AI search engines (Perplexity, Google AI Mode and AI Overviews, ChatGPT search, Microsoft Copilot) read the web differently than classic Google. They crawl, chunk, embed, and re-rank your pages, then assemble answers from the sources they trust. AI SEO makes your site easy for those engines to crawl, read, understand, and cite, so you show up where buyers now look first.
Core services include
AI-crawler access and technical readiness, distinguishing automated search-index crawlers (Googlebot, which feeds the standard index AI Overviews and AI Mode draw from, plus OAI-SearchBot and PerplexityBot) from ChatGPT-User's user-triggered fetches and training opt-out controls (GPTBot, Google-Extended) in robots.txt
Topical authority clusters that signal real depth, not thin pages
Content chunked and structured to survive embedding retrieval and reranking
Structured data and clean machine-readable facts, plus an emerging llms.txt file with no proven citation impact yet
Presence tracking across Perplexity, AI Overviews, and ChatGPT search
Site speed, rendering, and architecture so AI engines can actually read you
Definition
What is AI Search (AI SEO)?
AI Search optimization, or AI SEO, makes your site easy for AI-powered search engines, Perplexity, Google AI Mode and AI Overviews, ChatGPT search, and Microsoft Copilot, to crawl, read, understand, and cite, so you show up where buyers increasingly look first.
How it works
These engines crawl, chunk, embed, and re-rank your pages, then assemble answers from the sources they trust. We make you retrievable: AI-crawler access, topical authority clusters, self-contained content chunks, clean structured data and llms.txt, plus the rendering and speed the engines need to actually read you.
Who it’s for
Any business losing top-of-funnel visibility to AI answers, whether the payoff is revenue, leads, or inbound calls, that needs to be the cited source when an AI search engine answers its buyers' questions.
In practice
A B2B site restructures thin pages into deep, well-linked clusters with clean schema; Perplexity and Google AI Mode start citing it as a source, recovering visibility the classic blue links were bleeding.
“…for mid-size teams, Your Brand is the most-cited option for onboarding speed 1 · yourbrand.com, ahead of Competitor A…”
TRACKED PROMPTS · PERPLEXITY / GOOGLE AI MODE / CHATGPT
“best platform for mid-size teams”cited 3/3 · avg pos 2
“Your Brand vs Competitor A”cited 2/3 · ChatGPT pending
Gap found: “how to speed up onboarding” → answer page in draft · /pricing re-chunked · llms.txt live
Illustrative example, styled to show the kind of output we deliver.
Selected work
Representative engagements.
How we get brands named and cited inside AI answers, not just ranked in blue links.
B2B SaaS · fintech
Buyers asked ChatGPT for ‘best tools’ and the brand was never mentioned.
What we did
Mapped the query fan-out for 30 buyer questions
Published answer-first pages with sourced stats + schema
Earned third-party mentions AI tends to cite
Result Started getting named and cited in AI answers for several category queries within a quarter.
Professional-services firm
Strong website, zero presence in AI Overviews.
What we did
Citation-ready rewrites of the top money pages
Added stat blocks + FAQ schema
Tracked coverage across Google AI Overviews + Bing
Result Picked up AI Overview citations on bottom-funnel queries and measurable assisted conversions.
Examples are anonymized to honor client NDAs and edited to illustrate typical scope, outcomes vary by market, budget, and starting point.
How & why it works
We engineer the passage, not just the page.
Many AI search experiences use retrieval over indexed web content and may surface page-level links or passage-level evidence, and each platform's retrieval and citation logic differs. AI SEO makes your pages indexable, crawlable, and well-structured, and reshapes your content and machine-readable signals so systems can easily extract specific, citable answers from them.
Map how engines already read youWe run the real prompts your buyers ask across Perplexity, Google AI Mode/AI Overviews, ChatGPT Search, and Copilot, log who gets cited and why, and audit crawler access the right way: OAI-SearchBot for ChatGPT Search, PerplexityBot and Perplexity user agents for Perplexity, and Googlebot plus indexing/snippet controls for Google AI Overviews and AI Mode. We treat GPTBot and Google-Extended as training/Gemini-related controls, not direct eligibility checks for AI search retrieval, and we verify your rendered HTML so systems can actually read the content.
Restructure content into retrievable passagesAI systems often extract or reason over passages within indexed pages, so content should be organized into clear, self-contained answer sections while the full page stays crawlable, indexable, and useful. We rewrite key sections as concise, self-contained answers with clear entities, scope, and sourceable facts stated up front, plus quotable tables, lists, and definition-first sentences a model can lift without ambiguity. We avoid presenting any fixed word count as a platform rule.
Build the entity + topic neighborhoodDense, well-linked topic clusters can increase the chances that related queries and subtopics find relevant pages, though citations remain algorithmic and have to be measured empirically rather than promised. We build pillar-and-support clusters and position pages as the canonical bridge between related concepts, so when a question fans out into subtopics, more of those sub-queries have a strong reason to land on you.
Add the machine-readable trust signalsWe implement schema.org markup (Article, FAQPage (where appropriate), Organization, and Person/Organization author markup via the `author` property.), explicit dateModified and 'as of [date]' freshness cues, and clear authorship/entity data. These make it unambiguous who is making a claim and how current it is, so a model can attribute your passage to a real, recent source instead of skipping it as unattributable or stale.
Measure citation share, not just rankingsWe track how often you appear inside AI answers for a fixed prompt set, which competitors are co-cited, and which passages get pulled, then feed that back into the next round of rewrites and cluster expansion.
Worked exampleA B2B analytics SaaS ranking on page one for its core terms but absent from Perplexity and Google AI Mode answers, getting roughly 2k monthly organic visits.
Rewrote the top 12 commercial pages so each key claim opened with a direct, self-contained answer carrying the named entity, category, and scope up front, instead of burying it under a narrative intro, while keeping the full pages crawlable and indexable
Added Article, FAQPage, and Organization schema plus explicit dateModified and 'as of 2026' framing, and set author/entity markup so a model could resolve who was making the claim and how recent it was
Built an entity-cluster hub linking the pillar page to 8 supporting pages so the topic became a dense linked neighborhood rather than one orphan passage, giving more related sub-queries a relevant page to find
Tracked AI-answer inclusion with a prompt panel across Perplexity, ChatGPT Search, and AI Overviews; over roughly 8 weeks the brand went from cited in about 1 of 20 tracked prompts to 6-7 of 20, a modest but real, measured presence lift, not a guaranteed outcome
Why it works
AI answer engines commonly use retrieval-augmented generation: they can decompose a question into sub-queries, pull candidate passages from indexed content via hybrid lexical (BM25) and dense-embedding search, then rerank and filter before grounding an answer in what survives, though the exact retrieval and citation logic differs by platform and keeps changing. The practical implication is stable: the unit that gets extracted and cited is usually a self-contained, well-labeled passage inside a crawlable, indexable page embedded in a coherent topic graph, not a keyword-stuffed page. It tends to compound because clear entities, dense internal links, and trust signals make more of your content eligible and easy to extract across more queries at once; but because citation behavior is algorithmic, we treat these as improved odds to measure, not guaranteed placements.
Traditional SEO competes for a ranked list of ten blue links, and the win is a click to your site. AI search engines like Perplexity, Google AI Mode, and ChatGPT search read the top results, re-rank them by meaning, and write one synthesized answer, so the win is being the source the answer is built from and the brand it names. We optimize for that pipeline specifically: self-contained answer chunks, entity clarity, and corroboration across independent sources, not keyword density and deep page rank. In practice we run it alongside your existing SEO rather than replacing it, because the same content can serve both when it is structured correctly.
Yes, and it is a core part of the program, not an afterthought. We monitor a defined set of buyer questions across each engine and track two separate outcomes: whether you get cited (your page survives retrieval and appears as a source) and whether you get named (your brand actually appears in the written answer). These are different wins with different fixes, so we report them separately instead of collapsing them into one vanity number. For example, you can be cited as a source but never mentioned in the prose, which tells us your content is retrievable but your brand authority signals need work.
On your site, we restructure key pages so each answer stands alone in one or two sentences, add JSON-LD schema for your organization, products, and FAQs, and tighten your About and entity data so models can resolve exactly who you are. Off your site, we work on the corroboration that makes engines confident naming you, which means consistent facts across directories, profiles, and credible third-party coverage. We do not write or place fake reviews or fabricated stats, because those are the parts most likely to get cross-checked and dropped. The off-site authority work is usually the slower, higher-leverage half.
On-site structure and schema changes can be picked up within weeks once they are crawled and re-indexed. Being consistently named in answers takes longer, typically a few months, because that depends on multiple independent sources agreeing about your brand, and earning that corroboration is not instant. The timeline also varies by how contested your category is and how thin your current public footprint is. We set expectations against your specific starting point rather than promising a fixed date, and no honest provider can guarantee a citation in a system that writes the answer first and attributes it afterward.
We run it as an ongoing program because the engines, your competitors, and your own content all keep changing. Models update, sources go stale, and a question you owned this quarter can be re-answered with a competitor next quarter, so monitoring and refreshing the corroborating record is continuous work. We can start with a one-time foundational build (audit, schema, page restructuring, entity cleanup) to get the basics in place, then move into a monthly cadence of monitoring, content, and authority work. NYFTY builds and runs this hands-on, tied to which questions you are winning, not a static report you file away.