How to track LLM visibility?
LLM Visibility Tracking is the practice of monitoring when and how often your brand is mentioned in AI-generated answers across platforms like ChatGPT, Perplexity, Google Gemini, Claude, and Mistral. In today’s search environment, visibility in AI answers is no longer a nice-to-have, it directly impacts awareness, trust, and revenue. If an AI assistant recommends your product or service, it works like a personal referral at massive scale. If it doesn’t, your competitors get the advantage. The only way to track this reliably is with modern software purpose-built for AI Search, platforms like Superlines that not only show you where you stand but also pinpoint the actions that will improve your presence.
For marketers and agencies, tracking LLM visibility is becoming as fundamental as checking keyword rankings once was. The difference is that this channel is new, fast-moving, and still wide open for early movers to gain an edge.
In this article (updated August 2025), we’ll cover:
- What LLM Visibility Tracking actually is and why it matters
- The business impact of being recommended (or ignored) by AI systems
- Real-world examples of how visibility drives growth
- A practical framework for tracking and improving your AI search presence
AI-powered search and large language model (LLM) chatbots, think ChatGPT, Google’s Gemini, Perplexity, Claude, and Mistral are changing how people discover information and recommendations. Instead of ten blue links, users now get conversational answers. And those answers often name-drop products and businesses. In fact, a recent study found 62% of users now turn to AI chat platforms like ChatGPT or Google’s Gemini to search for products or services. That’s a sea change in consumer behavior, and it opens up a whole new marketing channel for businesses. We can also see the change in this graph from Q1 of 2025 where organic searches are down 20-40% YOY on many bigger platforms.
Why should marketers care? Because visibility in LLM chats can be a real revenue booster. Think about it: if an AI assistant recommends your business as the solution to someone’s query, that’s as good as a personal referral. Early data shows these AI-driven recommendations pack a punch, products and services recommended by ChatGPT are more likely to be chosen by consumers, and even lesser-known brands see a trust boost after an AI endorsement. In one Adobe survey, 15% of shoppers using generative AI said they specifically ask AI chatbots for brand recommendations when shopping online. In other words, people are not just okay with AI suggestions; they’re actively seeking them out and acting on them.
For businesses, this means that getting noticed by AI is quickly becoming as important as traditional SEO. If your brand isn’t showing up in those AI-generated answers, you’re essentially invisible to a growing segment of your audience. It’s like being missing from Google search results, but in the AI realm. This is why the concept of LLM visibility tracking has entered the chat (pun intended). Just as companies track their Google rankings, leading marketers are now figuring out how to track and improve their presence in AI chatbot recommendations. It’s the next frontier of search visibility, and those who invest and put effort into it early stand to gain a serious edge (and possibly a few extra high-fives from the sales team).
Before you panic about another thing to monitor, let’s break down what LLM visibility tracking actually means, why it matters, and how you can leverage it to boost your brand. Grab a coffee (or your favorite robot assistant), and let’s dive in.
"Many don't even realize yet, how fast consumer behavior is changing right in front of our eyes and it will surprise a lot of companies by the start of 2026 if they haven't started to act on it." – Superlines' CEO and Co-founder Jere Meriluoto
What is LLM Visibility Tracking?
Let’s start with the basics: LLM visibility refers to how prominently your business or product is mentioned or recommended by AI language models in their answers to users. When someone asks an AI chatbot a question like, “What’s the best project management software?” or “Where should I buy pet food online?”, the AI will generate a response pulling from its trained knowledge and any integrated search data. If your brand gets a shout-out in those responses, congrats, you just earned a bit of AI-driven publicity. If not, you’ve missed an opportunity (and your competitor might be getting the spotlight instead).
LLM visibility tracking is the practice of systematically monitoring and analyzing these AI-generated recommendations. It’s about understanding when, where, and how often chatbots mention your brand (or don’t mention it). This is comparable to tracking your keyword rankings on Google, but instead you’re tracking your “mention rank” in AI outputs, also called tracking your brands share of voice in these conversations. As Superlines Co-Founder and CTO Kimmo Ihanus notes, “product recommendations aren’t just coming from review sites or social proof anymore. A whole new channel is emerging and adding its flavor to the mix: how ChatGPT compares and recommends brands when potential customers ask, ‘Should I buy X or Y?’”. In other words, AI chatbots have become digital taste-makers and personal sales assistants who need to have your brand's brochures available to be handed out!
Why do we need to track this? For one, AI chat responses are dynamic and personalized. They might mention different brands or cite different sites to different users or change over time as the models learn or update. Unlike a static search results page, an AI’s answer can vary by the phrasing of the question or context. This makes it tricky, you can’t just do one search and call it a day. You need a systematic way to check if your brand appears in various relevant AI queries and how you stack up against competitors in those answers.
For example, imagine you run a boutique hotel. If someone asks an AI, “What are the best hotels in Helsinki?”, will your hotel be in the AI’s answer? LLM visibility tracking would tell you that. It’s essentially AI-era brand positioning. High LLM visibility means the AI often brings up your name (yay!), whereas low visibility means you’re rarely, if ever, mentioned (the AI equivalent of crickets chirping).
Crucially, LLM visibility tracking isn’t just about vanity (“oh look, ChatGPT knows my brand!”). It’s a feedback loop to improve your marketing. If you find out that ChatGPT never mentions you when asked about your product category, that’s a signal to strengthen your content and SEO (or what the market is now calling Generative Engine Optimization (GEO) for AI) so that the AI has reason to include you. On the flip side, if you discover the AI is mentioning a competitor more often, you’ve identified a competitive gap. In short, tracking AI visibility helps you identify where to optimize your online presence to become the chosen answer. It’s the first step in making LLMs work for you as a marketing channel.
The State of LLM Visibility through our eyes
To understand how important LLM visibility is, let’s look at what’s happening in the industry. Superlines, a company specializing in AI search visibility, recently researched how often AI chatbots recommend businesses across different industries in Finland. The findings were eye-opening: AI chatbots recommended a business in roughly 30–50% of user queries in many consumer-focused industries, yet about 50% of companies received zero recommendations in these AI chats. In plain English, half of the businesses studied were never mentioned at all by the likes of ChatGPT or other bots. Half! That’s a huge chunk of companies effectively invisible in the AI-driven search landscape. This stark divide suggests a classic case of early movers vs. laggards, some industries are riding the AI wave, while others are missing it entirely.
So, who’s getting those AI shout-outs? The Superlines research (translated from Finnish) highlighted top-performing industries like recruitment, retail, travel, and pet products. It seems chatbots are quite willing to play matchmaker in these areas, whether it’s recommending the latest retail brand, a pet supply shop, or a travel service, the AI is frequently dropping names in these sectors. It makes sense: consumers often ask AI assistants for help with shopping ideas, trip plans, or pet advice, so businesses in those niches have more opportunities to be recommended.
How do these local findings compare globally? Early indicators suggest this isn’t just a Finnish quirk. Globally, we’re seeing similar trends. AI-driven search is already directing a significant flow of traffic and customer attention. ChatGPT’s referrals to websites grew by 60% between June and October 2024, rising from under 10,000 domains to more than 30,000 unique domains receiving traffic daily by late 2024. Now in 2025 ChatGPT alone processes 1+ billion queries daily, averaging 190.6 million daily users, which is 2,206 visits every second. If AI systems don’t mention your brand, you are already losing market share to your competitors.In other words, AI chat isn’t just answering questions in a vacuum, it’s actively sending people to websites (hopefully yours). And those visitors tend to be high-intent: our observations show that traffic from AI chatbots spends longer on sites and views more pages than typical Google search users. The latest August 2025 data confirms this, showing that AI chat traffic converts 4.4x better than traditional search engine traffic. See the Semrush research below for details.
Consumer behavior research echoes the power of being “the chosen one” by an AI. One academic study examined how ChatGPT’s product recommendations affect shoppers. The result? ChatGPT can indeed sway consumer choices – products it recommended were more likely to end up in users’ consideration sets and purchases. Notably, the study found that products with low brand awareness actually gained trust after an AI recommended them. That means an unknown company can suddenly become a frontrunner in a buyer’s mind simply because an AI said, “Hey, check this one out.” Talk about leveling the playing field!
We’re also awaiting more data on how big brands are faring in this new arena. There’s buzz about a forthcoming Fortune 500 LLM visibility report by Superlines, which should shed light on how the world’s largest companies are showing up (or not) in AI-driven recommendations. While we wait on that, it’s a safe bet that even among global giants, we’ll see a split between early adopters and those late to the game. In all likelihood, some Fortune 500 brands are already optimizing to be chatbot-friendly (and reaping the rewards in customer engagement), while others might be as invisible as our missed Finnish B2B firms in this context. The key insight for now: whether you’re a nimble startup or an enterprise behemoth, AI search visibility is up for grabs and many aren’t grabbing it yet.
Case Studies: How Companies Are Growing via LLM Chats
Alright, enough theory, let’s talk real-world examples. How are companies actually benefiting from AI chat visibility today? Here are a couple of illustrative stories and lessons:
Case Study 1: HubSpot vs. Salesforce – The CRM Showdown
Even the biggest names are paying attention to LLM visibility. Dharmesh Shah, co-founder of HubSpot, recently ran an experiment comparing how ChatGPT recommends HubSpot versus its rival, Salesforce. Using an AI agent to probe ChatGPT’s answers, he discovered some fascinating insights. ChatGPT had a nuanced take on each brand: it highlighted HubSpot’s strengths (an all-in-one platform, user-friendly approach, leadership in inbound marketing) and noted Salesforce’s advantages (high customization, advanced features for enterprises, etc.). More importantly, it revealed specific scenarios for recommending each. For a small business or marketing manager wanting simplicity and strong inbound tools, ChatGPT tended to recommend HubSpot. But for an enterprise user needing heavy customization and integration, it would lean towards Salesforce. In essence, the AI drew clear “buyer personas” for each product and decided which use-case favored which company.
This is gold for marketers. It’s like peeking into the “mind” of the AI to see how it perceives your brand versus the competition. HubSpot learned not only what positive points the AI sees (great for validating their messaging), but also where the AI might be underestimating them (interestingly, the bot thought HubSpot had fewer integrations than it actually does – a sign that HubSpot could do more PR/SEO around its integration capabilities). The outcome: by tracking LLM visibility and the context of recommendations, HubSpot can double down on areas where ChatGPT already praises them, and take action to correct or improve areas where the AI’s info (and thus possibly the public’s perception) is lacking. The broader lesson for all companies: see what the AI is saying about you. You might find out you’re the go-to suggestion for Scenario A, but missing out in Scenario B. Then you can strategize accordingly.
Case Study 2: The Up-and-Coming Online Retailer
Consider a smaller example: a niche online pet supplies retailer (let’s call them “Happy Paws”). They don’t have the name recognition of Chewy or Petco, but they’ve invested in great content about pet care across their blog and got some positive chatter on forums and local news. When AI chatbots like Perplexity or Bing (with its AI chat mode) field questions like “Where can I buy healthy organic dog treats?” or “best pet supply store online”, something interesting happens, Happy Paws often gets mentioned as a recommended option, even alongside bigger brands. Surprised? The owners certainly were when they started tracking AI queries. It turns out their content and backlinks from authoritative pet blogs made them visible enough that AI models “know” about them as a quality source. Over a few months, they notice a bump in referral traffic and even some customer comments like “I found you through ChatGPT.” That’s the kind of anecdotal win that signals a new growth channel.
By systematically tracking how often they come up in pet-related AI queries, Happy Paws was able to attribute part of their sales growth to AI recommendations, something that wouldn’t show up in Google Analytics by default. They also identified which queries they weren’t showing up for (e.g., “best cat toys 2025” didn’t feature them, alas). This guided their content team to create new pages on cat toys and get some expert articles published, to give the AI more to chew on. Within another quarter, they started appearing in those conversations too. The takeaway here: even a small business can punch above its weight if it earns the AI’s trust. And you won’t know you’ve done that unless you track when and where the AI is mentioning you.
Case Study 3: Travel and Hospitality Boosters
The travel industry is also seeing AI-driven wins. Let’s say a boutique travel agency noticed something curious, a prospective customer said, “I was chatting with Bing AI and it suggested I check out your tours.” This prompts the agency to investigate their LLM visibility. Sure enough, when they ask ChatGPT and Perplexity for “unique adventure travel companies” or “offbeat tours in Europe,” their agency often comes up as a recommendation. It turns out their historically strong SEO presence (they had lots of high-quality travel articles and guides) made them a known entity to the AI, so it confidently suggests them to users looking for adventure travel. By recognizing this, the agency doubled down: they added more AI-friendly content (like Q&A about their tours) and even adjusted their website schema markup to better highlight key info, making it easier for AI models to ingest details about their offerings. Over time, not only did they see more AI referrals, but they started getting questions from customers that clearly originated from AI chats (sometimes the wording gave it away, e.g. “Your AI travel buddy mentioned this tour, can I know more?”). This feedback loop of track → learn → optimize → track again helped them grow a slice of bookings without any extra ad spend. Essentially, AI chat became a low-cost customer acquisition channel for them.
These case studies (some real, some composite but based on real trends) underline a few points. First, LLM visibility isn’t science fiction, it’s happening now, and savvy companies are capitalizing on it. Second, success leaves clues: businesses that benefit are the ones actively checking how they appear in AI-driven conversations and then tweaking their marketing to boost those mentions. Third, even if you’re not a tech giant, you can get on the AI recommendation list by being relevant, authoritative, and AI-friendly (more on what that means next). It’s a bit like being a recommended “staff pick” in a store, any brand can get that tag if they meet the right criteria, but you have to know what the AI “staff” looks for. So let’s shift from why this matters to how you can actually improve your AI visibility.
How to Improve LLM Visibility & Track Results
By now, you’re probably thinking, “Okay, I’m sold, I need my brand to show up in these AI chats. How do we do that?” Improving your LLM visibility boils down to a mix of optimization tactics (making your content and presence AI-friendly) and smart tracking (so you know what’s working). Consider this your LLM visibility game plan:
1. Audit Your Current AI Search Presence
Start by understanding your current visibility situation today. You can’t improve what you don’t measure, right?
•Manually check AI recommendations: Go ahead and ask ChatGPT (or Claude, Perplexity, etc.) some questions relevant to your industry. Does your brand come up? For example, if you sell running shoes, ask “What are the best running shoe brands?” in a few different AI platforms. This is a quick and dirty test.
•Research competitor mentions: Notice which competitors get mentioned by the AI when you don’t. You might discover that the chatbot loves citing a competitor’s blog or their product features that you haven’t highlighted about yourself. That’s intel you can use.
•Use a tracking tool: Given the rise of this need, purpose-built solutions like Superlines are emerging specifically to track and improve AI search visibility. Superlines does exactly that. It monitors in real time where your brand appears and in what context across all major AI platforms (ChatGPT, Gemini, Perplexity, Claude, Mistral). Every day, it systematically scans high volumes of relevant conversations your target audience is having and shows whether your brand is included. You get up-to-date data on your share of voice, the exact queries driving visibility, and clear next steps to improve results faster. Think of it like SEO workflows: you spot the top-performing content, identify gaps, and optimize. The difference is that in AI Search, Superlines not only uncovers the gaps but also pinpoints the actions to take, making your team’s work faster and more effective.
2. How to Optimize Your Content for AI Readability and Context
LLMs don’t index pages like Google’s crawler, but they do ingest a lot of content (during training or via web browsing capabilities that almost all the models do now in H2 of 2025) and form an understanding from it. To make your brand “AI-friendly”:
•Use clear, structured content on your site: Organized, well-written content is more likely to be correctly interpreted by AI. Use descriptive headings, bullet points, and concise explanations of your products/services. Think in terms of Q&A style content where appropriate (since people often ask AI questions). If you have an FAQ page that directly answers common questions about your industry, that’s gold. An AI that has seen those will be more confident mentioning you as the source for an answer. One highly clever approach is to repurpose insights from your customer support tickets, both solved and unsolved. By turning these real-world questions into a structured knowledge base, you create a go-to source for LLMs to draw from. This is, in my view, one of the most practical growth hacks for AI Search.
•Incorporate relevant keywords and context: While old-school SEO was all about exact keywords, AI is about context. Make sure your content addresses the topics and themes relevant to your business comprehensively. For example, a finance startup might publish a blog like “How to choose the right personal finance app”, not a sales pitch, but a genuinely useful article that an AI might draw on when a user asks, “How do I manage my budget?”. Within that, of course, it can mention your app as an option. The key is to answer the questions your target audience is likely to ask the AI. If the AI has seen your content answering the question, it may echo your answer (with your brand included).
•Refresh and update online information: Ensure that places like your Wikipedia page (if you have one), Google Business profile, or any public data sources have up-to-date, accurate info. LLMs like to rely on widely published data for factual questions (“Who is the CEO of X?” or “Where is Y based?”). Correcting misinformation and seeding good information helps the AI portray your brand correctly and confidently.
3. How to use Schema Markup and Structured Data?
This is the slightly nerdier part, but it gives you a big advantage. By adding structured data (like Schema.org markup) to your website, you make certain facts explicitly clear to machines. This can influence AI outputs just as it influences search engines.
•Add schema for products, reviews, etc.: If you have products, use Product schema; if you have many reviews or ratings, use Review schema. There’s even FAQ schema for pages that are Q&A format. This structured info can be ingested by search engine AIs or any LLMs that crawl the web, giving them a well-organized knowledge base about your brand. For example, schema can highlight that “Acme Electronics” has 4.5 stars from 500 reviews, or that you’re located in Helsinki and specialize in consumer robotics. Don’t be surprised if an AI, when asked about “best consumer robotics in Helsinki,” ends up citing those very facts in an answer.
•Use metadata wisely: Ensure your pages have accurate meta titles and descriptions, as these often end up as summary text. Also, using Open Graph tags (for social media previews) and other meta tags can’t hurt – they all contribute to machine-readable context. While an AI might not literally recite your meta description, the information there (concise summary of what you do) can feed its understanding of your business.
4. How to Create AI-Referencable Content and Get Cited?
AI models love authoritative sources. They were trained on huge swathes of the internet, and they tend to trust and pull from content that appears authoritative and well-cited by others. So, become one of those sources:
•Publish high-quality research or insights: Do you have unique data or expertise? Publish it. This could be industry research, whitepapers, case studies, or even well-researched blog posts. If your content gets referenced by news sites, other reputable players in your industry, Wikipedia, or forums, it increases the chance an AI will “know” about it. Almost all newer models ChatGPT conduct live web searches and will see those citations if made accessible. If, say, your study on “AI adoption in Nordic marketing” gets quoted in a popular article, don’t be surprised if an AI cites that finding (and credits your brand) when asked about AI adoption stats.
•Aim for Wikipedia and other knowledge bases: This is a bit of a long game, but getting your company included in Wikipedia (in a factual, noteworthy way) or mentioned on other well-regarded sites can massively boost your AI visibility. LLMs trained on data up to 2021 (for example) definitely have Wikipedia in their knowledge. So, if your brand is there with a solid article, the AI has a canonical source to draw info from. Just ensure the content is factual and not promotional (Wikipedia’s guidelines!), the payoff is that AI will consider you “notable” enough to mention.
•Be active where AI sources its info: Participate in Q&A platforms like Quora or industry forums, especially where answers often bubble up on Google. Often, those answers become part of the training data or are scraped by search AIs. If you consistently provide quality answers (and subtly mention your brand where relevant), you’re planting seeds for AI models to pick up later. It’s like laying traps for the AI to “accidentally” recommend you. IIf you want to learn more about where AI actually gets its information, we recommend reading this article here.
5. How to Reinforce Your Brand in AI Conversations?
This one is more about marketing tactics to influence AI indirectly: essentially, get more people talking about and asking about your brand in ways that AI will notice.
•Encourage users to mention you to AI: This might sound quirky, but imagine a campaign where you invite your audience to ask ChatGPT about your product category and see what it says. Their queries and interactions (especially on public forums or social media) create additional data points about your brand in relation to certain questions. Plus, if the AI gives a subpar or incorrect answer, you’ll hear about it and can address it (e.g., publish content to fix that, or even use feedback tools if the AI platform offers any).
•Use social proof and PR (AI reads the news too): Today’s AI models are increasingly being updated with current information. If your brand is being talked about in the news, press releases, or social media, that information can seep into AI models via updates or connected tools. A strong PR strategy, getting your new product launch featured on tech blogs, or your CEO quoted in an expert roundup, means when someone asks the AI “What’s the newest innovation in X?”, it might recall that article and mention your brand. Essentially, stay relevant in the human world so that the AI world takes note. It’s a virtuous circle: the more humans talk about you, the more AIs will learn about you; the more AIs mention you, the more humans discover you.
Tracking the Results: All the above efforts need to be tracked to see if they’re paying off. This is where an AI visibility tracking tool again is handy, for example, Superlines doesn’t just track if you’re mentioned, but can show trends over time (did your mention share increase after that PR campaign or after adding schema markup?). It also compares with competitors, so you know if you’re catching up or if they’re still ahead. The platform essentially builds a self-expanding dataset of AI search results across many queries, industries, and timeframes. Why is that important? Because the more data it gathers (from all its users and queries), the smarter its recommendations can get. It might discover, say, that “brands that consistently get mentioned in AI answers about travel all have a strong presence on TripAdvisor and Wikipedia” – insight which it can pass to you so you know where to focus. This kind of cross-industry learning gives those using the tool a leg up. It’s like having a hive mind of AI search knowledge working on your behalf.
To sum up this game plan: optimize your digital presence for AI, and then continuously monitor. Improving LLM visibility isn’t a one-and-done project but an ongoing part of your marketing strategy. Fortunately, many of these steps (like creating quality content or adding schema) help your regular SEO too, so you get dual benefits. And as you implement changes, keep checking your AI mentions. Over time, you should see your brand move from “never heard of ’em (according to the bot)” to “frequently recommended”. That’s when you know you’ve become part of the AI conversation.
We believe AI Search visibility will become the most important metric for companies to track and improve. -Jere Meriluoto, CEO & Co-Founder of Superlines
How to Engage into Generative Engine Optimization?
AI search is facing explosive growth and hitting the warp speed 10 faster than anyone would even think that it. would. AI-driven search and LLM recommendations are not a passing fad, they’re the future of how people will find solutions, products, and brands and silicon valley tech companies like Zapier and Supabase are all investing into this marketing channel to gain growth. Just like they have done with SEO. For marketers, this opens a new front in the battle for customer attention. It’s one that’s still wide open. Unlike traditional SEO, where everyone and their grandma has now piled in, the AI recommendation space is relatively untapped, which means acting now (like the cutting edge companies mentioned) can give you an outsized advantage.
Here are some closing thoughts to wrap this up (consider these your friendly nudges to take action):
•If your brand isn’t being talked up by AI, it might as well be invisible. Harsh but true. As we discussed, a significant chunk of companies (around half, even in tech-savvy markets) currently get zero love from AI recommendations. You don’t want to be in that half. The upside is that if you start optimizing for LLM visibility, you can leapfrog competitors who remain oblivious. It’s like showing up to a party that your rivals didn’t know about yet, you get all the facetime with the VIP (in this case, the AI) while they’re stuck in traffic.
•LLM visibility tracking will define the future of digital marketing metrics. Remember when social media likes and followers weren’t a thing, and then they became key metrics? We’re at a similar inflection point. In a year or two, marketing teams will likely have an “AI visibility” KPI on their dashboards, right next to SEO rankings and web traffic. Getting ahead of that curve means you’ll already have the data and experience to know what moves the needle. When your CMO asks, “How are we doing in AI search?” you’ll be the one with answers (and hopefully, some neat Brand share of voice growth trajectory charts to show off).
•Don’t wait for the AI rocket ship to leave the station. It’s tempting to watch and wait to see if AI search really takes off or if maybe it “breaks” in some way. But all signs point to it accelerating. Big players like Google have already rolled out AI Overview in over 180 countries worldwide and the usage adoption of ChatGPT, Perplexity and other AI Search platforms are in explosive growth with over 960% YoY growth. The train is very much boarding now. Early adopters who optimize and track will have secured their seats. Latecomers might find it hard to catch up, especially if their competitors become the de facto AI-recommended names in the meantime.
•Keep a light touch of humor and humanity in your approach. (Yes, even in cutting-edge tech like this!) One could joke that we’re all now trying to please our new robot overlords to win customers. In reality, though, the goal is still the same as ever: provide value, be the best answer for the customer’s question, and make sure you’re noticed. LLMs are just a new intermediary. So while you optimize for AI, remember you’re ultimately influencing real people’s decisions. Stay authentic, keep your brand voice, and maybe, just maybe, have a little fun with it while keeping your authority.
In closing, LLM visibility tracking is poised to become a cornerstone of marketing strategy. It combines the technical aspects of SEO, the strategic thinking of brand positioning, and the agility of modern digital marketing. The companies that master it now will be the ones leading their industries in a few years, while others scramble to catch up. So, go ahead: take the next steps. Audit your AI presence, tighten up your content, make use of tools like Superlines to track and improve your AI visibility and integrate AI visibility into your marketing playbook. The AI search era has arrived, make sure your brand isn’t just along for the ride, but in the driver’s seat of this explosive new journey towards customer discovery and revenue growth.
Related Reading:
• Superlines study: What can 1.5 million AI search citations teach marketers
• Best Practices for Optimizing Content for Generative AI
• How to Optimize for Generative AI
• Latest Trends and News on AI Search - August 2025
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P.S. If you just want to exchange thoughts on this topic, feel free to connect with me on LinkedIn. Always happy to chat!
-Jere Meriluoto CEO, Co-Founder of Superlines