When someone asks ChatGPT, Gemini or Perplexity which is the best company in your sector, do you appear in the answer - or do you not exist? This is GEO. And it is already defining who will grow in 2025.
0%
of Portuguese companies are prepared for LLMs
4
Top LLMs where your brand can appear - or not
IPD-IA
methodology that underpins LLMscope’s GEO Score
What is GEO
GEO - Generative Engine Optimization - is the set of practices that make a brand more likely to be cited by language models when they answer user questions.
The mechanism is simple: when someone writes "who is the best labor law lawyer in Lisbon" in ChatGPT, the model does not perform a real-time search - it retrieves patterns from its training and, in some cases, searches the web before responding. GEO's question is: when this happens, does your brand participate in the response or is it left out?
The fundamental difference: In traditional SEO you have access to the real ranking. You can see that you are in position 7 for a certain keyword. In GEO, there is no public index to consult. You only know if you show up by systematically triggering prompts and analyzing the answers. It's a proxy - not a direct reading - and any honest tool should say that clearly.
GEO vs SEO - what changes
They are not opposites. There are different layers of visibility. A site well positioned on Google can be completely invisible to ChatGPT - and vice versa.
Dimension
SEO
GEO
Where you appear
Search results pages (SERPs)
AI-generated answers
How you are evaluated
Ranking by position (1st, 2nd, 3rd...)
Mentioned or not mentioned
What the user sees
List of links to click
Direct answer with (or without) your brand
How you measure
Rank tracking tools (actual position)
Systematic prompts and output analysis
Determinism
High - ranking is consistent
Low - LLMs are not deterministic
What you optimise
Backlinks, on-page, speed
Entity, authority, structure for RAG
Result horizon
Weeks to months
Months (depends on model training cycles)
The three layers of AI visibility
LLMscope measures visibility in three distinct layers. Confusing them is the most common error in GEO analyzes circulating in the market.
Organic
What the model knows about your training.
The brand was mentioned enough in public data for the model to "know" it independently. This is the most lasting visibility - and the hardest to build quickly.
Web Search
What the model finds when searching in real time.
ChatGPT with search, Perplexity, Gemini with Search Grounding - when these models search before responding, your traditional SEO counts again. But only for models with active search.
RAG
Whether the content is structured to be retrieved.
RAG-Readiness measures whether your website architecture - structured data, semantic clarity, FAQs - allows RAG systems to extract information about your brand effectively. High RAG-Readiness with low organic GEO Score is the most common scenario: the content exists but has not yet entered the models.
What LLMs value
There is no public algorithm like PageRank. What exists are patterns systematically observed in research and direct auditing of outputs. These are the factors with the highest correlation with visibility in AI:
Entity clarity
Can the model unequivocally identify what your brand is, does, and for whom? Ambiguity kills visibility.
External citations
Is your brand mentioned by sources that models value: media, technical publications, sector associations?
Data consistency
Are name, address, contact details and descriptions the same everywhere? Inconsistencies create conflict in models.
Structured data
Correct schema markup - Organization, FAQPage, LocalBusiness - facilitates the extraction of information for RAG systems.
llms.txt and ai.json
Instruction files for AI crawlers. They tell models what you are and what you can do for them - directly.
Volume and quality of content
Useful, specific and non-generic content. Models have learned to distinguish SEO-inflated content from content with substance.
How to interpret the GEO Score
LLMscope's GEO Score ranges from 0 to 100. It combines frequency of mention, relative position in response, and quality of citations, across multiple LLMs and prompt sets. It's not an absolute number - it's a trend indicator. What matters is the direction, not the exact digit.
0-20
Invisible
The brand is not mentioned or appears in a negligible way. Starting point for most Portuguese companies.
21-40
Emerging
Appears in some prompts but inconsistently. Mentioned but not as a reference in the sector.
41-70
Present
Consistent visibility across relevant prompts. It begins to be mentioned as an option, not just as an existence.
71-100
Reference
The brand is systematically included in the responses as a reference in the sector. Rare position in the current Portuguese market.
How to start working on GEO
Before optimizing, you have to measure. Without baseline data, you don’t know if what you do has an impact. The minimum viable process:
Define 10 to 20 prompts that a potential client of yours would make to ChatGPT or Gemini to find someone like you
Run these prompts in the main LLMs and record if you appear, where you appear, and in what context
Audit the base factors: llms.txt, ai.json, schema markup, entity consistency
Fix what's wrong before creating new content - structure before volume
Measure again after 4 to 8 weeks for trend data
Repeat systematically - GEO is not an action, it is a continuous process
The most common mistake: create new content before fixing underlying issues. A website with the wrong schema markup, ambiguous entity and missing llms.txt will not gain visibility in AI just by publishing more blog articles.
Frequently asked questions
Is GEO the same as AEO?
Not exactly. AEO (Answer Engine Optimization) focuses on appearing in featured snippets and direct answers from traditional search engines. GEO specifically focuses on generative LLMs. There is considerable technical overlap - structure, clarity, FAQs benefit both - but the evaluation mechanisms and update cycles are different.
How long does it take to see results in GEO?
The web search layer (Perplexity, ChatGPT with search) can change in days or weeks, because it depends on your traditional SEO. The organic layer - what the model learned during training - changes in model re-training cycles, which can be months long. There is no shortcut to organic visibility in LLMs. There is consistent work over time.
Do I need GEO if I already have good SEO?
Strong SEO helps the web search layer, but does not guarantee organic visibility in LLMs. The models learned from a specific slice of the internet and have their own criteria for what constitutes authority. There are companies with position 1 on Google that are invisible on ChatGPT, and vice versa. They are complementary subjects, not substitutes.
Are LLMs deterministic? Can the score vary?
They are not. The same prompt given twice may generate different responses, especially in high temperature models. This means that any visibility score has inherent variance. LLMscope uses multiple runs and aggregates the results to reduce noise. The score should be read as a trend, not as an exact number.
What is RAG-Readiness and why does it matter?
RAG-Readiness measures whether website content is structured to be retrieved by RAG (Retrieval-Augmented Generation) systems - the technology that allows LLMs to access up-to-date information rather than relying solely on training. A website with high RAG-Readiness but low GEO Score is in a favorable position: the content exists and is well structured, but has not yet been incorporated into the models. This is the most common scenario in Portuguese companies that have already done technical SEO work.
How do I know which prompts are relevant to my sector?
Start with what your real customers ask - in meetings, in email, in support chat. Converts these questions into natural prompts like someone would write them to an AI assistant. Avoid very specific prompts ("what is the best product X from company Y") - these only test whether the model knows your brand, not whether it associates it with market needs.
Go deeper
Each article explores a specific dimension of GEO with data from the Portuguese market.