Fundamentals

Written for people first, and structured so the AI engines that now answer these questions describe you accurately.

• How do AI models handle disambiguation for people and companies with common names?

  AI engines separate entities with common names through four infrastructure layers: a Wikipedia disambiguation page that explicitly lists distinct subjects, a unique Wikidata Q-ID that anchors each entity unambiguously, schema.org Person markup with sameAs links connecting owned pages to canonical identifiers, and contextual cues in the user’s query. When these layers are present, engines route correctly; when they are absent, conflation is the predictable result.

  Read the full answer →
• What industries face the most complex AI reputation challenges?

  Financial services, healthcare, regulated technology, and high-profile consumer brands face the most complex AI reputation conditions. In each, a distinct structural factor compounds the challenge: regulatory exposure that turns an AI mischaracterization into a compliance risk, a heavily weighted but heterogeneous source layer, or a volume of user-generated content that gives the AI engines material the brand cannot control.

  Read the full answer →
• What website content is most likely to be cited by AI models?

  The content AI engines cite shares a recognizable profile: it is fact-dense (concrete numbers, dates, named entities), structured for extraction (clear headings with short self-contained answers, lists and tables), carries schema markup, has named expert authorship, is recently updated, and cites authoritative third-party sources within the text. Engines extract what they can quote with confidence.

  Read the full answer →
• What happens when an AI chatbot gives wrong information about your company?

  When an AI engine says something wrong about your company, the fix starts at the source, not the engine. Identify which source the engine is anchored to, AIQ shows this directly for retrieval-based engines and infers it for training-baselined engines, then correct or counter that source (a Wikipedia Talk-page edit request, a press correction, or a structured-data fix). Once the source-level work is done, monitor propagation across the eight major AI engines AIQ tracks: retrieval-heavy engines update within days; training-baselined engines update on their retraining cycle.

  Read the full answer →
• Gemini gives a completely different description of my CEO than Google web results. What’s going on?

  Different engines, different source weights. Gemini sits on Google's infrastructure and queries the Knowledge Graph, which draws on Wikipedia and other authoritative sources, for entity facts, while a Google web-results page reflects the broader live index, including recent news and coverage those reference sources may not yet show. When the two disagree, the fix is engine-specific: identify which source is feeding each version and target that source.

  Read the full answer →