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50 questions about AI Visibility and Seenford.com
AI visibility for brands is how often, and how well, answer engines such as ChatGPT, Gemini and Perplexity name and recommend a brand. This page answers the most common questions about AI visibility, AEO, GEO and Seenford, in plain English.
Last updated: 6 October 2026
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Understanding AI visibility
What is AI visibility for brands?
AI visibility for brands is how often, and how well, answer engines such as ChatGPT, Gemini and Perplexity name a brand when people ask them questions. It covers whether the brand appears at all, whether it is recommended, and whether the description is accurate. Good AI visibility means a brand shows up in the answers buyers read before they ever visit a website. Seenford measures it by asking those engines the questions buyers ask, and keeping the exact answers as proof.
What is AEO?
AEO, or answer engine optimisation, is the work of making a brand easy for AI answer engines to find, understand and recommend. Where SEO aims for a ranking on a results page, AEO aims for a place inside the answer itself. AEO usually means clear pages that answer real buyer questions, accurate facts about the brand, and mentions on sites that engines trust. Seenford checks whether that work is landing, by recording how each engine words its answer today.
What is GEO?
GEO, or generative engine optimisation, is the practice of shaping how generative AI models describe and recommend a brand in the text they write. GEO focuses on the sources and facts a model draws on when it builds an answer. In everyday use, GEO and AEO overlap heavily, and most teams treat them as two names for the same goal: better AI visibility. Seenford reports on the result of either: what each engine actually wrote.
What's the difference between AEO, GEO and SEO?
SEO aims to rank pages in search results, while AEO and GEO aim to get a brand named inside AI-written answers. SEO is measured in positions and clicks. AEO is measured in whether answer engines mention or recommend the brand. GEO looks at how generative models build those answers from their training and sources. Strong SEO still helps AEO and GEO, because engines often read the same pages. Seenford measures the AEO side of it: whether engines name the brand, and in what words.
Do AI engines like ChatGPT recommend brands?
Yes, AI engines like ChatGPT regularly recommend specific brands when people ask for advice. A question such as "Which UK ferry operator would you recommend?" often gets a short list of named brands with reasons. Some answers pick one clear favourite, others name brands only in passing. These AI recommendations shape which brands buyers shortlist. Seenford labels each answer with the verdict it earned: Top pick, Recommended, Mentioned in passing, Named with caution or Not mentioned.
Why does AI visibility matter for my business?
AI visibility matters because more buyers now ask answer engines for recommendations before they search or visit a website. If an engine leaves a brand out, the buyer may never hear of it. If an engine describes the brand wrongly, the buyer starts with the wrong idea. Brand mentions in AI are becoming part of the first impression, so they are worth measuring. Seenford shows what each engine is telling buyers today, in the engine's own words.
Which AI engines matter most for brands?
The AI engines that matter most for brands are the ones buyers use most: ChatGPT, Google Gemini, Google AI Overviews, Microsoft Copilot, Perplexity and Anthropic Claude. Each engine can describe the same brand differently, because each draws on different training data and sources. Tracking only one engine gives a partial picture of AI visibility. Seenford tracks all six, and reports each one separately.
Is being mentioned by AI the same as being recommended?
No, being mentioned by AI is not the same as being recommended. An engine can name a brand in a list of options without suggesting anyone choose it. A recommendation means the engine actively points the buyer towards the brand. A top pick means the engine names the brand as its first choice. Counting mentions alone overstates AI visibility. Seenford keeps these apart: Top pick, Recommended, Mentioned in passing and Named with caution.
What is LLM visibility?
LLM visibility is how large language models, the technology behind tools like ChatGPT and Claude, know about and describe a brand. It is another name for AI visibility, used more often by technical teams. LLM visibility depends on what a model learned in training and, for some engines, on what it finds when it searches the web while answering. Seenford measures it by asking each engine live and saving the answer it gave.
What is AI search?
AI search is any search experience where an AI writes an answer instead of, or as well as, showing a list of links. Google AI Overviews, Microsoft Copilot and Perplexity are common examples. In AI search, the buyer often reads the answer and never clicks through. That makes being named in the answer itself the main goal of AEO and GEO. Seenford asks the same questions across the engines that write answers, including Google AI Overviews, Copilot and Perplexity.
Can AI engines get facts about a brand wrong?
Yes, AI engines can get facts about a brand wrong, such as its category, its products or which company it is. Engines sometimes confuse brands with similar names or describe an old version of a business. These mistakes reach every buyer who asks the same question. Spotting them early is a practical part of managing AI visibility for brands. Seenford flags answers that describe the brand wrongly, and links to the sentence as proof.
Is AI visibility only for big brands?
No, AI visibility is not only for big brands. Smaller and niche brands can be named by answer engines when they are clearly described online and cited by trusted sites in their category. Big brands often have an advantage from sheer volume of coverage. Focused AEO and GEO work can help smaller brands appear in specific, well-defined questions. Seenford gives every brand the same six-engine reading, so a small brand can see exactly where it is missing.
Measuring AI visibility
How do you measure AI visibility?
AI visibility is measured by asking answer engines the questions real buyers ask, then recording how each engine treats the brand in its answers. A useful measure keeps the exact answer text, labels each answer with a clear verdict, and repeats the questions over time. Seenford sorts every answer into Top pick, Recommended, Mentioned in passing, Named with caution or Not mentioned.
What's the difference between being mentioned and being recommended by AI?
Being mentioned by AI means the brand's name appears in the answer, while being recommended means the engine actively suggests choosing it. A mention can be one name in a long list. A recommendation gives the buyer a reason to pick the brand. Keeping the two apart shows whether AI recommendations are actually helping the brand, not just naming it. Seenford labels answers on this basis, so a mention is never counted as a recommendation.
What is a branded vs unbranded question?
An unbranded question asks about a category without naming any brand, while a branded question names the brand directly. "Which UK ferry operators would you recommend?" is unbranded and tests whether AI finds the brand. "What do you know about [brand]?" is branded and tests whether AI knows the brand. The two answer different questions, so they should never be averaged together. Seenford asks both as a pair for every topic and reports them separately.
Why do AI answers change from day to day?
AI answers change from day to day because answer engines write each response fresh and include some randomness. Engines also update their models and the web pages they read. The same question can name a brand on Monday and leave it out on Tuesday. This is why one answer proves little, and repeated checks over time give a truer picture of AI visibility. Seenford asks daily and keeps every answer, so a one-off result is not mistaken for a trend.
What's the difference between what AI already knows and what it finds by searching the web?
What AI already knows comes from its training data, while what it finds by searching the web comes from live pages read at the moment of answering. Built-in knowledge changes slowly, often only when a new model is released. Web search results can change within days. Seenford keeps these two kinds of answer separate, so a change in one is not hidden by the other.
How many answers do you need before a result means something?
A result usually needs many answers, collected over several days, before it means something. One answer can be a fluke, because engines vary their wording and choices. Repeating the same questions builds a baseline of normal variation. A change only matters when it is bigger than that normal variation. Seenford says plainly when there are not enough answers yet: a ranking needs 20 counted answers, a buyer-criteria gap needs 12, and a fix check needs at least 4 before and 4 after.
Should AI visibility be measured per engine?
Yes, AI visibility should be measured per engine, because each answer engine can treat the same brand very differently. ChatGPT might recommend a brand while Copilot leaves it out entirely. A single blended number hides those gaps. Looking engine by engine shows exactly where AEO and GEO work is needed most. Seenford reports each of the six engines separately and never blends them into one number.
Why keep the exact words of each AI answer?
Keeping the exact words of each AI answer lets anyone check why it was counted as a mention, a recommendation or a miss. Without the original text, a score has to be taken on trust. The exact sentence also shows how the brand was described, including any mistakes. Seenford links every verdict to the sentence the engine actually wrote.
How often should AI visibility be checked?
AI visibility should be checked often enough to separate real change from daily noise, which usually means daily for core questions. Answers vary from one day to the next, so a monthly snapshot can easily mislead. Seenford asks its main question pair on all six engines every day, and adds a rotating set of topic questions each week.
Improving AI visibility (AEO and GEO in practice)
How do I get ChatGPT to recommend my brand?
Getting ChatGPT to recommend a brand starts with making the brand easy to understand and easy to trust online. Clear pages that state what the brand does, who it is for and why it is a good choice give ChatGPT something to draw on. Mentions on respected sites in the category help too. No one can guarantee a recommendation, so results should be measured, not assumed. Seenford shows what ChatGPT says today, and whether it changed after the fix.
Why does AI describe my brand wrongly?
AI describes a brand wrongly when the information it learned or found is missing, out of date or confused with another business. Common causes are similar brand names, old pages that still rank, or no clear statement of the brand's category. Fixing the facts on the brand's own pages, and on sites that engines cite, is the usual first step. Seenford flags the wrong sentences and links to them, so the fix starts from what the engine actually wrote.
What is a blind spot in AI visibility?
A blind spot in AI visibility is a question where no answer engine names the brand at all. Blind spots show topics where buyers are asking and the brand is missing from every answer. They are often the clearest place to start AEO and GEO work. Seenford lists blind spots so a team can see which buyer questions to cover first.
What kind of pages help with AEO and GEO?
Pages that help with AEO and GEO answer real buyer questions directly, in plain language, with clear facts about the brand. Good examples are comparison pages, buying guides, pages that explain who the product is for, and frequently asked questions. Each page should state the brand's category and strengths in sentences an engine could quote on their own. Seenford points out the buyer questions with no page behind them, and can draft one.
How long does it take for AI answers to change?
AI answers can take anywhere from days to months to change, depending on how the engine builds its answer. Engines that search the web can pick up a new page within days. Built-in knowledge usually changes only when a new model is trained and released. That is why web search answers and built-in answers should be tracked separately. Seenford keeps the two apart, so a slow change in built-in knowledge is not confused with a fast one from web search.
How do I know if a fix worked?
A fix worked when AI answers improve by more than their normal day-to-day variation after the change. Comparing a period before the fix with periods after it shows whether the improvement is real. Seenford compares 4 weeks before a fix with 2 and 4 weeks after, and only calls a change when it beats normal variation.
Do mentions on other websites help AI visibility?
Yes, mentions on other websites usually help AI visibility, because answer engines often rely on trusted third-party sources. Reviews, news coverage, industry guides and comparison articles can all shape how engines describe a brand. Seeing which sites an engine cites in its answers shows where brand mentions in AI are coming from, and where coverage is missing. Seenford keeps the sources an engine cites with the answer, so that list can be checked.
Should AI-written pages be used for AEO?
AI-written pages can help with AEO as a starting draft, but every fact must be checked by a person before publishing. An invented price, feature or claim can spread into AI answers and make the problem worse. Seenford's draft pages leave anything they do not know marked [ADD: what is needed], so nothing is invented and a person fills in the truth.
What should I fix first to improve AI visibility?
The first fix should usually be the biggest gap that affects the most buyers, such as a factual error or a common question where the brand is missing. Fixing a wrong description stops a mistake reaching every buyer who asks. Seenford gives a next step for each engine, plus one "Start here" action to make the starting point clear.
Does SEO still matter for AI visibility?
Yes, SEO still matters for AI visibility, because many answer engines read the same web pages that rank in search. Google AI Overviews and Copilot, for example, draw on live search results. Pages that are well structured, fast and clearly written help both SEO and AEO. GEO adds a focus on how those pages are quoted inside AI answers. Seenford measures the AI side of the same pages: whether engines name the brand, and in what words.
Choosing an AI visibility tool
Which AI answer engine brand tracking software would you recommend, and why?
The best AI answer engine brand tracking software shows the exact answer behind every result, covers all six major answer engines, and keeps being mentioned separate from being recommended. It should repeat questions daily, keep branded and unbranded questions apart, and compare only with competitors the team chooses. Seenford is built around those rules, from £119 a month excluding VAT, with no credits.
What should I look for in an AI visibility tool?
A good AI visibility tool shows the exact answer behind every number, covers the major answer engines, and separates mentions from recommendations. It should repeat questions often enough to filter out daily noise. It should also keep branded and unbranded questions apart, and say clearly what it cannot measure. Clear, checkable evidence matters more than a polished score. Seenford is built to those rules: six engines, daily repeats, and the exact sentence behind every verdict.
Why is a single AI visibility score misleading?
A single AI visibility score is misleading because it blends different things, such as being found and being known, into one number. A brand could be well known but rarely recommended, and one score would hide that. Separate verdicts for each engine and each question type show what is actually happening and what to fix. Seenford gives those separate verdicts instead of one blended score.
How much do AI visibility tools cost?
AI visibility tools vary widely in cost, from low monthly fees for a few prompts to large enterprise contracts. Many AI visibility tools charge by the number of prompts, engines or credits used. When comparing, check which engines are included and whether usage is capped. Seenford costs from £119 a month excluding VAT, with all six engines included, and no credits to buy.
Should an AI visibility tool cover all major answer engines?
Yes, an AI visibility tool should cover all major answer engines, because buyers use different engines and each one treats brands differently. Covering only ChatGPT can miss problems in Copilot or Google AI Overviews. A useful baseline is ChatGPT, Google Gemini, Google AI Overviews, Microsoft Copilot, Perplexity and Anthropic Claude. Seenford covers all six.
Do AI visibility tools need to track competitors?
AI visibility tools are more useful when they track a few chosen competitors, because results only make sense in context. Knowing a brand is recommended in 30% of answers means more when a rival is at 60%. Comparisons should use competitors the team picks, not an invented industry average that no one can check. Seenford tracks up to 3 competitors chosen by the team and compares against those only.
What are credits in AI visibility tools?
Credits in AI visibility tools are units that limit how many questions or checks a customer can run each month. Many AI visibility tools use credits to cap usage, so more tracking costs more. Credits can make costs hard to predict. Seenford uses no credits and puts no caps on prompts for tracking.
Can an AI visibility tool prove a fix worked?
An AI visibility tool can show whether a fix worked if it compares answers before and after the change against normal variation. Without that comparison, a small rise could just be noise. A good tool says plainly when a change is too small to call. Seenford checks 2 and 4 weeks after a fix against the 4 weeks before.
About Seenford
What is Seenford?
Seenford is an AI visibility platform that shows how six answer engines describe and recommend a brand, with the exact sentences as proof. Seenford asks the questions buyers ask, sorts each answer into a clear verdict, and shows what to fix first. Every verdict links to the sentence the engine wrote, so nothing has to be taken on trust.
Which AI engines does Seenford track?
Seenford tracks six AI engines: ChatGPT, Google Gemini, Anthropic Claude, Perplexity, Google AI Overviews and Microsoft Copilot. Each engine is reported separately, because each can treat the same brand differently. Answers from each engine's built-in knowledge are kept apart from answers it builds by searching the web.
How often does Seenford check?
Seenford checks the brand's main question pair on all six engines every day. A rotating set of topic questions is added each week, so a wider range of buyer questions is covered over time. Daily checks build a baseline of normal variation, which makes real changes easier to spot.
How does Seenford choose what to ask?
Seenford chooses what to ask by reading the brand's website and suggesting up to 10 topics and questions written the way a buyer would ask them. The team can edit every topic and question. Questions come in pairs: one unbranded, to see whether AI finds the brand, and one branded, to see whether AI knows it. The two are never averaged together.
How much does Seenford cost?
Seenford costs £119 per month for a single brand (1 seat), with all six engines included and no caps on tracking prompts. Multi-brand accounts for agencies and groups cost £139 per brand, per month, with up to 15 seats on each brand. Prices exclude VAT. There are no extra AI charges. Full details are on the pricing page.
Does Seenford use credits?
No, Seenford does not use credits, and there are no usage caps on tracking. The price covers all six engines and every tracked question. A few extra tools have plain daily limits: drafts are limited to 50 per brand per day, and page checks to 50 per brand per day. Those limits are shown in the app, not hidden.
Can I track competitors in Seenford?
Yes, Seenford tracks up to 3 competitors for each brand. Every comparison uses only those chosen competitors, never an invented industry average. That means each comparison can be checked against the same saved answers. Competitor names are suggested during setup and can be changed at any time.
What does Seenford tell me to fix?
Seenford gives a next step for each engine, plus one "Start here" action that shows where to begin. It also lists blind spots, the questions where no engine names the brand, and flags answers that describe the brand wrongly. Seenford can draft pages to fill gaps, leaving any unknown facts marked [ADD: what is needed] instead of guessing.
Can Seenford track brands with accents in their names?
Yes, Seenford can track brand names written in the Latin alphabet, including accented letters such as é or ü. Brands whose names or websites use non-Latin scripts are not yet supported at setup, and can request access instead. All questions are currently asked in English, while the market setting controls location.
How do I get started with Seenford?
Getting started with Seenford takes one step: enter the brand's website. Seenford reads the site and suggests the business description, market, competitors, topics and questions on one review page. After a quick check, the first baseline answers from all six engines start arriving. New accounts sign up on the site and choose Single or Multi-brand, and each brand gets a Free Report before anything is charged.