Improving visibility in LLM-powered search starts with a specific question: what can an AI assistant find and verify when someone looks for a business like yours? Publishing an article that repeatedly mentions the company name is not enough. There needs to be a clear connection between the requested service, the actual offer, demonstrated experience and sources that support the business's reputation.
For a customer asking “which company in Bucharest can develop a CRM for a service business?”, useful information includes projects, integrations, delivery stages, maintenance and the area served. A page saying only “we offer innovative solutions” does not answer those needs. This guide explains what to build and check without presenting assumptions about algorithms as established facts.
The key point: SEO, a backlink profile, frequently asked questions and evidence about the business provide a foundation for AI visibility. None of them independently guarantees a business recommendation or citation.
How a business appears in an AI answer
An LLM is a language model. Not every answer it generates involves an internet search. For current business information, we are particularly interested in assistants and features that search the web, consult sources and build an answer from them. This should not be confused with including information in a model's training dataset.
- Understanding the request: service, location, budget, audience and other conditions stated in the conversation.
- Discovering information: web pages and other sources available through the search system being used.
- Selecting and synthesising: relevant information is combined into an answer; some sources may receive citations.
- The user's decision: checking the business, visiting its website or contacting it directly.
GEO, meaning Generative Engine Optimization, and AEO, meaning Answer Engine Optimization, are terms used for this area of work. GEO research shows that content presentation can influence visibility under certain experimental conditions. It does not provide a formula that guarantees a place in a list of businesses or a growth percentage across every platform.
A mention, a citation and a conversion are different outcomes. A company name may appear without a link. A page may be cited without the business being recommended as a supplier. And a click is not automatically a quote request. That is why we assess these stages separately.
10 criteria worth examining when someone searches for a business
Platforms do not publish a universal table of selection weights. The order below is a practical implementation priority, not a purported ranking of ChatGPT, Copilot or Google AI algorithms. “Essential” means that an issue can prevent the offer from being discovered or understood; it does not represent a percentage of an AI score.
1. Technical access to information — essential priority
An excellent page cannot help with web discovery if it is inaccessible. We check server responses, indexing directives, the canonical URL and potential firewall blocks. Core information should be available as text, not only inside an image or a document that is difficult to consult.
OpenAI distinguishes OAI-SearchBot, used for search, from GPTBot, associated with training. Allowing one does not automatically require allowing the other. A robots.txt permission is also insufficient if the hosting environment subsequently rejects the request. Access enables participation; it does not guarantee selection.
What to implement: check important commercial pages, keep their text accessible and address blocks at their source. Do not disable protection across the entire website simply to accommodate a crawler.
2. Matching the service and customer intent — essential priority
The business needs to be associated with what is actually being requested. “Software development” is a broad category. “Custom CRM development with data migration and API integration” is much more informative for a specific question. This clarity primarily helps customers understand whether the service fits their needs.
What the page should contain: the problem being solved, included services, suitable customers, limitations, the process and the next step. Separate genuinely distinct services into substantial pages without creating copies for every variation of the same phrase.
Example: a CRM page can explain contact centralisation, interaction history, user permissions and integration with invoicing. These details must describe actual capabilities, not assumed features.
3. Business identity and local relevance — essential priority
The trading name, the company providing the service, contact details and service area should be easy to find and consistent. For a local search, a provider in another city may be unsuitable even if it has an excellent website. Google documents relevance, distance and prominence as local-result factors; the same weights cannot automatically be transferred to every LLM.
What to check: the contact page, company information, public profiles and location details. Use Google Business Profile and Bing Places when the business is eligible, with genuine addresses and opening hours.
Do not invent offices or confuse “we serve customers in Bucharest” with “we have an office in Bucharest”. For remote services, explain availability and how the collaboration works.
4. Experience and verifiable evidence — high priority
A list of superlatives says little about the ability to deliver. More useful evidence for assessing a supplier includes explained projects, identified specialists, methodology and results presented with their timeframe, context and measurement limitations. These are credibility recommendations, not a way to obtain a public “E-E-A-T score”.
What to publish: a team page with genuine roles and experience, case studies describing the initial problem and delivered solution, verification processes and collaboration terms. Where confidentiality restrictions apply, describe functionality and your contribution without exposing customer data.
A solid case study can demonstrate an editorial workflow, role-based permissions or a complex integration. It does not need invented figures or promises about outcomes that cannot be substantiated.
5. The backlink profile and independent references — high priority
The backlink profile matters to the foundation of AI visibility. Google confirms the use of link-analysis systems in traditional search, while Microsoft includes backlinks among the important elements for discovery in AI Search. However, there is no published universal rule assigning each link a fixed value in an LLM's recommendations.
Examine the relevance of the source, the context in which the business is mentioned and the credibility of the article. An editorial reference explaining a real capability differs from a link mechanically inserted into an unrelated list. Unlinked mentions can provide context, but they are not equivalent to backlinks.
Useful strategies: projects featured by partners, original resources worth citing, relevant interviews and documented professional participation. Commercial metrics such as DA or DR are not “AI trust scores”.
The safety boundary: paid advertorials should be handled transparently, and commercial links appropriately qualified, for example with
rel="sponsored"ornofollow. Avoid buying links to manipulate rankings, repeated optimised anchors and mass distribution through footers.
6. Coverage of relevant traditional searches — high priority
Appearing for more relevant keywords in traditional search results can support better citation opportunities in AI answers. The explanation is coverage of more needs and discovery opportunities, not an automatic bonus for the number of phrases a domain ranks for.
Google explains that its AI features can expand a question into related searches, a process called query fan-out. A business with useful pages covering the service, implementation and specific problems may be encountered in more of those searches. This supports good topical coverage; it does not prove that first place in Google produces first place in ChatGPT.
Example structure: the main CRM service page, a guide to data migration, explanations of application integration and a case study about a sales process. Each page addresses a distinct need and naturally points to the appropriate service. Do not publish dozens of near-identical pages targeting synonyms or neighbourhoods.
7. Genuine FAQs and understandable answers — high priority
Frequently asked questions on pages matter because they make customer selection criteria explicit. Microsoft recommends clear structures and FAQs to help information interpretation in AI Search. The value comes from the visible question and answer, not merely from a box labelled “FAQ”.
Useful service-page questions: What does the offer include? What type of company is it suitable for? How is the cost calculated? What does the customer need to prepare? How does migration work? Who provides support after launch?
Illustrative example: for “Can we keep the data from our current system?”, a useful answer explains export assessment, field mapping, data validation and import testing. “Yes, we offer complete solutions” does not clarify the process.
Google stopped displaying FAQ rich results from 7 May 2026. We therefore do not recommend FAQPage as a promise of extra space in Google or AI citations. Keep FAQs for their actual usefulness and avoid repetitive answers written solely for keywords.
8. Reputation and consistency of external information — high priority
What a business says about itself should be checked against what a customer can find elsewhere. Authentic reviews, professional references and current information help with evaluating the company. In Google's local results, links and reviews contribute to prominence. Their precise importance and availability may differ for each AI assistant.
What to do: request genuine feedback after delivery, respond professionally, correct outdated profiles and document an affiliation or certification only when it is valid. Do not invent testimonials or present sponsored articles as independent validation.
More mentions do not automatically mean a better reputation. Incorrect information repeated across ten platforms remains incorrect. Consistency should start with truth, not with multiplying a claim.
9. Freshness and accuracy — high priority when information changes
Outdated opening hours, a discontinued service or a price without conditions can make a recommendation unsuitable for a customer. Prioritise updates to commercially important details: availability, locations, the process, prices and terms. Do not change an article's date simply to make it appear new.
What to establish: clear ownership of every important page and a review schedule appropriate to how frequently the information changes. When updating an offer, also check pages and profiles that repeat it.
Dates and methodology are necessary when publishing results. “We reduced working time” is more useful when the process, timeframe and measurement are explained. Do not present an isolated observation as a guaranteed outcome for the next customer.
10. Page structure, structured data and user experience — supporting priority
A precise title, descriptive headings and well-grouped information make an offer easier to consult. Structured data can describe the company, author or article, but it must match the visible content. There is no special markup that guarantees inclusion in AI answers.
What to check: contrast, mobile navigation, image dimensions, form access and pages explaining the offer. Keep essential details in HTML and use real photographs in the appropriate context. An office photograph is not evidence of a project or a partnership with an AI platform.
Do not burden the website with widgets, scripts or extensions merely to tick a GEO box. Google states that it does not use llms.txt files for AI Search. Neither such a file nor a PageSpeed score of 100 guarantees citations.
How to measure whether the strategy works
A single question to a chatbot is not an audit. Use a stable set of commercial questions, checked periodically under recorded conditions. Results can vary with the platform, conversation context, location, web-search availability and the time of the check. Do not combine answers without search with answers citing current sources.
- Visibility: is the business mentioned for relevant questions, or only when its name is explicitly requested?
- Citations: which URLs are used, for what information, and how accurately is the offer described?
- Traffic: which visits can be attributed to AI sources in analytics, within the limitations of the available data?
- Commercial outcomes: enquiries, calls or sales, rather than clicks and crawler requests alone.
In 2026, Google and Bing tools provide reporting for their own AI surfaces, subject to each product's limitations. Bing AI Performance tracks citations from Microsoft Copilot, Bing summaries and selected partners; it is not a complete measurement of every LLM. Supplement these data with manual checks and analytics. A session reported as “direct” does not, by itself, establish an AI visit.
Example test: the same business can be checked for “CRM development in Bucharest”, “supplier for CRM migration” and “custom application integrated with invoicing”. Record the date, platform, exact question, cited source and factual accuracy. An answer without the business is still a useful observation; do not exclude it from the report.
A practical plan without tactics that sacrifice SEO
- Remove obstacles: access, indexability, hidden essential information and unclear contact details.
- Clarify the offer: distinct service pages, the process, suitable customers and genuine commercial questions.
- Build evidence: the team, case studies, original resources and verifiable information.
- Strengthen reputation: authentic reviews and relevant references, without manipulative link networks.
- Expand coverage and measure: useful content for distinct needs, citation monitoring and conversion checks.
This plan can be organised into an initial work cycle, but it should not be mistaken for a guaranteed results timeframe. A company with incomplete information has different priorities from one with a well-indexed website and documented projects. Choose interventions based on the observed problem, not on a universal list of tricks.
Frequently asked questions about LLM visibility
Should we abandon SEO and focus only on GEO?
No. For AI systems using web search, page discovery and information quality remain important. Build on the existing SEO foundation and monitor visibility, citations and commercial enquiries separately. There is no reason to remove useful pages or weaken website performance for a new label.
Does a strong backlink profile guarantee a business recommendation?
No. It can support discovery and reputation, but the offer must match the question, and its information must be accessible and credible. A website with many links but no explanation of the requested service may not provide the information needed for a useful answer.
Why do several relevant searches help more than a single keyword?
Customers ask different questions, and systems may search for information about several aspects of the same problem. Useful coverage of those needs creates more discovery opportunities. This is not an invitation to duplicate pages or a rule that a certain number of keywords automatically produces citations.
Is adding FAQs to every page enough?
No. FAQs should address genuine needs and complement the service information. Ten repetitive questions cannot fix an unclear offer, an identity that is hard to verify or a blocked page. Start with the questions customers actually ask before commissioning the service.
Is organic AI visibility the same as ChatGPT Ads?
No. Paid ads and mentions or citations within answers are different mechanisms. An advertising campaign does not guarantee organic recommendations. They can be part of the same commercial strategy, but budgets, measurement and promises should remain distinct.
Connect AI visibility to a coherent promotion strategy
LLM visibility should not be isolated from SEO, reputation, content and conversions. A sound approach makes the business easier to find, verify and choose, regardless of the channel through which the customer arrives.
Our online promotion for businesses page explains how these areas connect in a plan tailored to the objective and budget. Start with missing information and evidence, preserve what already works and measure results without promising guaranteed positions.
Researched on 11 October 2026 using OpenAI crawler documentation, Google Search Central, Google Business Profile, Microsoft Bing and GEO research. Practical recommendations are implementation interpretations, not published algorithm weights. The photographs illustrate collaboration and communication planning, not AI results or projects involving the people pictured.
