Straight answers.
The questions companies ask us about AI visibility, answered without padding. The list grows with what clients and engines actually ask, not with what would be convenient to answer.
01How do I get my company to appear in ChatGPT?
AI engines recommend companies they can read, understand and verify. In practice that takes four things: content on the site that answers the questions buyers actually ask, structured data (schema.org) identifying the company without ambiguity, presence in the sources the engines cite when they search, and a consistent brand description across the platforms where models learn.
There are two distinct routes and both count. In training memory, the model answers from what it learned about the brand up to its cut-off; here the entity matters most, meaning the knowledge graph and consistent descriptions. In live web search, the engine searches and cites sources on the spot; here what matters is being on the pages it finds and treats as reliable. A company can be strong in one route and invisible in the other, and the diagnostic separates them.
There is no way to pay for a place and no trick that replaces the work. What exists is method: measure where the brand stands today in each engine, find the questions with no dominant answer yet, and build the quotable presence before a competitor does.
02How does ChatGPT choose which companies to recommend?
Answering from memory, the model draws on what it absorbed in training: mentions on pages with authority, consistent descriptions of the company across several sources, presence in lists and comparisons. Searching the web, the process changes: the engine runs a search, reads a handful of pages and synthesises an answer citing the ones it found useful.
In both cases selection favours brands with a clear identity (who they are, what they do, for whom), verifiable proof (cases with numbers, open documentation) and presence in the right sources for the category. What holds a brand back: sites whose content only exists after JavaScript runs, contradictory descriptions across platforms, and marketing claims with nothing underneath for a model to quote.
Worth saying plainly: answers vary between engines and between weeks. The same question can be answered differently by ChatGPT, Gemini and Perplexity, which is why measurement runs continuously across several engines instead of concluding from a single search.
03How long does GEO take to show results?
It depends on the starting point, but the honest pattern is this: technical fixes (schema, llms.txt, extractable content) show up in live-search answers within weeks, because the engines re-read the site on every search. Training memory moves more slowly, at the pace of each vendor's training cycles, and can take months to reflect new presence.
That is why the plan runs across four horizons: weeks 1 and 2 for the technical foundation, weeks 3 to 6 for content and sources, weeks 7 to 12 for external authority, and from 90 days for entity consolidation and positioning. Each horizon has its own actions and measurable signals, and weekly measurement shows progress without waiting for the end.
Distrust promises of results in days. The engines have no priority queue for sale, and anyone promising guaranteed positions in a non-deterministic system is promising what they do not control.
04Does GEO work? Is it worth investing in?
It works in the sense that it is measurable: define a fixed set of buyer questions, measure the percentage in which the brand appears, apply the fixes and measure again. If citation rises, it is working; if it does not, the data shows where the blockage is. Engineering with measurement, not faith.
It is worth it for companies whose buyers ask AI assistants before deciding, which today includes B2B buyers, technical decision makers and consumers in research-heavy categories. Google AI Mode reached a billion monthly users in May 2026; the question is no longer whether buyers use these tools.
The answer can also be that it does not pay yet. If your category has residual demand in AI engines, or if the SEO base is not in place, we say so in the diagnostic. Investing in GEO without a foundation is paying to build the second floor first.
05Are GEO and AEO the same thing?
In practice, yes. GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization) describe the same work: making a brand quotable by engines that generate answers instead of returning lists of links. The difference is mostly of market and of period; some countries and tools adopted one term, some the other.
We use GEO as the umbrella term because it describes the scope better: you are not optimising only for answer boxes, you are optimising for any generative engine, from ChatGPT to Google AI Mode. If you see AEO, LLMO or AISO in a proposal, what matters is what sits under the acronym: real measurement, technical work and content work.
06Do I need an llms.txt file?
It is one piece of the technical foundation, cheap to implement and with no downside. llms.txt is a markdown file at the root of the site giving AI engines a curated index of the content: who you are, what you do, where the pages that matter live. The llms-full.txt variant delivers the main text of the site in a single read.
On its own it works no miracles; it is an emerging standard and not every engine reads it today. But the cost is an afternoon and the benefit is less ambiguity for those that do read it, with the trend pointing up. The same hygiene pack includes a robots.txt that allows the relevant AI crawlers and schema.org on the main pages.
07Is GEO the same as geomarketing?
No, and the confusion is common enough to show up in the answers of the engines themselves. Geomarketing is geographic market analysis: where to open a shop, how to segment campaigns by region. GEO, in the sense used here, is Generative Engine Optimization: the work of making a brand quotable by generative AI engines.
The distinction matters because they are different services, searched for and bought differently. Anyone searching for GEO consultancy finds the two mixed together. When evaluating proposals, one question settles it: what exactly will you measure, and in which engines.
08How do I run an AI visibility audit?
In five steps. First, define a fixed set of questions, the ones your buyers ask, from generic to decision stage. Second, choose several engines and separate the two routes, training memory and live web search. Third, ask each question in each engine more than once and record whether you are mentioned, recommended, and in what position. Fourth, record which competitors appear instead. Fifth, repeat on the same questions, week after week.
A first snapshot can be done by hand and is worth doing to know where you stand today. What hands cannot sustain is continuity: AI visibility changes week to week, and only a time series shows whether you are improving. Avoid the mistakes that invalidate an audit: a single engine, a single run, confusing a mention with a recommendation, and figures without an explicit denominator.
09How do I choose a GEO consultancy?
Four objective criteria separate method from marketing. First, continuous multi-engine measurement: if the proposal does not include measuring your visibility across several engines, week after week, with explicit denominators (how many questions, how many engines), there is no way to know whether the work is producing anything. Second, a documented method: the methodology should be readable, not a black box.
Third, honesty about what is controllable: AI engines are not deterministic, and anyone promising guaranteed positions is promising what they do not control. Fourth, a clear distinction between training memory and live search: they are different mechanisms, with different actions and different timelines, and a proposal that treats them as one thing has not understood the problem.
Add a fifth, practical one: ask to see the measurement from a real case, even anonymised. Whoever measures seriously has numbers to show; whoever does not has adjectives.
10Is it better to hire a consultancy or build the capability in house?
It depends on two things: what the team already knows and how much time you have. Building in house works when there is a technical SEO base, regular editorial capacity, and someone with time to follow a field that moves at the pace of vendor releases. The knowledge is public; the real cost is the time to turn it into practice.
A consultancy accelerates the parts where experience counts: the initial diagnostic (knowing what to measure and how), a technical foundation done right the first time, and access to continuous measurement without building your own tooling. The most common arrangement is hybrid: diagnostic and measurement external, execution shared with the internal team.
The honest decision rule: if in six months you cannot say with numbers whether your AI visibility improved, the problem is not who executes, it is the absence of measurement. Start there, with whoever you like.
11What should I expect from the first 90 days?
A serious start has three visible phases. In the first two weeks, diagnostic and technical foundation: measuring the starting point in each engine, fixing schema, llms.txt, robots and extractable content. Invisible work to the end customer, but it defines everything that follows.
From weeks three to six, content and sources: answering the questions where the category has no dominant answer yet, and planting presence in the sources the engines cite. From weeks seven to twelve, authority: external mentions, entity consistency, signals the models can verify. In parallel, weekly measurement from day one.
What not to expect: immediate jumps in the training memory of the models, which moves at each vendor's own pace. The first movements typically appear in live-search answers; consolidation in memory is a matter of further months. Anyone telling you otherwise is selling a calendar, not a method.
12Google AI Overviews took my traffic. What can I do?
First, the honest framing: the drop in clicks caused by generated answers is structural, not a technical fault on your site that an adjustment will fix. Google increasingly answers on the results page itself, and part of the traffic that used to exist does not come back. The strategy is not to recover the lost clicks; it is to exist inside the answer.
In practice, three fronts. Be among the sources cited: AI Overviews cite a handful of pages per answer, and extractable content with original data and correct schema competes for those places. Measure what changed: in Search Console, separate losses caused by position from losses caused by behaviour. And build presence where the answer is assembled, not only where the link used to be.
Blocking AI crawlers in retaliation is the wrong answer: it removes you from the answers without giving back the clicks. Presence in the answer became the asset; the click follows when the intent is strong.
13What is GEO, in simple terms?
GEO (Generative Engine Optimization) is the work of making generative AI engines, such as ChatGPT, Gemini or Perplexity, know your brand, describe it correctly, and recommend it when someone asks a question you are the right answer to.
It is the logical successor to SEO in a world where a growing share of searches ends in a generated answer, with no click. In SEO you optimised to appear in a list of links; in GEO you optimise to be cited inside the answer itself. The techniques overlap in part (good content, structured data, authority), but the target and the measurement are different.
14How is AI visibility measured?
With three main metrics, measured over a fixed set of questions relevant to your brand. Citation rate: the percentage of answers in which your brand is mentioned. Share of voice: what slice of the category's mentions is yours, against the other brands. Average position: when you appear in a list, in what place.
The word that matters is fixed: the same questions, in the same engines, week after week. A one-off search in a single engine on a single day is not measurement, it is an anecdote; engines vary between themselves and between runs. Only a time series with explicit denominators lets you say honestly whether visibility is rising.
15What do I do if the AI describes my company wrongly?
First, find the origin. If the error appears in memory answers (no web search), the model learned outdated or contradictory information; the fix is consistency: the same company description on the site, on LinkedIn, in directories and in every public source, reinforced by structured data. If the error appears only with live search, the source is a page the engine is reading, and that page is what has to change.
Second, do not expect an instant correction in the models' memory: it moves at the pace of each vendor's training cycles. Live search corrects faster, because the engines re-read the web on every answer. In both cases the prerequisite is monitoring: without continuous measurement, a wrong description can circulate for months before anyone notices.
16Can the AI invent information about my company?
It can, and it does: prices that never existed, services you do not provide, headquarters in another city. Models generate plausible text, and when they hold little reliable information about a brand they fill the gaps with assumptions. Brands with little structured presence are the most exposed, precisely because they give the model less verifiable material.
The defence is twofold. Reduce the vacuum: the more clear, consistent and structured information exists about the company (an extractable site, schema, identical descriptions on every platform), the less room is left for invention. And watch: measure regularly what each engine says, to catch the fabrications as they appear and correct the sources feeding them.
17Is classical SEO still worth investing in?
It is, and more than that: it is the foundation without which GEO does not hold. AI engines with live search read the same web Google indexes; a technically fragile site, or a slow one, or one with content hidden behind JavaScript, fails in both worlds at once.
What changed is the ceiling. Classical SEO alone no longer guarantees presence where a growing share of decisions begins, because the generated answer replaces the list of links. The right reading is not to swap SEO for GEO; it is to build the AI layer on top of a well-made SEO base, as one strategy for being found.
18Which AI engines matter for my brand?
It depends on who your buyer is, which is why measurement covers several. Google AI Mode carries the largest volume, because it sits inside the search everyone already uses. ChatGPT dominates direct assistant use. Perplexity weighs with technical users and early adopters who search with citations. Claude, Gemini, Grok, Mistral and DeepSeek each carry their own audience.
That is why we measure 10 engines and surfaces rather than picking one. The distribution of your buyers across them is itself a finding: it is common for a brand to be strong where its buyers are not, and absent where they are.
19Can I use AI-generated content on my site?
You can, on two conditions: real human review and substance of your own. Engines do not penalise text for having been written with AI help; they penalise thin, repeated content with no new information, whoever the author. A hundred pages generated in series on the same theme are spam to any modern engine.
What makes content quotable is what only you can add: original data, real experience, cases with numbers, positions taken. AI is good at giving shape; the raw material the engines cite has to come from the brand. Used that way it accelerates without risk; used as a volume factory, it builds a site the engines learn to ignore.
20Do I need to be on Wikipedia for the AI to cite me?
Not required, but what Wikipedia represents does matter: a verifiable entity, described by third parties, with sources. Models train intensively on Wikipedia and Wikidata, and brands present in those bases enter the models' memory with a firmer identity.
For most small and medium companies Wikipedia is out of reach in the short term (it requires notability demonstrated by independent coverage) and that does not block visibility: a Wikidata record is available right away, and entity consistency is built with the site, schema with sameAs, LinkedIn and the sector directories. That is the usual path.
21Do reviews count for AI visibility?
They do, and for local brands they are among the strongest signals. When someone asks an assistant for a clinic, a restaurant or a nearby service, the engines lean on sources carrying social evaluation: Google Business Profile, review platforms, rated directories. A complete listing, with recent reviews and replies, is read as evidence.
For digital and B2B brands the equivalent is the sector's review platforms and specialist directories, plus the public discussions where real customers talk about the product. The pattern is the same: engines prefer to recommend what independent voices can verify. Reviews are not bought or simulated; they are asked for, systematically, after real work.
22Why does Reddit appear so often in AI answers?
Because the engines look for genuine human experience, and forums like Reddit concentrate real discussions, with pros and cons, written by people who use the products. Several engines cite community platforms far out of proportion to their traffic, precisely because the content is less polished and more trustworthy than commercial pages.
The practical implication: the public conversation about your category is feeding answers whether you are there or not. Taking part has rules: an identified presence, contributions with substance, and zero spam, because communities expel and engines learn. One genuinely useful answer in a relevant thread can be cited for years.
23Can a small company compete in AI answers?
It can, and in some respects it has the advantage. AI answers do not simply reproduce the Google ranking: in many specific questions there is no dominant brand yet, because nobody built the quotable answer. Whoever gets there first keeps them, regardless of size.
The small company's advantage is specificity: a well-defined niche, concrete questions from its buyer, depth the generalists do not have. The disadvantage is a weaker entity, which is offset with consistency and patience. The realistic strategy is not to win the generic category questions; it is to own the specific ones.
24Should I block AI crawlers on my site?
For most brands, no. Blocking GPTBot, ClaudeBot or PerplexityBot removes the site from the sources the engines read, which means giving up on appearing in the answers where your customers are looking. It is a real trade: content protection against commercial visibility, and for anyone who lives on being found the arithmetic rarely favours blocking.
Fine control exists and should be used: robots.txt lets you decide crawler by crawler, and separate training bots from search bots. A publisher with paid content has reasons to block training; a services company that wants to be recommended has reasons to allow everything. The decision should be deliberate and revisited, not a default nobody reviewed.
25How much does GEO cost?
It depends on three factors: the technical starting point (a site with the SEO foundation done costs less to prepare than one that needs building work), the competitive intensity of the category (entering a contested category takes more content and more authority than owning a niche), and the pace you want.
That is why we publish no rate card: a number without a diagnostic would be marketing, not a quote. The path is the diagnostic first, which measures where you are and sizes what is missing; the proposal follows from it, with scope and investment justified line by line. Our published prices are in euros and anchored to the Portuguese market; outside Portugal we quote per market.
26Is GEO for local brands or only for digital companies?
For both, with different work. For digital brands (SaaS, e-commerce, online services) the game is played in extractable content, schema, topical authority and presence in the sources the models read. For local brands (clinics, restaurants, tourism, retail) the centre of gravity moves: a complete Google Business Profile, reviews, consistent name and address, local directories, geographic signals on the site.
Local questions to assistants are already routine: the best dentist in the area, a restaurant for a group dinner, who deals with a specific problem in the city. The engines answer from whatever local signals exist, and most nearby brands have worked on none of them. It is one of the areas where being first to do it properly still pays most.
The general answers are here. Yours are not.
Five critical questions from your category, put to the engines, with the real answers and a written read of where you stand. Thirty minutes, no commitment.