AI Brand Recognition

What AI Brand Recognition measures

AI Brand Recognition probes ChatGPT, Gemini, and Perplexity with the live-grounding feature OFF, then asks each one to describe your brand. If the model already knows you (because you're well-represented in its training data), it produces an accurate description. If it doesn't, it either declines, hallucinates a wrong description, or describes a different entity that shares your name.

This is the AURA dim most directly tied to training-data inclusion. It's the slowest dim to move (3–12 months for changes to surface in next-gen models) but the most durable competitive moat: brands AI already recognises don't need fresh content for AI to mention them.

How the brand-blind probe works

Each audit fires three probes — one to ChatGPT, one to Gemini, one to Perplexity — with grounding/web-search disabled. The prompts ask for a brand description without using the brand name as a leading word. The score combines whether each model recognised you, how accurately it described you, and whether it confused you with another entity.

• visibility_off Grounding off. The models answer from their training data alone — no live web fetch, no search results, no URL ingestion. This isolates pure recall from on-demand retrieval (Citation Depth measures the live-grounding side).
• person_search Brand-blind framing. Prompts are phrased so the brand name appears once at most, in a neutral context. We're not asking AI "tell me about <BRAND>" — we're asking it to describe a category and seeing whether your brand surfaces unprompted.
• fact_check Accuracy verification. The model's description is then compared against your actual brand site copy. Did it describe you correctly? Hallucinate a feature? Confuse you with another company that shares your name? Each branch produces a different penalty.
• history Supporting signals. Internally, AIVerdict also tracks Wikipedia presence, Common Crawl indexation, news mentions in the last 12 months, and review-platform listings. These don't directly compute the score, but they explain why a model knows (or doesn't know) you.

Common reasons for low AI Brand Recognition

• history_toggle_off Brand is younger than the training cutoff. If you launched in 2025 and the model's training data ends in 2024, AI literally cannot know you. The fix is patience: get covered in publications and resources that will be in the next training set.
• edit_off No Wikipedia article. Wikipedia is the single highest-signal source in most LLM training datasets. Brands without a Wikipedia article almost universally score low. Earn an article via genuine notability, not paid services.
• person_off Brand-name collision. If your brand name is also a common word, a city, or shares the name with a more famous entity, AI describes the wrong thing. Adding modifiers ("Acme Software" not "Acme") to your owned media helps disambiguate.
• block Blocked AI training crawlers. If your Training Access score is low because robots.txt blocks GPTBot/ClaudeBot/etc., training-data inclusion suffers. Fix Training Access first; AI Brand Recognition follows over months.

How to improve AI Brand Recognition

This dim moves slowly — expect 3–12 months for fixes to surface in next-gen models. Push these levers:

• menu_book Wikipedia article (highest leverage). Earn one via genuine notability: 3+ independent sources, neutral tone, secondary sourcing. Wikipedia gates AI knowledge more than any other single source.
• newspaper Press coverage that gets scraped. TechCrunch, The Verge, Wired, Forbes, industry trade press, Hacker News (HN front page), Reddit (top of r/technology, r/SaaS, etc) are all heavily represented in training data. Bylines in those publications move the needle.
• verified Get listed in canonical category lists. "Best X" articles, G2 / Capterra / Trustpilot, GitHub Awesome lists, ProductHunt's category leaders. AI synthesises these as authority sources for category questions.
• person Define your entity clearly on your About page. A schema.org Organization entry plus a clean factual paragraph (founded year, location, what you do, who runs it) helps AI ground your brand correctly when it does see your site.
• policy Allow training crawlers. Fix Training Access first. Without it, none of the above gets ingested.

Frequently asked questions

• What does "brand-blind grounding-off" mean? expand_more
  AI Brand Recognition probes ChatGPT, Gemini, and Perplexity with the grounding/web-search feature OFF — meaning they answer from their training data alone, not from a live web fetch. Brand-blind = the prompt never names your brand prominently. Together, this measures pure recall: does the model already know who you are without being shown anything?
• Why is my AI Brand Recognition score low even though I'm in Wikipedia? expand_more
  Wikipedia presence is a strong supporting signal but it's not the score itself. Possible reasons: your Wikipedia article was created after the model's training cutoff, your industry/niche doesn't surface in the probed prompts, or your brand name collides with another entity (a city, a person, a product) and AI describes the wrong one. Run the audit again in 6–12 months once next-gen models include the article.
• How do I improve AI Brand Recognition? expand_more
  It's the slowest dim to move because it depends on training-data inclusion. The levers, in priority order: (1) earn an accurate Wikipedia article via genuine notability, (2) earn coverage in publications that get heavily scraped (TechCrunch, The Verge, Wired, Reddit, Hacker News), (3) build a Common Crawl footprint by being well-indexed and allowed in robots.txt, (4) ensure your About page clearly defines your entity with schema.org Organization markup. Expect 3–12 months for changes to surface in next-gen models.
• How is this different from Citation Depth? expand_more
  AI Brand Recognition tests offline recall (grounding off) — what AI knows without looking. Citation Depth tests live grounding (grounding on) — whether AI cites your own pages or only third-party sources when it does look. They can disagree: a brand can have low Brand Recognition (AI doesn't know you) but high Citation Depth (when forced to look, AI cites your docs because they're well-structured).