Honestly: no AI company publishes the exact rules it uses to decide which businesses get mentioned in an answer, so nobody outside those companies knows the full formula. What is documented is the general process — the system gathers information from sources it can access, judges which sources are relevant to the question, and builds an answer from them — and businesses that are clearly and consistently described in those sources are the ones available to be mentioned.
The general process, in plain language
When you ask an AI assistant a question that involves businesses — “Who repairs furnaces in Rochester?” or “What’s the difference between these two software companies?” — two things can happen. The system may answer from patterns in the data it was trained on, or it may search the web at that moment and read pages before answering. Many current systems do a mixture of both.
Either way, the answer is built from published information: websites, directories, review platforms, news articles, and reference sources. Businesses are not mentioned because they applied, registered, or paid. They are mentioned because information about them exists in the material the system draws on, in a form it can use. This retrieval-and-selection process is explored in depth in our guide to how AI citations work.
The factors that appear to matter
Platforms describe their systems only in general terms, so the following separates what is documented from what is a reasonable inference based on that documentation.
Documented: AI systems retrieve information relevant to the question asked. A page that directly addresses the subject of the question — the service, the place, the problem — is retrievable for it; a page that never mentions the subject is not. Systems also draw on many sources, not one: the same business described on its own site, in directories, and in reviews presents more accessible material than a business described in only one place.
Documented: These systems have no public submission channel for mentions, and no published “mention score” that businesses can check or improve directly. OpenAI, Google, and other providers do not publish exactly how they weight one source against another.
Reasonable inference: Consistency appears to matter. When sources agree on a business’s name, location, services, and facts, the system can state those facts with fewer contradictions. When sources conflict — different addresses, different founding years, different descriptions of what the business does — the business becomes harder to describe, and a system summarizing cautiously may describe a competitor instead simply because the competitor’s record is cleaner.
Reasonable inference: Specificity appears to matter. A page that says “we handle emergency furnace repair across Monroe County, usually within 24 hours” gives a system a concrete fact it can match to a concrete question. A page that says “quality service you can trust” gives it nothing to match. Vague marketing language is not information.
A concrete example
Two heating companies serve the same county. Company A has a polished homepage, but its services are described only in slogans, its service area is never stated, and its listings around the web show two different phone numbers and an address it moved from three years ago. Company B’s website is plainer, but it names each service, states the towns it covers, and says what an emergency callout involves. Its listings match its website, and it appears in a local news article about storm repairs.
Asked “Who does emergency furnace repair in this county?”, an AI system has far more usable, agreeing material about Company B. That is not because Company B gamed anything, and it does not guarantee Company B is mentioned every time — answers vary, and the system’s internal weighting is not public. It is because Company B is describable, and Company A largely is not. Multiply that difference across every question customers ask, and it becomes the practical meaning of AI visibility.
What this means for your business
You cannot control the decision, and any service claiming it can is misrepresenting how these systems work. What you own is the input: the published record the decision is made from. The useful questions to ask about your own business are therefore factual ones. If an AI system went looking today, what would it find? Would the sources agree with each other? Would they state, specifically, what you do and where you do it? Or would it find slogans, contradictions, and gaps?
Those questions can be answered with evidence rather than guesswork. An AI Visibility Audit documents what AI systems currently say about your business and what published sources that output appears to rest on — so you can see the record as the systems see it, and decide which parts of it are worth fixing.
Related questions
- How do I get my business to show up in ChatGPT answers?
- What is an AI citation?
- Why does AI describe my business incorrectly?
- Can I pay to be mentioned by AI?
Published: November 1, 2026
Last reviewed: November 1, 2026