Long-form evidence companion to our framework. If you want the short pitch, start there. If you want the research that backs it, this is the page.
Traditional SEO asks whether your pages can be found, indexed, ranked, and clicked.
AI search changes the question.
AI is becoming a layer between your buyers and your business. It does not merely help users find pages. It interprets the market, compresses options, explains differences, and recommends next steps.
When someone asks ChatGPT, Perplexity, Gemini, Claude, or Google AI Overviews about a business category, the answer may not look like a list of search results. It may look like a recommendation, a comparison, a summary, or a confident explanation of who does what.
That means the new risk is not only invisibility.
The new risk is misrepresentation.
AI may find your business but describe it incorrectly. It may cite one of your pages but recommend a competitor. It may flatten your strongest point of view into generic category language. It may use old information, unsupported claims, or a source that does not actually say what the AI says it says.
This is why AI Business Accuracy matters.
The practical question is no longer just:
Do we rank?
It is:
Does AI find us, understand us, support what it says, preserve our context, and recommend us in right-fit situations?
Traditional SEO still matters, but it is not the whole problem
Google describes traditional Search as a three-stage process: crawling, indexing, and serving search results. Google discovers pages, analyzes their content, stores information in its index, and returns results it believes are relevant to the user's query. That world gives businesses familiar levers: crawlability, metadata, structured data, technical site health, rankings, snippets, links, and Search Console measurement. (Google Search Central: How Search Works)
Those foundations still matter. Google says its generative AI features in Search are rooted in core Search ranking and quality systems, and that SEO best practices remain relevant. Google also names retrieval-augmented generation and query fan-out as part of how generative AI search experiences retrieve and use information. (Google Search Central: Optimizing for Generative AI Features)
But "still relevant" does not mean "unchanged."
AI search does not only return ranked pages. It forms answers.
That answer-forming process introduces new failure points that traditional SEO was not designed to measure.
AI retrieves fragments, not just pages
Many AI systems use retrieval-augmented generation, or RAG, to bring outside information into an answer. A major survey of RAG research explains that this approach helps large language models address problems like hallucination, outdated knowledge, and opaque reasoning by incorporating external databases and retrieved information. (Gao et al., Retrieval-Augmented Generation for Large Language Models: A Survey)
That sounds like a good thing, and often it is.
But it also changes what visibility means.
In classic SEO, a page can rank as a whole. In AI search, a system may retrieve a passage, paragraph, excerpt, or chunk. Surfer describes this plainly: AI systems do not just rank pages; they select passages, reuse evidence, and cite what helps them answer confidently. Surfer also reports that 67.82% of cited sources in Google AI Overviews do not rank in Google's top 10 for the same query. (Surfer: How LLM Citations Work)
So a business can have a strong page that still fails in AI if the important claim is buried, vague, too dependent on surrounding context, or hard to extract.
The page may be good.
The passage may not be reusable.
AI can lose context when it breaks content apart
Anthropic's work on Contextual Retrieval makes this problem concrete. Anthropic explains that traditional RAG systems often split documents into small chunks, and those chunks can lose the surrounding context that makes them meaningful. In their example, a chunk saying "The company's revenue grew by 3%" is not useful if the chunk does not identify which company or time period it refers to. (Anthropic: Introducing Contextual Retrieval)
Anthropic's proposed fix was to prepend chunk-specific explanatory context before retrieval. Their experiments found that Contextual Embeddings reduced retrieval failure rates, and combining Contextual Embeddings with Contextual BM25 reduced the top-20 retrieval failure rate by 49%; adding reranking improved results further. (Anthropic: Introducing Contextual Retrieval)
For a business, the takeaway is straightforward:
AI does not only need your claims.
It needs your claims with enough context to know when they apply.
That is why Marty Koepke measures context preservation and recommendation fit. A claim like "we help service businesses improve AI visibility" is not the same as "we help founder-led service businesses understand whether AI systems can find, represent, support, and recommend them in right-fit situations." The second version gives AI more of the conditions that keep the meaning intact.
Different AI systems trust different source ecosystems
Traditional SEO often centers Google. AI visibility is more fragmented.
Profound analyzed citation patterns across ChatGPT, Google AI Overviews, and Perplexity from August 2024 to June 2025. Their analysis found distinct sourcing patterns: ChatGPT's top sources leaned heavily toward Wikipedia, Google AI Overviews showed a more distributed mix including Reddit, YouTube, Quora, and LinkedIn, and Perplexity's top-source set was strongly concentrated around Reddit. (Profound: AI Platform Citation Patterns)
Search Engine Land reported similar platform-source complexity from a study of 30 million sources across ChatGPT, Google AI Mode, Gemini, Perplexity, and AI Overviews. The article highlights Reddit, YouTube, LinkedIn, and Wikipedia as major cited surfaces and notes that these sources shape AI responses in different ways. (Search Engine Land: AI Search Engines Cite Reddit, YouTube, and LinkedIn Most)
This means "being findable" is no longer only a website problem.
For factual questions, AI may prefer official pages, business listings, and structured information. For subjective questions, such as "who should I hire?" or "is this company reputable?", AI may lean more on reviews, forums, professional profiles, third-party mentions, community discussions, or industry publications.
The right source strategy depends on the question being asked.
That is why AI Business Accuracy separates readiness from measured outcomes. A business may have a good website and still be weak in the sources AI uses for comparison, trust, or recommendation questions.
AI citations can be wrong, weak, or misleading
AI search can sound authoritative even when it is wrong.
The Columbia Journalism Review's Tow Center tested eight generative search tools across 1,600 queries. It found that the chatbots collectively provided incorrect answers to more than 60% of queries. The study also found that many tools were bad at declining to answer when they could not answer accurately, and that premium chatbots sometimes gave more confidently incorrect answers than free versions. (Columbia Journalism Review / Tow Center: AI Search Has a Citation Problem)
This matters for business visibility because an AI answer is not automatically trustworthy just because it includes a citation.
A citation might be real but irrelevant.
It might mention the business but not support the claim.
It might point to an outdated page.
It might cite a third-party source that copied or summarized another source.
It might use a source to support one part of an answer while making a separate claim that the source does not support.
Traditional SEO analytics can tell you whether someone clicked. They usually cannot tell you whether AI accurately represented your business before the click ever happened.
The new work is accuracy infrastructure
This is the core reason the work matters.
If AI gets the business wrong, the business can lose trust before the buyer ever reaches the website.
If AI cannot find enough evidence, it may skip the business.
If AI finds evidence but strips out context, it may overstate, understate, or flatten the business's real difference.
If AI cites the business but recommends a competitor, the business may have contributed the answer without receiving the commercial benefit.
That is why Marty Koepke treats AI visibility as the doorway and AI Business Accuracy as the deeper framework.
The work is not just "AI SEO."
It is accuracy infrastructure for AI-mediated buying decisions.
Welcome to Daizie AI visibility!
Every small business owner I know wants the same thing. To bloom. To be seen, to be understood, to be recommended when the right customer asks the right question.
And the world I've just described — different engines, different sources, different playbooks, live partnership deals reshuffling the map every quarter — can leave a person a little dazed. Overwhelmed. Not sure where to plant the first seed.
That is the space Daizie lives in. The name is a small play on words: part daisy, the flower that turns toward the light, and part dazed, the way most business owners feel when they first look at all of this. And all of this in the new crazy era of AI. Daizie is built for businesses who want to grow, and hold confidence that their online strategy will thrive in the AI era.
Daizie is an AI visibility assessment for small businesses. Same research-backed rubric for every business, with recommendations calibrated to whether you're local, services, or product. It shows you exactly what AI already says about you, what it gets wrong, and the specific moves that help you bloom in the answers that matter — without having to become an SEO expert to get there.
If any of this hit close to home, that is where I would start.
— Marty
