Every AI search product builds an internal model of what your company is, who runs it, what it does, and how to describe it. That model gets built from a combination of your website, your social profiles, third-party publications that mention you, Wikipedia if you have an entry, Crunchbase, the LinkedIn pages of your employees, and any other sources the AI training and retrieval systems pull from. When a user asks ChatGPT or Perplexity or Google AI Overviews about your company, the answer is generated from that model. If the model is accurate, complete, and consistent, the AI describes you accurately. If the model is partial, contradictory, or based on outdated sources, the AI describes you incorrectly, and the incorrect description spreads as the AI products quote each other.
A brand entity page is the structural lever you control to shape the model. Done well, it becomes the canonical source AI products treat as the authoritative reference for facts about your company. Done poorly, it leaves the AI products to construct your description from whatever they find first, and the result is often wrong. This piece walks through how to build a brand entity page that works for AI search, what schema markup matters, what content to include, and how to maintain the page so it keeps shaping the AI representation of your company over time.
What an entity page actually does
The traditional About page on a corporate website was designed for human readers. It told the company story, introduced the team, and built emotional connection. The format favored narrative over fact density and rarely included structured markup.
A brand entity page is designed for both human and machine readers. The human reader still gets the story and the introduction. The machine reader gets a structured set of facts marked up in schema, with citations to third-party sources, organized in a way that maps to the schema.org Organization vocabulary. The same page serves both audiences but is engineered for the machine reader to parse cleanly.
The machine reader matters because of how AI products construct their model of your company. They read your website. They read structured data on your website. They cross-reference against third-party sources. They build entity confidence from the consistency of the references. A page that explicitly states the canonical facts, marked up in schema, gives the AI products a clean source of truth that anchors their model.
Without an entity page, the AI products construct the model from fragments scattered across your site, your LinkedIn, your press coverage, and whatever other sources they encounter. The model that emerges is a probabilistic guess. Sometimes it is right. Often it has small errors that propagate (wrong founding year, wrong founder names, wrong description of what the company does, wrong industry classification). Each error becomes a quote in an AI answer, and corrections require systematic intervention.
The structural pattern
The most effective entity page structure has six sections.
Identity facts at the top. Company name. Legal name if different. Year founded. Founders by name. Headquarters location. Type of company (private, public, subsidiary, division). These facts are stated explicitly in prose and marked up in schema.
What the company does. A clear, factual description of the products or services, the customer types, and the geographic footprint. Avoid marketing language. Write the description as if it would appear in Wikipedia, with verifiable facts and minimal puffery.
The team. Named founders, named executives, with their roles and brief credentials. Each named person should link to their public professional profile (LinkedIn at minimum) and ideally to a personal page on the same site. The named people become entities in the AI knowledge graph, attached to the company entity.
The history and milestones. A factual timeline of major company events: founding, key product launches, funding rounds, acquisitions, geographic expansions, awards. Each milestone should have a date and, where possible, a citation to a third-party source that documented the event.
External references and recognition. A list of meaningful third-party citations: press coverage from established publications, awards from credible programs, partnerships with named entities, certifications from recognized authorities. Each citation should link to the third-party source, not just describe it.
Contact and operational facts. Address, primary contact methods, geographic service area, operational hours if relevant. These feed into Local Business and Organization schema and help AI products answer practical questions about how to engage with the company.
The page reads naturally as prose with these sections sequenced as headers. The schema markup runs underneath, mapping the prose facts to the schema.org Organization vocabulary.
The schema markup
Organization schema is the foundation. The full property coverage that matters includes:
The name property with the canonical company name as it should appear in AI answers. If the company has both a brand name and a legal name, use the brand name as @name and the legal name as legalName.
The url property pointing to the company’s primary domain.
The logo property with a high-resolution logo image at a public URL. The logo is what AI products use as the visual identifier in answers.
The description property with a 150-200 character description. This often appears verbatim in AI Overview citations and Knowledge Panel results.
The foundingDate property with the date the company was established. Use ISO date format.
The founders property as an array of Person objects, with each founder’s name, link to their professional profile, and brief description.
The address property with the full headquarters address, structured as PostalAddress. If the company has multiple locations, the headquarters address goes in Organization and additional locations can be marked up as Place objects elsewhere on the site.
The areaServed property indicating where the company operates. Can be a list of countries, regions, cities, or a single canonical entry depending on the company’s footprint.
The sameAs property as an array of canonical URLs pointing to the company’s profiles on other authoritative sites. LinkedIn page. Twitter or X profile. Crunchbase profile. Wikipedia page if one exists. GitHub organization if applicable. Bluesky profile. The sameAs property is what tells AI products that all these profiles refer to the same entity.
The numberOfEmployees property if disclosed. Can be a specific count or a range (51-200, 1001-5000).
Industry-specific properties where applicable. naics code. industry. parentOrganization or subOrganization if part of a corporate structure.
The schema markup goes in the page head as JSON-LD. The JSON-LD format is preferred over Microdata because it separates structure from content and is easier to maintain.
The citations problem
The single most underused tactic in brand entity page construction is third-party citations.
AI products do not just read your page. They cross-reference your claims against external sources to verify them. A claim on your entity page that “we were founded in 2018” carries more weight if the page links to a TechCrunch article from 2018 covering your launch. A claim that “we serve Fortune 500 customers” carries more weight if linked to a public case study or a customer’s own announcement.
The pattern is to make every significant claim citable. Not just “we have raised $50 million” but “we have raised $50 million across three rounds (Series A in 2020 led by Sequoia, Series B in 2022 led by Andreessen Horowitz, Series C in 2024 led by Insight Partners)” with each round linked to the press coverage that documented it.
The citations serve three purposes. They give AI products verification anchors. They build authority signal because the cited sources are themselves authoritative. They demonstrate that the entity page is maintained by people who care about accuracy, which becomes a credibility signal in itself.
The maintenance overhead is meaningful. Every milestone added to the entity page needs an accompanying citation. Citations need to be checked periodically because URLs break and articles get pulled. The discipline of maintaining citations weeds out the milestones that should not have been added (ones with no third-party coverage to cite) and creates a more rigorous entity page over time.
What to leave off
A brand entity page should not include:
Marketing claims that cannot be substantiated. “Industry-leading,” “best-in-class,” “revolutionary,” and similar superlatives without citation get parsed as marketing voice and dilute the credibility of the page.
Customer logos without permission or citation. Some customer logos can be used per contractual terms. Others cannot. Logos used without contractual right become reputational and legal risk and can backfire when the customer publicly complains.
Information that is not stable. Quarterly revenue figures, current employee count down to a specific number, current customer count down to a specific number. These metrics change and a stale entity page with a 2024 employee count in 2026 reads as poorly maintained. Either omit the metric or commit to updating it.
Speculation about the future. Roadmap claims, expected growth, planned acquisitions, anticipated awards. The entity page is a factual document about what is, not a forward-looking statement.
Non-canonical names. If a company has had multiple names through M&A or rebranding, the entity page should state the current canonical name and reference past names as history rather than alternatives. AI products that encounter multiple “main” names get confused and produce inconsistent results.
The maintenance cycle
A brand entity page is not a one-time build. It is a maintained document.
Quarterly review of the facts. Are the founder names still accurate? Did anyone leave the company who should be removed? Have any new milestones occurred that should be added? Are the current employee count and revenue figures (if disclosed) still accurate?
Annual review of the citations. Do all the linked sources still resolve? Have any URLs broken? Have any of the cited articles been substantively updated in ways that change the citation context?
Continuous monitoring of how AI products describe the company. Search yourself in ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini at least monthly. Note what they get wrong. The entity page is where you correct the persistent errors.
When something material changes (a new round of funding, an acquisition, a leadership change, a major customer win, a major product launch), the entity page gets updated within a week. The faster the update, the faster the AI products start reflecting the new reality.
How AI products use the entity page
Understanding how each AI product uses the entity page helps with optimization.
Google AI Overviews and Knowledge Panels read entity pages as canonical sources. The schema markup feeds Knowledge Graph entries. The descriptions feed snippet generation. The sameAs links feed cross-reference verification.
Perplexity, ChatGPT with browsing, and Claude with web search read the entity page as a primary source when they encounter the company. The retrieval pipelines pull the entity page near the top of the candidate set for queries about the company itself.
LLM training pipelines pull from the public web, including entity pages. The frontier model training cycles take 12 to 24 months from data collection to deployment, so the entity page you publish today shapes the next-generation model’s training data.
The compounding effect is meaningful. A well-maintained entity page becomes the canonical source across multiple AI products simultaneously. Errors that show up in one product often disappear after the entity page is corrected, because the products converge on the canonical source over time.
A practical build sequence
For a company starting from no entity page or a weak one, the practical sequence is straightforward.
Week one: gather the facts. Founders by name with current titles. Founding date verified from incorporation records or original press coverage. Headquarters address. Current product or service descriptions. Major milestones with dates. Major press coverage. Key external profiles (LinkedIn, Crunchbase, Wikipedia if applicable).
Week two: write the page. Six sections in clear prose. Specific facts. No marketing language. Active voice. Short paragraphs.
Week three: add the schema. Organization schema as JSON-LD with full property coverage. Sub-objects for founders (Person schema), address (PostalAddress schema), and any subsidiaries or parent organizations.
Week four: add the citations. Every significant claim gets a third-party reference. Every milestone gets a press citation if available. Every executive gets a LinkedIn link. Every external profile gets a sameAs entry in the schema.
Week five: publish and monitor. Publish at /about or /company URL. Submit a sitemap update to Google Search Console. Begin weekly testing of how AI products describe the company.
Subsequent quarters: maintain. The page is now the canonical source. Treat it as such.
The entity page is one of the highest-impact pages on a corporate website for AI search. The attention it earns relative to its build cost is exceptional. Companies that get it right see the AI products converge on accurate descriptions of their business within a few months. Companies that ignore it watch the AI products construct descriptions from whatever they find, and accept the resulting inaccuracies as a cost of doing nothing.