AI SEO / GEO
ChatGPT, Perplexity, Google, Siri. Every surface where your buyers look for recommendations. Plate Lunch Collective is an AI search optimization and Generative Engine Optimization agency. We build the presence that gets your brand mentioned, discovered, and remembered.
Sources: Semrush, How AI Tools Influence the Modern Buyer Journey, March 2026. McKinsey & Company, An update on US consumer sentiment: Embracing AI-supported shopping, March 2026. OpenAI announcement via Search Engine Land, February 2026.
How AI searches work
A buyer asks one question. The AI already knows their history, their constraints, their preferences. It breaks that question into the five searches it actually needs to answer, finds the best source for each one, and assembles a single recommendation. The brands that show up are the ones structured to be found on those searches. Not the original question. The searches underneath it.
SEO drives rankings, traffic, and the organic foundation your business depends on. That has not changed. What has changed is that AI search is no longer an extension of those rankings. As recently as 2024, roughly 70% of the sources AI platforms cited also appeared in Google's top ten results. By 2026, that overlap has collapsed to under 20%. AI platforms now operate with their own citation logic, their own domain preferences, and their own signals for deciding who to recommend. They are a separate channel.
That channel does not return the same answer twice. Every response is shaped by the buyer's specific question, their conversation history, and which content the system finds in the moment. There is no position one. There is no static ranking to defend. There is a recommendation to earn, every time someone asks.
Most brands have the expertise to earn that recommendation. What they are missing is the presence that lets AI systems recognize it, trust it, and attribute it to them specifically: not to the category, not to a competitor, not to no one.
How We Work
Every piece of work you invest in AI search presence stays. An entity signal corrected in June is still corrected in December. A passage that earns a citation this quarter is still retrievable next year. An authoritative mention that shifts what a model believes about your brand persists across every model update. SEO rankings require constant defense. AI search presence accumulates.
Traditional SEO is core to this work, not a precursor to it. Technical health, crawlability, site architecture, and organic ranking feed the indexes that AI platforms retrieve from. 87% of ChatGPT citations come from URLs that already rank in search results. If your pages do not rank, they do not get retrieved. That foundation has not changed and we do not skip it.
What has changed is what the system requires beyond ranking. AI platforms do not just pull from your pages. They form beliefs about your brand from training data, entity records, and the consistency of how you are described across every source they can see. Most SEO work addresses the retrieval layer, the content the model looks up in the moment. It does not address the parametric layer, what the model already believes about you before it searches for anything. A brand with strong rankings and a weak or inaccurate parametric presence gets retrieved and passed over. The model finds your content and recommends someone it understands better.
The methodology is built around three dimensions that together measure whether AI systems recognize your brand, trust your content, and recommend you by name. Parametric Presence measures what models already believe about your brand from their training data. LLM Influence Score measures how often and how prominently AI retrieves and cites your content when it searches. Unprompted Recommendation Rate measures whether models recommend you by name without being asked and without searching the web. The work that moves those dimensions spans entity SEO, citation-ready content, context mapping, and retrieval structure across every surface your buyers use.
The brands building this presence now are the ones AI will recommend for the next several years. Parametric knowledge does not update overnight. The beliefs a model forms about your brand during this window persist across training cycles, shaping recommendations long after the initial investment. Your competitors who start now will occupy the position in the model's understanding that becomes harder and more expensive to displace with every update. The gap between brands who have built AI search presence and brands who have not is widening every quarter.
AI search has two layers that work together. The retrieval layer is what most of the industry talks about: crawlers, chunks, embeddings, citation architecture. The parametric layer is what the model already knows about a brand before it retrieves anything, shaped by training data, third-party coverage, knowledge graph presence, entity signals accumulated over years.
Retrieval optimization addresses the first. It does not address the second. A brand that has strong retrieval presence but no parametric presence gets found when the model looks things up, and ignored when it does not. For well-established topics and well-known brands, the model often answers from memory without retrieving anything.
Plate Lunch Collective works both layers. The work on the page in front of you, and the work on every asset a model ingests elsewhere. Neither half is optional.
Every platform decomposes queries differently. Retrieval weighting, citation behavior, freshness preference, and parametric balance all vary. We build for the mechanics each one actually uses.
ChatGPT
Perplexity
Claude
Gemini
Meta AI
Copilot
DeepSeek
GrokCase Study
Their ideal buyers were out there. Canadians musing about warm weather in the dead of winter. UK travelers looking for white sand beaches and quiet enjoyment. US visitors who did not realize a direct flight from the east coast was shorter than going to Hawaii. The mix of buyers ran from pure vacationers to investors who wanted a place they could also use. None of them were typing “best fractional ownership Caribbean” into a search bar. They were having layered, personal conversations with AI assistants. About budget. About timing. About climate. About what they actually wanted a trip to feel like.
That is the retrieval reality most GEO work ignores. Query fan-out is shaped by session context, stated constraints, and personal framing. A synthetic prompt test against “luxury Caribbean resort” tells you almost nothing about whether the property gets named in the conversation a real buyer is actually having. We scoped the work around the decomposition patterns those real conversations produce, not around commercial-intent keyword lists.
The outcome showed up where AI-influenced buying shows up. Branded search volume climbed. Direct site traffic climbed. Direct bookings climbed. On-site restaurant covers climbed. The conversation that recommended the property happened somewhere analytics cannot see. The arrivals it produced are right there in GA4. They are still with us.
Hospitality
Properties with layered offerings get flattened by AI platforms into a single category frame, losing buyers whose intent matches a layer the model is not surfacing.
Professional Services
Expertise lives in practitioner heads and unpublished client work, leaving the retrieval index with almost nothing to cite when buyers ask AI platforms for recommendations.
Local Business
Local intent queries trigger retrieval more often than any other commercial category, but the citation candidates are dominated by directory aggregators unless the business has direct entity signals.
E-Commerce
Product-level queries decompose into many sub-questions, and brands without passage-level structure on product pages retrieve for none of them despite strong category presence.
SaaS
Category vocabulary hardens slowly in training data, which means newer SaaS products must work harder to establish the entity signals that feed parametric recognition.
On Island
Hawaii-specific intent queries return heavily genericized mainland-equivalent results unless local entity signals are explicit and structured.
Tourism
Destination queries decompose across accommodations, activities, timing, and logistics. Properties that are not mapped to every sub-query retrieve inconsistently across the buyer journey.
Skincare
Ingredient-level and formulation-level queries decompose heavily, and brands without passage-level structure retrieve for none of them despite strong topical authority.
Agritourism
A hybrid category that retrieval systems resolve inconsistently, with content often pulled toward either agriculture or tourism depending on the query, losing the specific intent in either direction.
Agribusiness
Category vocabulary is inconsistent across sources, which means entity resolution fails at the embedding stage before retrieval even runs.
Aviation
Legacy institutional authority does not automatically translate into retrieval-ready digital presence, leaving decades of expertise invisible to models that cannot find structured signal to cite.
Creators
Retrieval layer presence is the only commercial asset creators actually own, because platform distribution is rented and platform ranking changes weekly.
Tell us about your business. We will come back within 24 hours with a plain-language read on where you stand in AI search: what is working, what is missing, and what the highest-leverage fix is.
From the Field
