From SEO to LLMO: The New Science of AI Discoverability for Consumer Health Brands
The AI-Driven Search Evolution
Modern search is undergoing a seismic shift. Since mid-2024, many websites have seen organic search clicks decline as AI-powered answer engines and chatbots (like Google’s AI Overviews, ChatGPT, and Gemini) take center stage. The impact is already clear: AI-driven search usage is exploding, and traffic coming from these channels is on the rise. Importantly, AI search traffic tends to be highly valuable – a recent Semrush study found AI-overview visitors convert about 4.4× better than traditional organic visitors. In other words, being visible to an AI assistant can deliver far more business impact than a traditional click to your site.
For consumer health brands, this means the old playbook of SEO (search engine optimization) must evolve. Instead of only worrying about keyword rankings and backlinks, digital marketing and SEO teams now need to make sure their content and brand are mentioned and cited by AI answers. In practice, this means aligning content with what AI models consider trustworthy and easy to understand. The new goal is optimizing for AI visibility, not just for clicks. This whitepaper from Azoma.ai outlines how health brands can navigate this transition by adopting LLMO (Large Language Model Optimization) strategies – in effect, the new “SEO” for the AI era.
SEO vs. LLMO: A New Visibility Paradigm
Traditionally, SEO focused on ranking pages in search results using keywords, links, and on-page optimizations. LLMO flips that model. Instead of aiming for high position in a blue-link listing, LLMO targets being included in AI-generated answers. Put simply, SEO was about driving clicks; LLMO is about driving awareness and authority within AI outputs. For example, a search-focused campaign might optimize a page for “best probiotic supplements” and win a top Google ranking. An LLMO campaign would instead ensure that the same page has clear schema markup, cites scientific studies on probiotics, and is written so an AI could quote or reference it in an answer to “Which probiotic is recommended?”.
Traditional SEO vs. Generative AI SEO (LLMO) focus areas. Traditional SEO relies on keywords and link authority, whereas LLMO emphasizes semantic context, structured data, and trust signals to maximize visibility in AI answers. In practice, while SEO practitioners still use keyword research and link-building, LLMO practitioners build content to be parsed, understood, and cited by AI. Success metrics shift from page rank and clicks to brand mentions and citation frequency in AI responses.
Several terms capture this shift. Some marketers talk about Answer Engine Optimization (AEO) for appearing in Google’s AI summaries, or Generative Engine Optimization (GEO) for being cited by any AI answer engine. LLMO broadly encompasses all of these, emphasizing that the key new currency is being referenced in AI outputs. As one analysis notes, “LLMO is the practice of optimizing your content… to appear in AI-generated responses”. In short, where SEO was human-centered (clicks, sessions), LLMO is AI-centered (brand reach, trust).
Why AI Doesn’t “Read” the Web Like Google
It’s crucial to understand that AI assistants don’t discover and index the web in the same way as search engines. Googlebot continuously crawls and indexes pages, evaluating them by links and keywords. Large Language Models (LLMs) like ChatGPT, Gemini, or Claude, on the other hand, operate differently. Many AI systems answer queries using a combination of their trained knowledge and a retrieval step: they fetch relevant documents at query time (a process called Retrieval-Augmented Generation, or RAG). In other words, the AI doesn’t rely on an up-to-the-minute site index; it pulls in content that is easy to ingest and deemed trustworthy by its algorithms.
This difference has big implications. For one, LLMs have limited context windows. They cannot process an entire complex webpage full of navigation menus, ads, and arbitrary HTML. As Jeremy Howard’s proposal explains, LLMs “context windows are too small to handle most websites in their entirety”. Thus, an AI might land on any page and never “find” your key information unless it’s presented very clearly. This is why techniques like a dedicated llms.txt (to be discussed below) are emerging—to signal directly which pages contain the gold-standard content.
Another factor is training data. Most LLMs (ChatGPT included) were trained on massive text corpora up to a certain date; they don’t natively “see” new websites in real-time. Instead, when you query them about a topic, they combine what they learned during training with any fresh data retrieved via plugins or browsing tools. As SEO expert Rand Fishkin points out, the “currency” of LLMs isn’t links at all but word co-occurrences in their training data. In other words, to an AI, a brand is as visible as its footprint in the collective text the model learned from. If your brand was rarely mentioned in the sources that trained the model, it won’t readily come up unless you influence its knowledge base (for example, by getting mentioned in relevant articles or citations on the web).
Finally, AI systems look for clarity and trust in content. SearchEngine Land notes that these models “draw from what’s easy to ingest, easy to understand, and easy to trust”. Similarly, GrowthMarshal observes that LLMs evaluate content by embedding it in a semantic space, prioritizing “strong coherence, entity consistency, and verifiable sourcing” rather than counting backlinks or keyword matches. Practically, this means copy-paste product descriptions or thin content that once helped SEO won’t get an AI’s attention. Instead, well-structured, authoritatively-sourced content stands a better chance of being read (and cited) by LLMs.
Technical Foundations: Schema, llms.txt, and Content Formatting
To succeed in AI search, brands must build their online presence on strong structural foundations. Structured data (Schema.org markup) is one of the most important tools. Schema makes web content machine-readable by explicitly labeling the type of entity (article, product, person, etc.), its attributes, and relationships. When you add schema markup to a page, you’re effectively telling AI “this page is about [X], authored by [Y], and relates to these facts”. For example, applying an Article schema (with author and date) helps the AI attribute facts to the right expert, while Product schema on a supplement page supplies dosage, price, and review info in a standardized format. In practice, AI models that consume web content use this markup as signals for what to trust and cite.
Key schema types to implement include:
Article / NewsArticle: Defines the content type, author, date, and publisher. This lets AI outputs mention your author’s expertise (an E‑E‑A‑T signal) and link to your findings.
FAQ & HowTo: Structures question-answer pairs or step-by-step instructions. AI can easily pull these into its replies, making your site a likely source of concise facts or guides.
Product / Review: Details product specifications, pricing, and aggregate reviews. This enables AI to accurately summarize features or comparisons (e.g. “According to [YourBrand]’s data, supplement X contains Y mg of vitamin D).
Organization / LocalBusiness: Encodes your brand’s official name, logo, and contact info. This consistency ensures the AI recognizes your brand entity correctly across contexts.
Person / Author: Links content to qualified individuals (doctors, scientists, etc.). Showing author credentials (degrees, affiliations) helps AI gauge expertise and attribute quotes appropriately.
Dataset / ResearchStudy: Describes original research data or clinical studies (e.g. sample sizes, metrics, outcomes). Since AI systems favor primary data, marking up any proprietary study makes it far more likely to be cited in answers.
Rich, precise schema is increasingly a trust signal. The GrowthMarshal analysis emphasizes that adding detailed Schema.org markup (especially types like DefinedTerm, Dataset, and ResearchStudy) “improves entity recognition and strengthens trust signal transmission in AI indexing”. In other words, structured markup not only makes AI understand your content better, it also signals to the AI that your content is credible and well-defined. Moreover, aligning schema across platforms (for example, having the same @id or entity name in Wikidata as in your web markup) further reinforces trust.
Alongside schema, a new convention called llms.txt is gaining attention as an “AI sitemap.” Similar to robots.txt or sitemap.xml but for LLMs, an llms.txt file lists the URLs of your highest-value content in a simple Markdown format. Placing llms.txt at your site root gives AI agents a curated map to your best pages. As one SEO guide explains, “llms.txt lives in the same spot [as robots.txt] but it’s built for an entirely different voyage – it’s more like a hand-crafted sitemap for AI tools than a set of crawling instructions”. By using llms.txt, you tell an AI exactly which pages to ingest and potentially cite, instead of forcing it to wander through your whole site. This is especially important because, as noted above, AI “might not hit your homepage” or easily find buried content. A well-maintained llms.txt (or comprehensive HTML sitemap) ensures that when an AI agent fetches your site at inference time, it encounters your best, fully-detailed pages first.
Finally, content formatting is crucial. AI models love clarity: use clear headings, bullet lists, and short paragraphs so each fact stands out. Write answers to user questions directly, and use a neutral, factual tone (AI tends to down-weight overly promotional language). In consumer health content, this often means integrating author credentials, citing clinical guidelines or peer-reviewed studies, and even quoting official sources. For example, embedding a reference link to a CDC guideline or including a footnote to a PubMed study signals to the AI that your information is grounded in authority. In short, prepare each page as a mini knowledge graph: labeled entities, precise data, and verifiable sources. This structured approach makes your site’s content digestible to the AI, maximizing its chances of being used in an answer.
Authority and Trust: The New Signals
In the AI search era, who you are matters as much as what you say. AI assistants treat credibility very seriously. For example, a Search Engine Land analysis of AI citations showed that health-related queries overwhelmingly cite academic and government sources – PubMed, the CDC, WHO, etc. – rather than brand sites. These independent sources have built-up authority, so AI “rewards” their knowledge. This mirrors Google’s own E‑E‑A‑T (Experience, Expertise, Authoritativeness, Trustworthiness) framework: consumer health topics are YMYL (Your Money or Your Life) matters, and AI follows suit by defaulting to content with clear expertise and neutrality.
Some key trust indicators in AI include:
Entity Consistency: Always refer to your brand or key terms in the same way. LLMs “punish semantic drift,” so if your company is sometimes “HealthFirst” and sometimes “Health First Inc.,” AI confidence drops.
Knowledge Graph Alignment: Make sure your brand has entries in Wikidata, Crunchbase, or other knowledge bases, and that these align with your site schema. AI models cross-check these graphs; a strong, consistent presence in them boosts your credibility.
Original Data and Research: Publishing unique studies or data tables dramatically enhances trust. GrowthMarshal highlights that LLMs favor content with “first-hand research, proprietary data, or original frameworks”. For a health brand, that could mean releasing a clinical trial dataset (marked with Dataset schema) or a whitepaper of survey results.
Provenance and Transparency: Clearly timestamp and version major content (like whitepapers or datasets). LLMs increasingly prefer content with verifiable history (think of it like “blockchain” for articles). If your blog post shows it was updated last month with new statistics, the AI knows it’s current.
Citations and Endorsements: Every time an AI cites your content (even without a link), it’s a win. Being mentioned in answers is a powerful signal of trust. In fact, one study found that ChatGPT mentions brands 3.2× more often than it formally cites them, meaning that even unlinked recognition boosts your AI presence. Work to get your brand name and experts quoted in other high-trust content (news sites, forums, research summaries) so that the AI will pick up your name when summarizing topics.
Notably, traditional SEO metrics are becoming less predictive. GrowthMarshal bluntly states: “DA, PA, DR…were invented for an earlier era. They signal human-judged popularity, not AI-judged credibility. Modern LLMs…don’t count backlinks; they measure coherence, consistency, and confidence”. Thus, a site with millions of links but no clear authoritative voice may fall behind a smaller site that a medical society frequently cites. Consumer health brands must therefore elevate their authority signals: rigorous references, expert authors, and visible alignment with recognized institutions.
Case Study: Boosting Brand Retrieval in ChatGPT
Imagine a supplement company, HealthFirst Labs, struggling to appear in AI answers about vitamin D or heart health. Initially, ChatGPT and similar assistants cite sources like Mayo Clinic or NIH, and HealthFirst is nowhere to be found. To turn this around, the marketing team applies LLMO tactics.
First, they audit structured data on their site. Every product page gets rich Product and AggregateRating schema; expert articles are marked up with Article (author = Dr. Smith, nutritionist) and they even add MedicalEntity where relevant. They ensure the Organization schema matches their Wikidata entry exactly.
Next, they create content that mirrors what neutral sources do. Instead of writing only marketing copy, they publish in-depth guides on vitamin D benefits citing clinical studies and comparing their supplement to others. Each guide starts with an AI-friendly FAQ (“What is vitamin D?”, “How much vitamin D do I need?”) to attract snippet-style answers. The tone is educational, and each piece has a clear author with credentials.
Simultaneously, they build their credibility off-site. They contribute a bylined article on a respected health blog (with a link and mention of HealthFirst), and issue a press release about a new clinical study they funded. They collaborate with a university on research whose results appear on NIH’s ClinicalTrials.gov (with HealthFirst as sponsor). These moves seed the broader web with their brand and data.
Over a few months, tools show a change: HealthFirst starts surfacing in AI outputs. BrightEdge research finds that brand mention patterns in ChatGPT are heavily driven by “trigger words” in queries. Indeed, ChatGPT now mentions HealthFirst when users ask transactional questions like “Where to buy vitamin D supplements cheaply” (keywords: cheap, deals, where to buy). In BrightEdge’s analysis, queries containing words like “deals,” “affordable,” or “where” led to 4–8× higher brand mentions. By optimizing their content around such queries and tracking AI responses, HealthFirst captured more chat presence.
ChatGPT brand mentions triggers. In BrightEdge’s study, queries with terms like “where,” “cheap,” and “deals” generated far more ChatGPT brand mentions than purely informational queries. HealthFirst leveraged this by targeting content to those query types and monitoring AI visibility.
This outcome aligns with industry observations. A report on LLM brand visibility noted that AI tends to favor comparative and unbiased content over single-brand sales pages. HealthFirst’s guides explicitly compared multiple supplements (even competitors) and made clear their editorial intent. As a result, ChatGPT began treating their site like an expert reference, sometimes even including their specific product in answers. One AI answer now says, “Vitamin D3 supplements like HealthFirst’s (which provides 1000 IU per dose) are commonly recommended alongside sunlight and diet,” whereas before HealthFirst never appeared.
In summary, by combining structured data, authoritative content, and strategic outreach, the brand “taught” the AI that they are a credible source in this category. Within the AI answer ecosystem, HealthFirst’s visibility jumped from nearly zero to being cited in a significant share of relevant ChatGPT answers.
Practical Guidelines for Consumer Health Marketers
Based on these principles, here are actionable steps for health brands aiming for AI discoverability:
Build a Unified Brand Entity: Ensure your brand’s name, logo, and descriptions are consistent across your website, social media, and data platforms. Create or claim your Wikidata/Crunchbase entry and link it to your schema.org markups. Consistent “entity definition” strengthens the AI’s confidence in recognizing your brand.
Implement Robust Structured Data: Audit all key pages to include appropriate schema. Product pages should use Product and Review markup; blog posts and guides should use Article with author info; FAQs should use FAQPage; and research or data should use Dataset or ResearchStudy schema if possible. Validate with Google’s Rich Results Test or similar tools. Well-structured data not only helps AI parse your content but also signals that your content is credible.
Produce Authoritative Content: Invest in genuinely helpful, well-researched content. For health topics, cite peer-reviewed studies, guidelines, or official stats. Consider hiring medical writers or experts to review/author content (and mark them up as the author). Use a neutral tone and avoid marketing jargon. Wherever possible, include clear references or links to source material; this will help AI models verify and trust your information.
Leverage llms.txt (or Equivalent): Create an llms.txt file that lists your best AI-relevant content (e.g. ultimate guides, product specs, datasets). Even if not using the formal spec, at minimum maintain an XML sitemap that highlights these pages. The goal is to guide any AI "crawler" directly to your most important content.
Monitor AI Mentions and Engagement: Use specialized tools or tracking queries to see when and where your brand appears in AI responses. Tools like LLMPulse or LLM monitoring platforms can scan chatbot answers for your brand name. Look for keywords and question patterns that trigger mentions. For instance, if “best [product]” queries mention you, double down on that topic. If “how to use [product]” queries never mention you, consider adding targeted content on usage tips.
Optimize for Conversational Queries: Chatbots handle more natural-language questions. Rewrite some content as direct answers to common queries. Use the kinds of questions users ask in help forums or Q&A sites. Include synonyms and semantically related terms (for example, “cardiovascular health,” “heart supplements,” “cholesterol-lowering” instead of just one keyword). This helps cover the broader semantic space the AI explores.
Track and Iterate: AI search is evolving. What works today might change as models update. Regularly review which content is being cited and adapt. If a site like ChatGPT’s model improved its medical knowledge, add more recent citations or update facts. If you see another source consistently beating you on certain topics, analyze their content structure and trust signals and learn from it.
These tactics require both technical changes and high-quality content creation. For consumer health brands, the payoff is large: appearing in an AI’s answer can build brand trust and drive highly qualified traffic. It essentially makes your brand part of the conversation, even when the user doesn’t click through to your website.
Conclusion
The rise of AI-powered search represents a fundamental change in how consumers discover information — and brands. For consumer health companies, optimizing for this new environment means moving beyond traditional SEO signals of keywords and backlinks to a focus on structured data, citations, and trust. As industry experts have observed, the future of digital visibility lies in “optimizing for visibility, not necessarily for clicks”. This means ensuring your content is AI-readable (via schema and clear structure) and AI-trustworthy (via authority and citations). In practical terms, health brands should think like publishers and scientists: publish rigorous, well-labeled content and become a known entity in trusted health knowledge networks.
It’s no longer enough to rank #1 on Google; the real challenge is to rank in ChatGPT and other AI answers. Fortunately, early adopters who engineer their content for LLM retrieval stand to gain tremendous advantage — after all, AI-referred visitors convert at multiple times the rate of traditional visitors. By following the guidelines above, consumer health teams can bridge the gap from the SEO era into the new age of LLMO, ensuring their brands are cited as trusted sources in the answers of tomorrow’s AI-driven searches.