Can I make an AI chatbot recommend my business?

You can publish accurate, accessible information about your business, but you cannot determine which business a chatbot will recommend. The question, conversation, available sources and product behaviour all affect the response. A technically sound website is an input, not a promise of inclusion.

Is being cited the same as being recommended?

No. A citation identifies a source used to support a response. A chatbot can cite a page to explain a limitation, compare alternatives or support a factual statement without recommending the business behind it.

Does allowing an AI search crawler also allow model training?

Not necessarily. Providers can use separate agents and controls for search and training. OpenAI distinguishes OAI-SearchBot from GPTBot. Check the relevant provider's current documentation rather than assuming one robots rule covers every purpose.

Do I need llms.txt to appear in AI chatbot responses?

There is no universal llms.txt requirement across AI chatbots. It is a proposal, not proof of retrieval or citation. Prioritise accessible pages, clear facts and useful structure, and check individual providers' documented requirements.

Will a website update immediately change what a chatbot says?

No. A model's learned information and a live search response are different paths. An updated page may become available for retrieval without changing the model's learned information, and a search-backed response may still use other sources. Verify the page first, then inspect responses without treating a single answer as a stable result.

Be seen in AI chatbots

How a website gets seen in AI chatbots

A website becomes a useful source for AI chatbots when it clearly states what a business does, who it helps and what evidence supports its claims. The aim is not to write instructions for a chatbot to promote you. It is to publish facts that can be found, understood and checked when a conversation calls for them.

From direct answers to synthesis and recommendation

A person may ask a chatbot to explain a topic, compare approaches or suggest a supplier for a particular situation. The response can combine information from several sources rather than repeat one page. Your website needs to explain not just what you offer, but where it fits — and where it does not.

The three discovery surfaces overlap. ChatGPT, Gemini and other conversational products can offer search-backed answers as well as responses without live web search. These are useful distinctions between behaviours, not rigid categories of products:

Behaviour	What the person receives	What your site needs to provide
Search	Links to pages relevant to a query	Findable, readable pages with a clear topic
Direct answer	An answer, sometimes with supporting sources	A self-contained explanation backed by facts
Conversational synthesis	An explanation, comparison or suggestion shaped by the conversation	Clear identity, suitable use cases, limits and evidence

The foundations covered in search engines and answer engines still matter. The additional question is whether a source explains enough for someone to judge its relevance to a specific need.

How AI chatbots get information about a business

A chatbot can draw on information learned during model training, sources retrieved during the conversation, and material supplied by the user. These paths are different. A response without live retrieval may not reflect your latest website changes; a search-backed response may consult current pages, but it need not retrieve yours.

Training shapes the model before the conversation. Publishing a page is not a request to update that model, and you cannot schedule when a fact will be learned.
Live search or retrieval brings source material into a particular response. Access, relevance and the product's retrieval process affect which pages are available.
User-supplied context can include a URL, document or copied text. Clear statements remain useful when a reader supplies only part of a page.

1. Make it clear who you are and what you do

Your website is the source you control about your business. It should state your name, offering, audience and service area in plain language. Other sources can support or challenge those statements; calling the website a source of truth does not make unsupported claims true.

Use the same business and product names across the home page, service pages and contact information. Explain how a trading name relates to a legal name where that distinction matters.
Describe the offering with the terms customers use, not only internal names or slogans.
State who the offering is for, where it is available and which needs it does not cover.
Use structured data appropriate to the page, such as Organization or Product, only for facts supported by the visible content.

A vague promise gives neither a buyer nor a chatbot much to work with. A precise description of the problem you solve provides context for the rest of the site.

2. Explain the problem, solution and result

A service page should help a reader decide whether the service fits. That means explaining the situation, the work involved and the result that can actually be supported — not a list of adjectives.

The problem: describe the customer's situation in recognisable terms.
The solution: explain what you do, what inputs you need and what the customer receives.
The result: state the observable change or deliverable. Separate a documented outcome from an intended benefit.
The limits: explain exclusions, dependencies and cases where another approach is needed.
The questions: answer what a person needs to know before choosing, including practical constraints where relevant.

For example, a technical audit can deliver a page-by-page list of findings and fixes. That is a concrete deliverable. Saying the audit will make a chatbot recommend the company is an outcome the audit cannot establish.

3. Publish evidence, not claims you cannot support

Specific facts give a reader something to check. Publish relevant specifications, methods, qualifications and maintained documentation. Where you describe a customer result, give its context and use it only with permission. If the evidence is missing, narrow the claim or leave it out.

Name the author or responsible organisation where relevant, and make the basis of the explanation clear.
Date time-sensitive information and update it when the underlying facts change. Changing a date alone is not a substantive update.
Link factual claims to the supporting source when it helps a reader verify them.
Resolve contradictory descriptions, outdated offers and conflicting service details across your own pages.

Independent sources matter too. Your website cannot control how another publisher describes you, but it can give that publisher accurate information to check. Repeating an unsupported claim across more pages does not turn it into evidence.

4. Keep the source readable and accessible

A clear explanation is of little use to a web retrieval system if it cannot access the page. Keep important text available in the HTML, link important pages internally and use headings that describe the content beneath them. Tables and lists help where the information genuinely calls for comparison or steps.

Start by checking content availability in HTML and structured data. Markup should describe the visible page; it is not a substitute for the explanation and does not secure a citation.

Decide crawler access deliberately. OpenAI documents separate purposes for OAI-SearchBot, which supports search, and GPTBot, which relates to model training. Do not treat every AI-related crawler as the same permission. Follow each provider's current documentation before changing robots rules.

An llms.txt file is a proposed way to point language-model systems towards useful content, not a universal requirement. Do not make it the centre of the work or treat its presence as proof that a chatbot can find, cite or recommend you. The underlying pages still need to be accessible and useful.

5. Check the facts you control, then review the response

First check the website: can its important pages be reached, do they state the right facts, and do those facts agree? After a change, re-crawl to verify the published page. That confirms the input has changed, not that a model has learned it or a chatbot will use it.

You can also review real questions in a chatbot manually. Record the question, date, product and whether web search was used. Inspect any cited pages and note factual errors or missing context. One response is an observation, not a stable position: a different conversation or retrieval result can produce a different answer.

A referral visit can show that someone followed a link, but it does not account for responses without clicks. Google Search Console describes your site's Google Search performance; it is not a record of mentions across AI chatbots. Keep those measurements separate.

Where Be Seen Digitally fits

Be Seen Digitally checks the technical source: page access, metadata, headings, internal links, structured data and content availability in HTML. Its findings are deterministic checks on your own site data, with fix guides to explain the work. These checks identify problems in the source; they do not grade the truth of every claim on a page.

Start in Pages to review the source page by page. It does not track third-party AI mentions, measure a chatbot's preference for a business or promise placement in a response. The part you control is a maintained website that explains your business accurately enough for people and machines to use.

Provider documentation and further reading

Product behaviour and crawler policies can change. These primary sources explain the access and search distinctions behind this page:

OpenAI crawler documentation — the different purposes of its search, training and user-initiated agents.
ChatGPT search — web search and linked sources in ChatGPT responses.
Google's guidance on AI features and websites — existing search foundations, eligibility and the limits of special optimisation claims.

Common questions

Can I make an AI chatbot recommend my business?: You can publish accurate, accessible information about your business, but you cannot determine which business a chatbot will recommend. The question, conversation, available sources and product behaviour all affect the response. A technically sound website is an input, not a promise of inclusion.
Is being cited the same as being recommended?: No. A citation identifies a source used to support a response. A chatbot can cite a page to explain a limitation, compare alternatives or support a factual statement without recommending the business behind it.
Does allowing an AI search crawler also allow model training?: Not necessarily. Providers can use separate agents and controls for search and training. OpenAI distinguishes OAI-SearchBot from GPTBot. Check the relevant provider's current documentation rather than assuming one robots rule covers every purpose.
Do I need llms.txt to appear in AI chatbot responses?: There is no universal llms.txt requirement across AI chatbots. It is a proposal, not proof of retrieval or citation. Prioritise accessible pages, clear facts and useful structure, and check individual providers' documented requirements.
Will a website update immediately change what a chatbot says?: No. A model's learned information and a live search response are different paths. An updated page may become available for retrieval without changing the model's learned information, and a search-backed response may still use other sources. Verify the page first, then inspect responses without treating a single answer as a stable result.