Frequently asked questions about the next era of search

Unpack how companies achieve visibility inside large language models, from understanding how the new search stack works to choosing the right service partners and implementing the strategies that make it all work.

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Rethinking the rules of search

Best practices emerge from better quetions. This FAQ explains the principles, frameworks, and standards shaping the new discipline of AI search optimization.

AI Visibility for Brands & Businesses

Trust & authority | Machine-readable assets | Structured data | Content strategy

  • Showing up in AI answers starts with machine trust. Claim and verify your identity everywhere models look, then make it consistent. Build a tight knowledge graph with stable canonical IDs, authoritative profiles, and cross-linked references. Align names, founders, locations, and offerings across your site, social, and third-party sources. Publish a brand fact file and map relationships so models can disambiguate you from similar entities. Reducing uncertainty increases the chance a model selects you as a safe citation.

    Then cover the right ground with content written for how models learn. Define your domain, enumerate clusters and subtopics, and answer the exact questions users ask. Pair breadth with depth using clear headings, concise passages, and first-sentence answers. Front-load definitions and outcomes, use consistent terminology, offer comparisons and how-tos, and cite credible sources. Keep paragraphs scannable, make sections addressable, and shape summaries a model can lift cleanly. Knowing what to write and then how to write it builds topical authority and raises your probability of selection inside an LLM answer.

    Close the loop with AI-optimized schema. Use JSON-LD that declares the page type and primaryEntity, plus Organization, WebPage, and Article where appropriate. Include About and Mentions for entities, canonical IDs in sameAs, author and publisher with verified profiles, datePublished and dateModified, headline and description, inLanguage, and contentLocation if relevant. For FAQs use QAPage and mainEntity with question and acceptedAnswer. For product or service pages include concrete properties like brand, offers, areaServed, and serviceType. Make properties echo on-page claims and link out to authoritative identifiers. Schema that is complete, consistent, and verifiable lets models instantly understand the page and cite it with confidence.

Factually precise content | Schema.org cross-linking | Wikidata | llms.txt file

  • Start with facts the models can verify. Publish concise, source-backed pages that answer specific questions, define key terms, and document offerings with clear outcomes. Keep facts stable across your site and profiles, and add provenance metadata on each page so claims have dates, sources, and accountable authors. Maintain a public brand fact file and keep it consistent with your About page, press pages, and review platforms. Precision and consistency raise your probability of being selected as a safe citation.

    Attach machine-readable identity to every asset. Use canonical IDs for your organization and key people, and repeat them in sameAs links across properties. Cross-link your Schema.org entities to authoritative graphs like Wikidata and other stable registries so models can resolve you unambiguously. For articles and guides, include stable anchors for important passages, use addressable headings, and provide short, liftable summaries that match the page’s claim set.

    Stand up the infrastructure that tells LLMs where to look. Publish an llms.txt file at the root to list canonical sources for facts, feeds, sitemaps, and JSON-LD endpoints. Keep your JSON-LD complete and self-consistent: declare the primary entity, include about and mentions, verified author and publisher, canonical URLs, and cross-graph identifiers. Add isBasedOn, citation, or sameAs to tie claims back to originals. When precise content, canonical identity, structured links to public graphs, and explicit provenance all align, you make it easy for ChatGPT to ground on your material and cite you.

SEO ranking vs AI retrieval | Entity authority | Content clarity | Citation consistency

  • Being ranked or optimized for AI search means increasing the odds that a model selects and cites your brand when forming an answer. There is no universal results page or fixed position. Instead, models assemble responses by choosing entities and passages they can verify, explain, and trust. Optimization raises your selection probability by strengthening three things models care about most: entity authority, content clarity, and citation consistency.

    Traditional SEO ranks pages against queries on a results page. AI search retrieves facts and entities to compose a direct answer. In classic SEO, signals like links, anchors, and click data influence where a page sits. In AI retrieval, the focus shifts to whether your identity is unambiguous, your content covers the right cluster of topics with depth, and your claims align with external corroboration. The question is not “what position do I hold” but “how often am I the source chosen to ground this claim.”

    Operationally, optimization means building machine trust. Establish verified, consistent entity data across your site and profiles. Write helpful, precise content with liftable summaries that map to user questions. Keep schema complete and consistent with canonical IDs and cross graph references so models can resolve you without guesswork. When authority, clarity, and consistent citation signals line up, your brand gets pulled into answers more often, which is the practical meaning of ranking in AI search.

LLMs don’t “crawl” | Sitemaps | Public accessibility | Machine endpoints like llms.txt

  • ChatGPT does not index the web like Google. Most LLMs do not run an always-on crawler that continuously discovers and ranks pages. They rely on training data snapshots, licensed corpora, and a growing set of trusted, structured sources to ground answers in real time. Inclusion is therefore less about “getting crawled” and more about being easy to resolve, verify, and retrieve as a reliable source when a model assembles an answer.

    Make your site simple for machines to access and interpret. Keep key pages publicly reachable without logins, scripts, or heavy client rendering. Provide clean sitemaps for pages, images, videos, and structured data where applicable, and host them in robots.txt. Use stable URLs, descriptive titles, and clear, scannable content with addressable headings. Ensure your important facts are consistent across your site and third party profiles so a model can corroborate what it finds.

    Expose machine endpoints that tell LLMs where to look. Publish an llms.txt at the site root that lists canonical sources such as your primary sitemaps, JSON-LD feeds, brand fact file, and any change logs. Keep Schema.org JSON-LD complete and self consistent, with a canonical organization @id, sameAs links to official profiles, author and publisher verification, about and mentions for entities, and concrete properties that mirror on page facts. When public accessibility, sitemaps, machine endpoints, and rich schema align, you maximize the chances that LLMs can discover, ground, and include your site in answers.

Entity salience | Authority weighting | Factual consistency | External corroboration

  • ChatGPT mentions brands that are easy for the model to recognize, disambiguate, and rank as relevant to a query. That starts with entity salience. If your brand’s identity is fuzzy or inconsistent across the web, the model cannot confidently link mentions to a single entity. Clear names, stable canonical IDs, consistent facts about who you are and what you offer, and tightly linked profiles increase your “presence” in embeddings space. The more your pages, profiles, and references cohere around the same attributes, the higher the chance the model treats your brand as a distinct, on-topic answer candidate.

    From there, models apply authority weighting. Brands that accumulate reliable signals tend to outrank those with thin or noisy signals. These include sustained topical coverage, expert content that answers common questions, and credible sources that cite or reference your brand. Strong reputation signals such as high-quality reviews, authoritative backlinks, press mentions, and third-party profiles raise the model’s prior that you are a safe, helpful inclusion. When multiple entities could answer a prompt, the one with richer, more consistent evidence usually wins.

    Finally, external corroboration acts like proof. Verified IDs, publisher and author verification, structured data with sameAs to official profiles, and alignment between on-page claims and off-page sources reduce uncertainty. If independent sources repeat your facts, if review platforms and directories reflect the same details, and if your schema reinforces those facts, the system can ground on you with confidence. ChatGPT tends to surface entities it can verify, trust, and explain. Make your brand the easiest one in the set to verify.

Expertise | Transparency | Customer feedback | Entity reputation | Trust signals

  • Recommendations follow trust. Make your business the safest, clearest choice to include. Publish high quality pages that state who you are, what you do, where you operate, and for whom. Add evidence of expertise such as credentials, case studies, and named practitioners with bios. Be transparent about pricing models, processes, policies, and outcomes so a model can summarize you without guessing. Consistent facts across your site, profiles, and directories increase entity confidence and reduce the risk of confusing you with a similar brand.

    Signal a strong reputation with proof users recognize. Maintain a steady flow of genuine customer reviews on trusted platforms, address feedback publicly, and show third party recognition such as press, awards, or industry memberships. Build helpful thought leadership that answers common questions in your category and cite credible sources. The combination of customer validation and expert content raises your authority weighting, which makes recommendation more likely when the model compares candidates.

    Back it with machine readable trust. Use complete Schema.org with organization, services, people, and locations. Include canonical IDs, sameAs links to official profiles, and verified author and publisher data. Keep your data stable, timestamped, and in agreement with external sources. When transparent expertise, positive feedback, consistent facts, and structured identity all line up, LLMs have both the confidence and the material to recommend your business.

Create authoritative profiles | Link to Knowledge Graphs | Maintain canonical schema | Unique identifiers

  • Start by making your official identity easy to confirm. Create and maintain authoritative profiles for your organization, people, and locations on your website and on trusted third parties. Use consistent names, descriptions, addresses, and contact details everywhere. Publish a brand fact page on your site that lists key facts, leadership, services, and service areas, and link to it from your footer and About page. Consistency across these sources helps models confidently match all mentions to a single entity.

    Link your identity to public knowledge bases so models can resolve you unambiguously. Add or claim entries on Wikidata, Crunchbase, LinkedIn, and relevant industry registries. From your site’s profiles and articles, point sameAs to these official pages. Use stable, unique identifiers wherever possible, such as a Wikidata QID, company registry IDs, and consistent profile URLs. Tie your people to their verified profiles and publications. The more authoritative cross references you provide, the lower the chance of brand confusion with similarly named entities.

    Reinforce everything with canonical, complete Schema.org. Include JSON-LD for Organization, WebSite, WebPage, and Service or Product where relevant. Declare a single canonical @id for your organization and reuse it across pages. Add sameAs links to your official profiles, verified author and publisher data, founder, address, areaServed, and other concrete properties that mirror on-page facts. Keep schema synchronized with your visible content and update dateModified when facts change. Clear identifiers, authoritative profiles, and canonical schema together give AI models the strongest signal to recognize and verify your business correctly.