's some overlap in content quality principles.

How do I know if AI mentions are actually driving business results?

Track three key indicators: First, add questions to your customer intake or survey process asking how they first heard about you (include "AI chatbot" or "ChatGPT" as options). Research shows that 40-60% of customers who discover products through AI platforms won't show up as direct referral traffic. Second, monitor branded search traffic—AI mentions often lead to Google searches for your brand name. Third, correlate improvements in mention rates with overall lead volume and new customer acquisition. Bear AI helps with this by tracking not just mentions but also the context and prominence, which correlates with conversion likelihood.

What if ChatGPT is mentioning me negatively or incorrectly?

This is a real concern, and tracking helps you catch these issues. If AI models are providing outdated information (old pricing, deprecated features) or mentioning you in negative contexts, you need to address the underlying data sources. This typically means updating public-facing documentation, correcting information on major review sites, and publishing fresh, accurate content that training sets can incorporate. Unlike Google where you can sometimes quickly update rankings, AI model updates are slower, so addressing inaccuracies quickly is important. Bear AI's tracking includes sentiment and context analysis to flag these issues.

Can small companies compete with big brands in AI mentions?

Yes, more than you might expect. AI models don't inherently favor big brands the way traditional advertising does—they favor clear, comprehensive, relevant information. A smaller company with excellent documentation, strong use-case-specific content, and good presence in industry discussions can outperform a large company with poor content. Research from Moz shows that AI mention rates correlate more strongly with content quality and specificity than with brand size. This is actually an opportunity for smaller players to punch above their weight, especially if they track their gaps and optimize systematically.

How long does it take to improve my AI mention rate?

Expect 3-6 months to see significant improvements from GEO efforts. Unlike paid advertising where you can see results immediately, or even SEO where you might see movement in weeks, AI model training cycles are longer. Content you publish today might not be incorporated into model knowledge for months. However, the improvements tend to be sticky—once you've established strong AI visibility, it's relatively stable compared to the constant competition of paid search. Companies using Bear AI to track and optimize typically see initial improvements around month 3, with accelerating results by month 6 as multiple optimization efforts compound.

Do I need different strategies for different AI platforms?

Yes, but with significant overlap. The core principle—create clear, authoritative, comprehensive content—applies across all platforms. However, there are nuances: Perplexity favors brands with strong presence in sources it cites (major publications, authoritative reviews), Claude tends to be more conservative with mentions, and ChatGPT's longer responses often include more brands. Your tracking should break down performance by platform so you can identify where specific optimizations are needed. Bear AI tracks all major platforms separately, letting you see these differences and adjust strategy accordingly.

Conclusion: Taking Control of Your AI Visibility

Look, the reality is simple: AI-driven discovery is here, it's growing fast, and if you're not tracking whether ChatGPT mentions your company, you're flying blind in an increasingly important channel.

The good news? You're still early. Most companies haven't figured this out yet. AI search optimization is where traditional SEO was in 2004—a massive opportunity for those who move quickly, before it becomes crowded and hyper-competitive.

Your Action Plan

Here's what you should do right now:

This Week: 1. Sign up for Bear AI to start tracking your AI mentions across ChatGPT, Claude, and Perplexity

2. Manually test 5-10 key queries in ChatGPT to get an initial sense of your visibility

3. Document your top 3-5 competitors to include in tracking

This Month:

1. Review your first full tracking report to establish baseline metrics

2. Identify your three biggest visibility gaps (query types where you're rarely mentioned)

3. Create a content plan addressing these gaps with GEO-optimized content

This Quarter:

1. Implement comprehensive GEO strategies across your documentation and content

2. Track monthly changes in mention rates and adjust optimization priorities

3. Tie AI mention improvements to actual business metrics through customer surveys

The companies that will win in the AI search era are those that start tracking and optimizing now, while this channel is still maturing. Every month you wait is a month of potential customers discovering your competitors instead of you.

Is it possible to track if ChatGPT is mentioning your company? Yes—and it's not just possible, it's essential. The question isn't whether you can track it, but whether you can afford not to.

Start tracking today with Bear AI, the YC-backed platform built specifically for AI search optimization, and stop guessing about your visibility in the future of search.