42.5%
Of Luxembourg residents aged 16 to 74 used a generative AI tool in the three months before the survey, against 32.7% across the EU
Source: Eurostat ICT household survey, 2025 reference period
33.6%
Of Luxembourg enterprises with ten or more employees used at least one AI technology, against 20.0% across the EU
Source: Eurostat enterprise survey, 2025 reference period
29.8%
Of domains cited in AI Overviews appeared nowhere on the matching first page of organic results
Source: Measuring Google AI Overviews, arXiv preprint, May 2026, REPORTED
34-42%
Overlap between the sets of sources an AI search system cited on consecutive observation days
Source: Schulte, Measuring Visibility in AI Search, April 2026, REPORTED
About GEO and AEO in Luxembourg
Luxembourg has high AI use, a regulator that has already looked at it twice, and no published measurement of how assistants actually behave for a Luxembourg user. That last gap is the work.
There Is No Luxembourg GEO Market To Buy Into, And That Is The Opportunity
No statistical office, trade body or research firm publishes a figure for what Luxembourg organisations spend on generative engine optimisation, answer engine optimisation or AI visibility monitoring. Four independent research passes for this page reached the same conclusion, so we state it as unavailable rather than allocating a global estimate by population or GDP, which would manufacture precision out of nothing. What does exist is a handful of providers marketing to this market, some based here and most operating from Belgium, France, Germany and the Netherlands. Very few publish raw prompt sets, full response archives, repeatability data or independently verified before-and-after results. That is the actual competitive gap, and it is a measurement gap rather than a capability claim.
Adoption Is High, Deployment In Finance Is Governed
Consumer use is well above the EU average and enterprise use is roughly two-thirds higher than the EU figure. Regulated finance behaves differently. In the joint Banque centrale du Luxembourg and CSSF thematic review fielded between June and August 2024 and published in May 2025, drawing responses from 461 institutions at an 86% response rate, 28% of responding institutions had AI use cases in production or development while a further 22% were experimenting or planning to. An earlier review reported 30% using AI technologies. Those two numbers must not be read as a decline, because the later survey widened the institutional population and used different response categories. We keep them apart rather than drawing a trend line that the data does not support.
Ranking And Citation Do Not Select The Same Sources
The most expensive assumption in this category is that winning the ranking wins the citation. Measured against the same queries, the two source pools only partly overlap, which is why we never report a rank movement as evidence of a citation gain.
Domain overlap between AI Overview citations and organic results
Measuring Google AI Overviews: Activation, Source Quality and Search Overlap, May 2026. REPORTED, because the retrieved version is an arXiv preprint rather than a completed peer-reviewed publication. The same study found 29.8% of cited domains appeared nowhere on the corresponding first page. Bars share one linear scale.
45-59%
Even The Brand Set Moves Between Days
Across repeated daily observations, overlap between the sets of brands mentioned ran roughly 45% to 59%, and overlap between cited source sets roughly 34% to 42%, over an observation window of about 45 days. The operational conclusion is blunt: a single screenshot on a single day is not evidence of anything, and neither is a single prompt run once.
Up to 40%
What The Founding GEO Paper Actually Claimed
The paper that named this field reported visibility improvements of up to 40% under its own experimental setup across a 10,000-query benchmark (Aggarwal and colleagues, ACM SIGKDD 2024, peer-reviewed). That is a benchmark-specific visibility metric, not a citation-rate guarantee, and anyone quoting it as one has not read the method section.
Every figure in this section comes from a multi-market study, not from Luxembourg. We label them that way here and we label them that way in your reports.
The One Luxembourg Measurement Nobody Has Published
Four independent research passes for this page went looking for the same thing and none of them found it: a controlled measurement of which language an AI assistant answers in, and which language it cites from, for a query originating in Luxembourg. In a country with three administrative languages, English as the language of finance, and 46.6% of residents holding foreign nationality, that is not a footnote. It is the central question, and it is open.
What the evidence cannot tell you yet
Which language an assistant answers in from a Luxembourg networkNo controlled public study across Luxembourgish, French, German and English. Product documentation confirms location can influence relevance, which is not the same as telling you the outcome.
Which source language gets cited most for Luxembourg-origin promptsMultilingual retrieval research shows bias towards English and towards the query language, in experimental systems, on multi-language benchmarks. None of it is Luxembourg.
Whether adding a Luxembourgish version earns any citation at allNever measured for any language pairing in the sources reviewed. Separately, Luxembourgish is not currently a supported language for Google AI Overviews or AI Mode, which narrows the question considerably.
Which assistants Luxembourg finance professionals actually use for vendor researchNo sector survey exists. Country-level platform share for Luxembourg is also thin, so we do not present a European or global share as a Luxembourg number.
What we build in the first weeks, as deliverable one
A fixed prompt set of your buyers' real questionsVersioned, with a change log, written in each language your buyers actually use, and covering transactional, comparison, reputational and regulated-finance intent separately.
Run from Luxembourg network origin, on documented settingsClean accounts with interface and preferred-language settings recorded, approximate and permitted precise location handled as separate conditions, repeated on different days.
Answer language and cited-source language captured separatelyBecause they are different variables. Plus source type, whether regulator, government, media, association, directory, owned site or academic publication.
Raw evidence handed to you, so you can re-run itPrompts, timestamps, engines, product surface, full answers and cited URLs. If you cannot reproduce our finding independently, it is not a finding.
Produced for you Genuinely unmeasured today
This is deliberately framed as a research deliverable rather than a promise about the result. We do not know in advance whether your French pages or your English pages will earn more citations in this market, and neither does anyone else. What we commit to is producing the dataset that answers it for your category, then reallocating the content plan to whatever it shows.
Our GEO and AEO Services in Luxembourg
Measurement first, then the content and entity work the measurement justifies. In that order, because the reverse is how agencies end up optimising for a language their buyers' assistants never cite.
Multilingual Prompt-Set Design and Baseline
A versioned prompt universe built from your buyers' real questions, written in each language they use, split by intent so a transactional query is never averaged with a reputational one. Run across ChatGPT, Gemini, Perplexity and Google AI features from Luxembourg network origin, on documented account and language settings, repeated on separate days.
The output is a baseline you can audit and re-run, not a score.
Citation Monitoring With Definitions Fixed In Writing
Presence, owned-domain citation, independent-source citation, source share and competitive answer share tracked as separate series, with each definition written into the engagement before the first report. Vendors define citation differently, some counting a linked URL and some counting an unlinked brand mention, so cross-vendor comparison is meaningless without the definitions attached.
Competitor list and prompt set are frozen for the reporting year so the numbers stay comparable to each other.
Accuracy and Correction Workflow
Every material statement an assistant makes about you classified as accurate, outdated, unsupported, misleading or false. In regulated finance this is frequently worth more than raw visibility, because a model can cite you correctly and describe your fund structure wrongly in the same answer.
Correction runs through your own domain, structured data, register entries and earned third-party coverage rather than through a support ticket to a model vendor.
Extractable Content Structuring
Self-contained passages that keep their meaning when lifted out of the page, a direct answer in the opening lines of each section, question-shaped headings, comparison tables for facts that invite comparison, explicit geographic scope, and named sources with dates on every claim that has one.
Google states plainly that no additional schema is required to be eligible for its AI features, so we do not sell structured data as a citation mechanism. We implement it because it clarifies entities, and we say so honestly.
Entity Work For A Thin Knowledge Graph
One identity across every language version rather than four apparently unrelated ones: consistent legal name, address, executive identities and service descriptions across the trade register, business profiles and directories, with language-specific descriptions and the correct inLanguage value on each variant.
Small-market entities are thin in knowledge graphs, so earned references from associations, regulators, specialist media and universities do work that on-page changes cannot.
Public-Source-Only Monitoring By Default
Generic commercial prompts, public company names, public URLs and aggregated observations. No client personal data, no named individuals, no employee-level prompt logs and no confidential material entering any assistant unless a specific task requires it and it has been approved in writing first.
Designed this way because it keeps ordinary brand-visibility monitoring clear of the heavier obligations that attach once personal data or systematic monitoring of people enters the workflow. Your data protection officer should confirm the assessment for your case.
if you cannot re-run it yourself, it is not a measurement
What We Report, And What We Refuse To Report
Three Layers That Must Not Be Collapsed Into One Score
Traditional search answers a question about position in a ranked list, so it takes organic position, impressions, clicks and click-through rate. Generated-answer visibility answers a different question about whether a system includes you and where it got you from, so it takes presence rate, owned-domain citation rate, independent-source citation rate, source share and descriptive accuracy. Business outcome answers a third question and takes AI referral sessions, self-reported discovery and influenced pipeline in euros. Movement in one layer is never reported as proof of movement in another, and no single blended visibility number is produced, because a blended number hides which of the three actually moved. Presence and citation in particular are different things: a name appearing in prose is a mention, and it only becomes a citation when the report's written definition of citation is also met.
Reporting To A Finance-Sector Reader
Each monthly dashboard carries the prompt-universe version and its change log, the engines tested, the languages tested, the location and personalisation settings used, the number of valid repeated runs, presence and citation by engine and by language, competitor presence, any inaccurate or unsupported claims found, the source-language and source-type distribution, what we changed during the period, AI referral traffic kept strictly separate from zero-click visibility, human-verified examples, and the archived raw evidence. Where the sample allows it, run-level variance is shown rather than a single point estimate. Euro figures appear only where AI referral traffic and conversion can actually be attributed, and we state plainly that this attribution is imperfect rather than presenting modelled revenue as measured revenue.
The Things We Will Not Put In A Report
No guarantee that an assistant will include you, because the outputs belong to third-party systems that change between sessions. No claim that structured data causes citation, since Google states no additional requirements exist for its AI features and no study establishes the causal link. No claim that llms.txt guarantees visibility. No European or global platform share presented as a Luxembourg figure. No Luxembourg AI Overview prevalence figure, because none has been published. No sales-cycle length unsegmented by company size. No performance fee tied to a one-off citation, which would reward exactly the day-to-day volatility the research documents. And no cost-saving percentage attributed to our location, because no methodologically comparable figure exists for it and inventing one would be the easiest lie in this industry to tell.
Already Live
The AI Act Duties That Actually Touch This Work
The Article 4 AI literacy duty and the Article 50 transparency obligations apply now. Regulation (EU) 2026/1744, the Digital Omnibus on AI, entered into force on 27 July 2026 and moved stand-alone Annex III high-risk obligations to 2 December 2027 and embedded Annex I products to 2 August 2028. Brand-visibility monitoring is very unlikely to be a high-risk use in the first place, but no regulator has published guidance naming this category, so we treat that as a reasoned position rather than a settled one.
Chapter V
Where Your Data Sits
Indonesia holds no EU adequacy decision, so personal data reaching us falls under Chapter V of the GDPR and needs Standard Contractual Clauses under Implementing Decision (EU) 2021/914 plus a transfer impact assessment. EU hosting alone does not settle it, because remote access from outside the EEA is still a transfer. Public-source-only monitoring is designed so this question stays narrow.
Why Choose Us as Your GEO Agency for Luxembourg?
Because The Differentiator In This Category Is Measurement Design, Not Optimisation Claims
"We optimise content for AI" is now a commodity sentence. What almost nobody publishes is the prompt set, the repeat runs, the source-language breakdown and the raw evidence. That is the part we lead with, and it is the part you can check.
3
Offices: Jakarta, Bandung, Bali
We Produce The Dataset That Does Not Exist
The language behaviour of assistants for Luxembourg-origin queries is genuinely unpublished. Rather than guessing it, we run a controlled multilingual prompt set from Luxembourg network origin and hand you the raw results as the first deliverable. It becomes your asset, not our secret.
We Repeat Before We Report
Cited-source sets overlap only about 34% to 42% between consecutive days. Every prompt runs more than once, on separate days, and variance is shown rather than smoothed. One run on one day tells you almost nothing, and we will not invoice as though it did.
We Write The Definitions Down First
Mention, citation, source share and answer share are four different questions with four different causes and fixes. They are defined in the engagement document before the first report, so nobody can quietly change a denominator later to make a chart improve.
We Name What We Will Not Claim
No citation guarantee, no schema-causes-citation claim, no global share dressed up as a Luxembourg number, and no cost-saving percentage attached to our location. Each of those is available to us and each of them is unverifiable, so we leave them out and say why.
Explore Related Services
GEO in Luxembourg shares its technical and entity foundation with search, so the two are scoped together.
Want To Know How Assistants Describe You In Luxembourg?
We will run a fixed prompt set for your category in French, German and English from Luxembourg network origin, twice, and hand you the answers, the cited URLs and the source-language breakdown. You keep the raw evidence whether or not you engage us. Contact our team to scope it.
Request Your AI Visibility Baseline
Frequently Asked Questions About GEO in Luxembourg
Is GEO just SEO with a new label?
No. SEO optimises for position in a ranked list of links. GEO and AEO optimise for whether a system names or cites you inside a generated answer, often with no click at all. The activities share a technical and content foundation, but the outcome and the evidence differ. Measured against the same queries, 29.8% of domains cited in AI Overviews appeared nowhere on the matching first page of organic results, and overlap with the top five organic results was 25.0%. So a page can rank first and never be cited, and a page outside the leading positions can be cited repeatedly. We report ranking and citation as separate series and never present a movement in one as evidence for the other.
Do you know which language AI assistants answer in for a Luxembourg user?
Not yet, and neither does anyone else. Four independent research passes for this page looked for a controlled measurement across Luxembourgish, French, German and English from Luxembourg network origin and none exists. Platform documentation confirms that location can influence relevance, which is a different statement from telling you the outcome. Multilingual retrieval research shows bias towards English and towards the query language, but on multi-language experimental benchmarks rather than in Luxembourg. This is exactly why our first deliverable is the measurement itself rather than a recommendation built on an assumption.
Should we publish in Luxembourgish to be cited?
For Google AI features the question is narrower than it looks, because Luxembourgish is not currently among the supported languages for either AI Overviews or AI Mode on Google own documentation, while smaller European languages including Romansh, Faroese and Maltese are. Peer-reviewed work also documents Luxembourgish as a low-resource language for large language models with characterised failure modes. Whether Luxembourgish adds incremental citation value on any platform has never been measured for any language pairing in the sources we reviewed. Luxembourgish may still be right for brand, civic or identity reasons. That is a separate decision, and we keep it separate.
Can you guarantee we will be cited by ChatGPT or Gemini?
No, and no responsible provider can. The outputs belong to third-party systems whose source selection changes between sessions and between days. What we can guarantee is the research, the implementation, the monitoring procedure and the delivery of raw evidence, and we can raise the probability while measuring it honestly. If a provider offers you a citation guarantee, ask them to put the definition of citation and the measurement method in the contract, and watch what happens.
Does schema markup make AI systems cite us?
There is no evidence that it causes citation, and we will not sell it as though there is. Google states that eligibility for its AI features depends on ordinary indexability and snippet eligibility with no additional technical requirement. No study establishes that adding structured data causes a citation, in Luxembourg or anywhere else. We still implement it, consistently across every language version with the correct inLanguage value and matching visible content, because it clarifies entities and relationships. That is a different and more modest claim than the one commonly made.
How often do you re-test, and why does that matter so much?
Monthly at minimum on a fixed prompt set, with every prompt run more than once on separate days. The reason is documented rather than stylistic: across repeated daily observations, overlap between cited-source sets ran roughly 34% to 42% and overlap between mentioned-brand sets roughly 45% to 59%, over a window of about 45 days. Against that level of movement, a single observation cannot distinguish a real gain from noise. We show variance rather than smoothing it away, and we freeze the prompt set and competitor list for the reporting year so the series stays comparable.
What is a realistic budget for this in Luxembourg?
No independent Luxembourg pricing survey exists, so we do not quote a market benchmark and treat any that you are shown as vendor-published asking prices rather than transaction data. Scope drives cost here more than anything else: the number of languages tested, the number of engines, prompt-universe size, repeat-run frequency, whether native French and German review is in scope, and whether regulated-claims review is required. We scope from those variables and put them in writing. We also do not attach a cost-saving percentage to our location, because no methodologically comparable figure exists to support one.
We are CSSF-supervised. Does AI visibility monitoring create a compliance problem?
Ordinary brand-visibility monitoring using generic public prompts, public company names and public URLs is a low-risk analytical activity, and it is very unlikely to meet the high-risk criteria under the AI Act, which centre on things like credit scoring, biometric identification and employment decisions. Two honest caveats. No regulator has published guidance naming AI-visibility monitoring specifically, so that is a reasoned position rather than a confirmed classification. And the picture changes if the engagement starts monitoring named individuals, profiling executives at scale or feeding visibility data into consequential decisions, none of which is in our default scope. Your compliance team makes the scope call.
Which platforms should we actually track?
Across Europe the measured distribution is heavily concentrated in ChatGPT, with Gemini second and Perplexity, Copilot and Claude behind, but no country-level breakdown for Luxembourg is available to us, so we do not present a European share as a Luxembourg one. Platform usage share also does not tell you which platform produces commercial discovery, which one cites Luxembourg sources, or which one your buyers use for vendor research. So we set platform priority from your own analytics and buyer research alongside the share data, and we track across several rather than betting the programme on one.
How does this fit with the SEO work, if we do both?
They share a technical and entity foundation, so running them as two disconnected projects means paying twice for the same crawlability, structured data and entity-consistency work. What differs is the measurement and the content structure. We scope them together, keep one identity across every language version, and report the two as separate series so you can see which one is moving. Our companion service page for search in this market is our SEO Company Luxembourg service page, linked at the foot of this page.
