Engine optimization / Tier 3
AI domination
Answer Engine Optimization, generative engine work, LLM citation tuning, and Knowledge Graph entity submission. The tier that gets a federation cited inside ChatGPT, Perplexity, Claude, and Google AI Overviews, not just ranked on a results page.
What ships
What is in AI domination
- Answer Engine Optimization: answer capsules on every key page
- FAQPage schema with concise answers tuned for snippets
- llms.txt and llms-full.txt published per the llmstxt.org spec
- brand.json, aeo.json, and keywords.json, machine readable identity files
- Speakable schema for voice friendly content
- Citation density tuning per Princeton GEO research
- Knowledge Graph entity submission where eligible
- Wikidata Q ID work where notability supports it
Why AI domination matters
A growing share of research now happens inside ChatGPT, Perplexity, Claude, and Google AI Overviews instead of a classic search results page. Those tools answer directly instead of handing back ten blue links, so being present in the answer matters more than being present in the list. Sites that show up in those engines get cited. Sites that do not are invisible to that share of users, no matter how well the same page ranks on a traditional results page. Tier 3 is the work that earns the citation.
The work splits across two surfaces: structured data the engines can read (FAQPage schema, speakable markup, llms.txt and llms-full.txt) and content the engines can extract and quote cleanly (answer capsules, citation density tuned to how these models actually pull a source). Get either surface wrong and the engine either cannot parse the page or finds nothing quotable to lift from it. We ship both surfaces together, so neither half gets left undone.
Inside a federation, this is not a new skill, it is the same one pointed outward. A private retrieval layer only returns a clean answer when the source content underneath is structured for extraction. Tier 3 takes that same discipline, the same clean structure, and aims it at the public AI engines that now sit between a business and its next customer.
Reference sources: Princeton GEO paper, llmstxt.org spec, Wikidata.
Deliverables
What you get at the end
- Answer capsule deployed on every key page
- llms.txt and llms-full.txt published
- FAQPage and speakable schema validated
- Wikidata Q ID submission if eligible
- Citation tracking dashboard for ChatGPT, Perplexity, and Claude
Prerequisites
Tier 3 assumes Tier 1 Foundation is already in place, since the schema and content structure it needs both depend on it. We also strongly recommend shipping Tier 2 Search Visibility alongside Tier 3, since the content cluster work behind both tiers overlaps substantially, sharing the same keyword research and page structure. Doing the two together avoids redoing that analysis twice.
FAQ
Tier 3 questions
Will I show up in ChatGPT after deploy?
Sometimes within two to four weeks for established brands. New brands may take three to six months. Citations come and go as the underlying AI models update.
What is the difference between Tier 3 and Tier 2?
Tier 2 ranks pages on Google. Tier 3 surfaces business citations inside AI engines. Different surface, overlapping signals.
Can you guarantee a Wikidata Q ID?
No. Wikidata notability is determined by the community. We submit when notability is supported by outside press or other independent references. We do not fabricate notability.
Next tier
Tier 4: Entity and authority
With AI domination in place, Tier 4 covers entity consolidation and the authority signals that carry across every engine, human and machine.