Why Contradicting Yourself Across Pages Kills AI Trust Signals
AI systems compare claims across every page on your domain. A single factual inconsistency can reduce trust scores across your entire site.
When an AI system processes a website, it does not evaluate each page in isolation. It builds a cross-page model of the domain's entities, comparing attribute claims across pages to assess consistency. A company that describes its founding year as 2010 on the About page and 2012 on a case study, or lists its headquarters as London on the homepage and Manchester in its schema markup, triggers a contradiction signal. This signal reduces the system's confidence in every claim made by the domain, not just the specific claim that contains the inconsistency.
What Gets Cross-Referenced
- Entity founding dates: if any two pages describe the same entity with different founding years, the inconsistency is flagged
- Employee counts and revenue figures: statistics that appear in multiple contexts should use consistent ranges and update dates
- Location and address data: especially important for LocalBusiness schema — inconsistency between schema and body text reduces voice retrieval probability
- Schema name attributes vs body text entity references: the exact name used in schema must match the name used throughout body text
- Author claims: an article schema attributing authorship to a named person should have that person mentioned in the article body
- datePublished vs visible content dates: if schema claims a publication date inconsistent with visible page metadata, the contradiction is detectable
▲Run a cross-page entity audit on your five most important factual claims. Check that each claim appears consistently across every page where it is mentioned, in your schema markup, and in any external sources you link to. A single inconsistency in a high-visibility claim (founding year, headquarters, primary service description) can propagate a trust penalty across the entire domain.
Schema vs Body Text Alignment
The most common source of AI-detectable contradiction is misalignment between schema markup and body text. Schema was added by a developer using official business registration data; the body text was written by a marketing team using current brand messaging. They often describe the same entity differently. A schema name attribute of "Smith & Jones Technology Limited" alongside a body text that consistently refers to "Smith Jones Tech" registers as a potential contradiction in entity recognition systems. Either align the schema to match the brand name used in body text, or add a legalName attribute to the schema and use the trading name in body text.
Temporal Inconsistencies
As websites accumulate content over years, temporal inconsistencies emerge naturally. A "we have been in business for 10 years" statement written in 2018 is still visible on the site in 2025 and now contains a factual error. AI systems that model temporal consistency weight content with absolute dates (founded in 2008) over content with relative dates (founded 15 years ago) for exactly this reason. Audit your content for relative time references and replace them with absolute dates. Every "currently", "recently", "this year", and "last year" is a potential future inconsistency.