AI brand visibility: optimise your brand for LLMs and AI search
Updated August 8, 2025.
Adapt your brand strategy to an AI-first approach for enhanced brand awareness and track your brand's visibility in AI search results.
The digital customer journey is changing fast. One prompt in an AI assistant like ChatGPT or one AI-generated snippet in Google’s AI Overviews can collapse the entire buyer journey into a single touchpoint.
According to this Wall Street Journal 2025 article, 80% of consumers now resolve 40% of their online queries without clicking any links, thanks to AI-generated summaries and LLMs.
According to another recent study, there’s a staggering 81.10% chance that at least one URL from Google’s top 10 SERP results will be cited in an AI Overview.
SearchEngineLand reports that, according to a 2025 survey, 68% of organisations are changing their search strategies right now in response to AI search.
In the age of large language models (LLMs), brands must understand:
How LLMs process and present brand information: Read my previous post on Generative AI and LLMs here: how do they work, what limitations do they have, and how are they trained.
How to improve and track brand visibility inside AI-driven environments. Let’s dive deeper into AI-first brand awareness in this blog post.
Why LLMs matter to growth marketers?
We’re now entering the “agentic internet.” This is a fundamental shift from the classic “type a keyword into Google” model to something radically different. Customers are increasingly outsourcing the entire discovery journey to AI assistants.
Whether it’s through ChatGPT, Perplexity, or Google’s AI Overviews, people are increasingly asking AI agents to research solutions, compare products, make recommendations, and even decide which brand to trust—all within a single chat.
In June and July 2025, Google rolled out a Core Update that introduced AI Overviews across most organic search results. These updates were designed to enhance Google’s ability to surface relevant, high-quality content in search results. These latest updates have had an impact on organic traffic and search clicks levels.
For example, according to SEO tool Mangools, significant changes in Google SERP positions caused by Google algorithm updates (see image below).
This has big consequences for digital marketing. Early data shows:
Declines in organic search traffic for many industries
Lower clickthrough rates on search result pages
Fewer customer visits to websites during the decision process
LLMs like ChatGPT, Claude, Perplexity, and Gemini are becoming trusted digital advisors.
This means that LLMs / AI search can influence:
Which brands does a user become aware of
Which brands are recommended during consideration
Which brand is chosen for the final decision
3 other key reasons to track your brand visibility in AI tools:
Brand reputation management and risk mitigation: AI-generated misinformation, fake citations, or brand impersonation can harm trust. Monitoring your brand visibility in AI allows you to address threats early.
Customer insights: how are users enquiring about your brand; valuable for improving messaging
Competitive analysis: How AIs discuss your competition; opportunity to optimise your brand positioning or messaging
LLM brand visibility: how LLMs "know" your brand
Here’s how LLMs gather and synthesise brand information:
1) Pretraining data:
Most LLMs are trained on a large swath of the public internet, including websites, forums, reviews, press releases, Wikipedia, and news articles.
For instance, for ChatGPT, OpenAI periodically retrains models on newly collected data via supervised learning and reinforcement learning from human feedback.
In the meantime, the models can pull up-to-date info via web search (when available), but that is separate from their core training (see next chapter/RAG).
Key takeaway:
If your brand has a strong, widely available online presence, it’s more likely to be “known” to these models.
2) Retrieval-augmented generation (RAG) & live web search:
AI tools are no longer limited to static training data; they can now incorporate real-time information from the web (or other sources, such as internal data) using techniques like retrieval-augmented generation (RAG) or integrated web browsing capabilities.
Tools like Perplexity, Bing Copilot, and some versions of ChatGPT/Gemini use live web search to enhance answers.
This is what ChatGPT-4 itself tells when asked how it chooses the web sources it uses:
“When I use the search tool, here's how I choose and evaluate the sources I rely on:
🔍 1. Search engine and relevance ranking
I start by sending your query to a search engine (currently using Bing).
It returns a list of the most relevant and recent results based on its internal ranking (similar to what you'd see if you Googled or Binge-searched something).
✅ 2. Criteria for choosing sources
I scan the top results and prioritize sources based on these factors:
Criterion & What I look for:
Credibility: Trusted sites like
.edu,.gov, major news outlets, scientific journalsAuthoritativeness: Subject-matter experts, known institutions (e.g., Mayo Clinic, MIT, etc.)
Clarity: Well-written, direct explanations over vague or clickbaity ones
Freshness: Especially important for news, events, or software documentation
Transparency: Sources that cite their own references or offer verifiable claims
⚠️ 3. What I avoid
I deprioritise or ignore:
Clickbait or SEO-stuffed articles
Unverified personal blogs or forums (unless the context is niche or firsthand experiences are valuable)
Misinformation-prone sources (I cross-check claims if something looks questionable)
🤖 4. How I summarise it for you
Once I open a promising page, I:
Read the relevant section (not the whole site).
Extract just the part that answers your question.
Tell you where it came from (domain, article title, etc.) and, when useful, include a link so you can verify or explore further.
5. Update August 8, 2025, after OpenAI launched GPT-5:
OpenAI just launched a new model, GPT-5. According to OpenAI, GPT‑5 is a unified system with a smart, efficient model that answers most questions, a deeper reasoning model (GPT‑5 thinking) for harder problems, and a real‑time router that quickly decides which to use based on conversation type, complexity, tool needs, and your explicit intent (for example, if you say “think hard about this” in the prompt). The router is continuously trained on real signals, including when users switch models, preference rates for responses, and measured correctness, improving over time.
So let’s test how GPT-5 finds and processes brand information, and how it is different from GPT-4:
“Hi GPT-5, how do you pull brand information for a user query, for instance, if a user is looking for a Growth Marketing agency? Which sources do you use, and how do you create recommendations for this query?”
GPT-5: Alright — let’s pull back the curtain on how I’d handle that kind of request.
If you ask me, for example,
“I’m looking for a growth marketing company — can you recommend some?” My process is usually a mix of source gathering, analysis, and recommendation logic:
1. Understanding the query
First, I parse your request into intent + parameters:
Intent → Find brands/companies in the growth marketing space.
Parameters → Could be location, budget, industry, stage of business, or style (e.g., data-driven, creative-first).
If you don’t specify them, I’ll either assume a broad/global search or ask you to narrow down.
2. Pulling brand information
If you want up-to-date results, I switch into real-time mode and use my web search tool to pull information.
Typical data sources I’ll check:
Company websites → For their services, case studies, industries served.
Business directories → e.g., Clutch, UpCity, Sortlist, G2.
LinkedIn → To verify size, specialties, and client focus.
Press/news → Funding announcements, awards, client wins.
Review platforms → Client ratings, testimonials, and average pricing.
I’ll cross-reference multiple sources to ensure accuracy — for example, if a directory lists “50–200 employees” but LinkedIn shows 15 employees, I’ll flag that.
3. Evaluating & Ranking
I don’t just dump links — I score or rank brands based on:
Reputation (reviews, awards, case study quality)
Expertise fit (e.g., if you want SaaS growth, I’ll look for SaaS-specialised agencies)
Scale (startup-friendly vs. enterprise-focused)
Location relevance (if you want someone local or with time zone alignment)
Differentiators (tech stack, creative approach, niche industry experience)
4. Creating Recommendations
The final output is usually:
Shortlist of companies (3–7)
Quick comparison table (services, size, pricing range, notable clients)
Pros & cons for each
Suggested “best fit” based on your criteria
Key takeaway:
Brands can appear in the search-based AI answers if
they match the intent of the user query: they answer directly to user’s questions.
With AI search and chat, people search specifically, not broadly:
Users ask detailed long-tail queries in the chat. For these bottom-of-funnel queries, AI recommends products and services — but it does so with deep context about the user’s needs and pain points.
To get recommended, your brand needs to be clearly connected to those pain points.
they are trusted sources such as .com sites
they rank well
they have a good brand reputation
they have relevant, trusted and fresh information
they are thought leaders / experts in their field
they have content clarity and schema markup (to make it easy for AI to find the information on the website)
GPT-5 model also cross-references information across multiple channels meaning that Brand consistency across all platforms is highly important.
3) Plugin & custom GPT ecosystems:
Some brands can build their own AI plugins or custom GPTs, creating direct brand experiences inside LLM platforms.
4) APIs and integrations:
Some AI tools pull data from brand APIs (e.g., Expedia in ChatGPT) to give real-time branded responses.
How to increase brand visibility inside LLMs
If marketers want their brands to show up in LLMs, they need to stop thinking like SEO experts and start thinking like AI strategists. LLMs are optimising for relevance, not clicks.
Think like an AI-First marketer:
If someone were to ask ChatGPT a question in your niche, how likely is it to recommend you—and what would it say?
How LLMs interpret information
LLMs generate responses based on patterns of language and associations between concepts. When LLMs are answering a prompt like:
“What are the best running shoes for marathon training?”
They’re not looking for brand slogans (“Just do it”); they look for topical relevance, consistency across multiple sources, and authority signals (e.g., linked studies, structured data, use case fit).
To be visible in AI-powered conversations, your brand messaging must clearly answer:
Who is this for
What problem or real user need does it solve? Answer the “job to be done”.
Why is it the right fit in this situation?
What evidence supports this claim? References of expertise and authority (e.g., studies, reviews, testimonials).
The more clearly you tie your product or service to specific needs of your target buyer, the more likely LLMs are to surface your brand in relevant chats.
Example: SaaS / B2B Software:
❌ “We provide intuitive project management tools.”
✅ “Our platform helps distributed teams reduce project delays by 27% through real-time task tracking, AI-powered prioritisation, and integration with tools like Slack, Jira, and Google Drive.”
This means that ICP research and understanding deeply your key prospects and customers is even more important than ever. Know your buyers, their needs, challenges, pain points, emotional drivers, buying motivations, buying objections, decision criteria.
How to track your brand visibility in LLMs?
Current challenges:
There are not yet widely used AI brand visibility tools showing how often LLMs mention your brand. There are, though, many solution providers for this.
AI-generated responses vary based on AI tools, context, prompting, and model updates.
It's non-deterministic and private—users have individual experiences.
4 x AI brand visibility tracking
How can you track your brand's visibility in AI search results?
1. Test out new AI brand visibility tracking tools
Many solution providers, such as Advanced Web Ranking, Ahrefs, and Semrush, are developing offerings for tracking and optimising brand visibility across ChatGPT, Gemini, Claude, Copilot, and Perplexity.
2. Manual testing: Prompt-based auditing & customer feedback
2.1. Regularly ask questions related to your brand category and see if your brand appears. Use variations across different tools (ChatGPT, Claude, Gemini, Perplexity, etc.) and log the responses over time. Examples:
“What’s the best [product type] for [use case]?”
“Top alternatives to [competitor name]?”
“Can you recommend a [industry] solution for [pain point]?”
2.2. Ask new won customers how they found out about your brand: Did they find you first in an AI chat?
Track:
Does your brand appear?
How is it described?
Is sentiment positive, neutral, or missing?
3. Brand mentions monitoring tools (updated for AI)
Some reputation tools are adapting to the AI age:
Set up alerts for indirect mentions (e.g., “best CRM for startups” without naming your brand)
4. Custom GPT or assistant embedding
Create a branded GPT (if your audience is using ChatGPT Plus) that teaches users about your product or service. It’s part awareness, part conversion tool.
Conclusion: why should you track LLM brand visibility?
LLMs like ChatGPT are becoming the new front doors to customer discovery. To stay relevant, marketers must adapt brand strategy for LLM visibility.
1) ICP and customer understanding more crucial than ever
AI highlights brands that answer user-specific, detailed questions.
Tie your product or service to specific needs of your target buyer.
To get recommended, your brand needs to be clearly connected to ICP pain points.
2) Optimise for conversational prompts, not just keywords.
With AI search and chat, people search specifically & conversationally, not broadly.
Users ask detailed long-tail queries in the chat.
Tie the product to use cases and long-tail prompts.
3) Increase brand authority signals:
Produce quality content that aligns with E-E-A-T (Experience, Expertise, Authoritativeness, Trust) signals.
Publish accurate, helpful, and well-sourced content.
Link to expert sources, studies, and customer outcomes.
Become the source for your niche audience.
Publish on authoritative domains (like guest posts or features in known publications).
4) AI SEO
Ensure your site and content use schema markup (Product, Organisation, FAQ, etc.). This helps LLMs trained on web data recognise your content with more context.
Create FAQ-style content in your niche.
Use lists, tables, and examples to feed LLM-friendly formats.
Optimise technical SEO for AI crawlers.
Having good SERP (search engine results page) positions for your content maximises your chances to be highlighted by AI tools.
5) Audit your AI-first brand visibility:
Manually audit your brand visibility in AI tools regularly to track presence and sentiment.
Explore partnerships with platforms offering plugin or integration opportunities.
What are the best tools for monitoring brand visibility in AI search results? Test out emerging AI brand visibility tools for tracking and optimising for AI brand visibility.
Track AI traffic on your website
👉 Read also: Increase your brand’s visibility in AI citations & download AI citability audit
