Increase your brand’s visibility in AI citations
How to make your brand more visible in AI citations: technical and editorial tactics that improve the likelihood your brand is cited in AI answers.
Want ChatGPT, Claude, Perplexity, or Google’s AI Overviews to mention your brand? Start optimising to be included in the conversation.
AI citations are the new backlinks
In the world of AI brand visibility, being cited or mentioned by a large language model (LLM) is the new standard of excellence.
However, unlike Google’s link-driven ranking, LLMs derive answers based on contextual relevance, semantic richness, and data confidence, rather than just traditional domain authority.
So if you're wondering:
“How can I make my brand more AI-citable?”
“Why don’t LLMs recommend my product even though we rank on Google?”
“How can I track my brand's visibility in AI search results?”
...this article breaks it down.
What it means to be “AI-citable”
To be AI-citable means your content is:
Recognised by LLMs as relevant to a question
Referenced directly or indirectly in responses
Contextualised in a way that matches real user intent
In other words, your brand becomes an answer.
Why LLMs don't just "read your site"
LLMs are trained on billions of parameters and patterns, including how often a brand or product is mentioned in relevant contexts. LLMs like ChatGPT (GPT-4 >), Claude, and Gemini were trained on:
Public web data (pre-2023 in many cases)
Structured datasets like Wikipedia, StackOverflow, Reddit
Authoritative publications
Some AI tools are no longer limited to static training data; they can pull in real-time information from the web using techniques like retrieval-augmented generation (RAG) or integrated web browsing capabilities.
Tools like Perplexity, Bing AI, and certain modes of ChatGPT (with browsing enabled) can search the internet on demand, retrieving fresh, relevant content—including your latest blog posts, news coverage, or product pages—at the moment a user asks a question.
This means that your brand can show up in AI-generated answers if:
Your content is publicly available and crawlable
It’s well-structured (with semantic clarity, headings, and metadata)
You’ve optimised for long-tail use cases (answering users’ questions)
It’s published or cited on authoritative or well-linked domains (e.g., Press, Wikipedia, trusted forums)
How to make your brand more AI-citable
1. Use structured content formats
1) Add schema markup (FAQ, Product, How To, Article).
Schema Markup helps to support how LLMs understand content.
In March 2025, Fabrice Canel, principal program manager at Bing, confirmed that Microsoft uses structured data to support how its large language models interpret web content.
Google structured data engineer Ryan Levering shared at Google’s Search Central Live event that schema markup plays a critical role in scaling Google’s generative AI systems.
2) Use clear subheadings, numbered lists, and bullet points.
3) Focus on one key topic in each chapter for content clarity. In addition to AI, humans also love content clarity: writing for clarity and conciseness is also one of the golden rules of journalism.
Keep sentences short and to the point (concise, brief).
Limit each chapter to one main idea (focus, clarity).
4) Provide definitions, comparisons, or decision frameworks (LLMs love patterns).
💡 Tip: Tools like Perplexity and Google’s AI Overviews tend to surface list-based, practical content more often than long editorial paragraphs.
2. Create use-case-driven content
This is important because when someone asks a tool like ChatGPT, Perplexity, or Claude a question (e.g., “What’s the best marketing tool for B2B lead nurturing?”), the model doesn’t just look for brand names. It looks for text that clearly explains what a product does, who it helps, and how it solves a specific problem.
By structuring your content around real-world use cases, you’re:
Giving LLMs the exact kind of language and framing they need to surface your brand in relevant answers
Increasing the chance that your brand appears as a helpful solution in AI-powered customer journeys
Making your value proposition more “citational”—easier for AI to quote, summarise, or reference in response to a prompt
💡 This is why specificity matters:
🚫 “We sell marketing automation software.”
👉 The above description is too generic. LLMs won’t understand when or why to surface it.
✅ “Our automation platform helps B2B teams increase lead conversions by 32% through multi-touch email workflows, with integrations into Salesforce and HubSpot.”
👉 This gives the model a contextual anchor: problem, solution, and audience, making it far more likely to be cited.
3. Publish on high-authority domains
In an AI-powered discovery landscape, your brand’s visibility doesn’t just depend on your own content, it depends on who else is talking about you and where.
Large Language Models build their knowledge from a mix of training data and current sources, including high-authority websites, news media, government databases, Wikipedia, review platforms, and research repositories.
These sources act as trust signals. If your brand is mentioned repeatedly across these trusted domains, LLMs are more likely to associate your name with credibility and expertise.
3.1. This means you need to shift your PR strategy
Traditional PR focuses on awareness, backlinks, or share of voice. But AI-first PR is about shaping how machines perceive your brand.
That means marketers and PR teams should:
Prioritise being referenced in high-authority media (e.g., TechCrunch, Fast Company, Gartner, Wired, trade publications)
Seek placement in knowledge-rich domains like Wikipedia, Reddit threads, academic citations, and Quora answers
Partner with analysts and industry bloggers who are frequently cited in AI training corpora
Ensure that brand descriptions in external sites are clear, up-to-date, and consistent
The goal isn’t just media coverage—it’s semantic visibility. You're trying to help AIs understand your brand’s relevance to specific topics, industries, and user queries.
The more high-authority sources that mention you in context, the more “citational gravity” your brand has in AI outputs.
4. Include external evidence and sources
In the world of AI-generated responses, credibility is algorithmic.
LLMs prioritise content that not only answers a question but also shows signs of trustworthiness. Including links to external, authoritative sources—like academic studies, government statistics, industry benchmarks, or expert quotes—signals to the model that your content is factual, reliable, and informative.
These references serve a similar function in AI as they do in journalism or academia: they validate claims and improve the perceived quality of your content.
💡 What to include
Links to scientific studies or whitepapers
Third-party product reviews (like G2, TrustRadius, etc.)
References to industry reports (Gartner, McKinsey, etc.)
Customer testimonials backed by real metrics
Media coverage or analyst commentary
Embedded data tables or visualisations from reliable sources
💡 So if your page says:
“Our product improves sales team productivity by 27%”,
and if you link to a reputable third-party study or show your methodology, it’s more likely to be:
Picked up in LLM crawling (for tools with browsing or RAG)
Used in AI-generated answers
Perceived as a “trustworthy source” in semantic clustering
🚫 But, if your content relies purely on slogans, bold claims, or vague benefits (“#1 in customer satisfaction!”), AI tools may either:
Ignore it altogether in favour of more verifiable content
Paraphrase it in a way that removes your brand attribution
Rank you lower in AI-generated lists due to lack of supporting data
5. Answer long-tail prompts
Every time someone types a detailed question into an AI tool, that’s a moment of intent. If your brand doesn’t have content that fits that exact prompt, you won’t be in the answer.
When users turn to AI tools, they aren’t just typing in short keywords like "CRM" or "running shoes." They're asking detailed, intent-rich questions like:
“What’s the best CRM for a remote sales team under 50 people?”
“Which running shoe helps reduce knee pain for overpronators training for a marathon?”
“Can I automate client onboarding workflows in HubSpot?”
These are long-tail prompts—natural language queries that reflect specific needs, contexts, and buyer journeys.
💡 How to do this well
Create FAQ pages or blog posts that mirror real customer queries
Use headings and copy that reflect natural question formats
Build “best for X” or “how to solve Y with our tool” pages
Include comparisons, use cases, and buyer-specific advice
Use semantic variations to cover the different ways people ask the same question
💡 Tip: You can test your brand visibility in ChatGPT, Claude, or Perplexity with prompts like:
“What are the best CRMs for startups under $100/month?”
“Alternatives to [Competitor] for small business accounting”
If you don’t appear, go create content that directly answers those queries.
Bottom line
The digital journey has shifted. What used to span multiple clicks, channels, and touchpoints can now happen in a single AI conversation.
If your brand isn’t understood, trusted, and contextually relevant to tools like ChatGPT, Perplexity, Claude, or Bing AI, you’re invisible in the moments that matter most.
This isn’t traditional SEO. It’s not just PR. It’s LLM optimisation, a new frontier where marketers must create content that’s:
Use-case driven (solving real problems with clear value)
Rich in external signals (citations, third-party validation, and structured data)
Mentioned across authoritative domains
Semantically aligned with how AI models understand intent
You’re not just trying to rank. You’re trying to be recommended, retrieved, and referenced by algorithms acting as your buyers’ digital assistants.
The brands that adapt early won’t just win clicks, they’ll win the entire conversation.
