Frequently asked questions about AI Reputation.

No, and confusing them leads to ineffective strategies. SEO optimises content to appear in traditional search results — Google, Bing — when users type a query. GEO (Generative Engine Optimization) optimises content to be cited and synthesised by generative AI systems — ChatGPT, Gemini, Perplexity — when users ask a question. AI Reputation is the higher layer: it is not only about being found, but about how you are represented by LLMs in the syntheses they generate about your company. SEO and GEO are technical levers; AI Reputation is a strategic discipline that integrates corporate communication, governance and algorithmic-perception analysis.

GEO (Generative Engine Optimization) is the discipline that optimises content to be cited and synthesised by AI systems. AEO (Answer Engine Optimization) optimises content to answer users' questions directly inside answer engines. Neither is a technical matter for the IT team: they are strategic levers of corporate communication. Without algorithmic readability, even the best reputational strategy remains invisible to the systems that increasingly mediate the first contact between your company and its stakeholders.

The Aurelian Lab AI Reputation Framework is a model that integrates corporate communication, stakeholder engagement, sustainability and the analysis of LLM-generated perception to build, measure and influence corporate reputation in the age of artificial intelligence. It interprets reputation as a system in which three dimensions (what the company does, what it communicates and what is perceived, including by AI models) must be aligned so that the algorithmic representation is consistent with the real identity of the organisation.

Yes. It is called hallucination: AI systems generate false information with full confidence, without signalling uncertainty. An LLM can attribute positions you never expressed, cite non-existent data, associate your brand with events that never happened. This information appears with the same authoritative tone as correct content — and is invisible to traditional monitoring systems. It is not a matter of bad faith: it is a structural feature of generative models. And the reputational damage is real even when the company has done nothing wrong.

Yes, and it is one of the lesser-known risks. LLMs have long memory and favour well-distributed structured content, regardless of publication date. A three-year-old crisis, documented by articles in authoritative outlets, can keep weighing on the algorithmic synthesis even if the company handled it well. The solution is not to erase the past — impossible — but to build a present information ecosystem that is more solid and algorithmically «stronger» than the one that tells the past.

There are three typical reasons. The first is visibility: if your presence on authoritative third-party sources is limited, AI systems simply have no material to build a representation. The second is structure: your content exists, but is not structured to be algorithmically readable — non-question headings, no highlighted data, no external confirmations. The third is consistency: signals across your channels contradict or overlap incoherently. AI systems favour what is verifiable, distributed over time and consistent across multiple sources.

The risk is not only reputational: it is competitive. Organisations with more coherent information ecosystems are represented better by AI systems — regardless of the real quality of the business. A competitor with a weaker narrative but better structured can be cited more favourably than you. That is why it is worth checking early how you are represented: the longer the gaps remain, the more work is needed to correct them.

If you communicate with consumers, yes. From 27 September 2026 in Italy, under Legislative Decree 30/2026, certain environmental claims are considered misleading in all cases, for example a generic statement without certification or "carbon neutral" based solely on offsetting. With ESG Reputation we identify the claims at risk and how to rephrase them; compliance is confirmed by your lawyers.

The only reliable way is to query them systematically. Asking a single AI once is not enough: each LLM has different characteristics, and answers vary by query, date and model version. A structured Corporate & AI Reputation Audit — analysing multiple systems with queries calibrated on the dimensions relevant to your organisation — is the starting point. That is exactly what the suite of proprietary tools Aurelian Intelligence does.

Through systematic and comparative analysis of multiple LLMs, queried with prompts calibrated on defined dimensions: sentiment (overall tone of the representation), visibility (frequency of citation in relevant queries), thematic coverage (which dimensions emerge and which are missing), narrative consistency (do the different systems converge or contradict each other?) and reputational risk (do negative, outdated or potentially false items emerge?). The result is a map of the distance between real identity, available evidence and algorithmic representation.

Yes, and it's one of the reasons monitoring a single system is not enough. ChatGPT, Gemini, Claude, Perplexity and Mistral have different training data, different source-retrieval logic and different synthesis modes. A company can be described positively by Gemini, be nearly absent in ChatGPT and miscategorised in Perplexity — in the same week. That's why multi-model analysis is the necessary condition for truly understanding your AI Reputation.

It is not enough on its own. AI systems favour independent third-party sources over proprietary corporate content. Media, Wikipedia, institutional databases, independent reports, citations on editorial platforms: all of this weighs much more than the corporate website. A company with an excellent site but weak external editorial presence will appear in the algorithmic synthesis exactly as its media coverage portrays it — not as it would like to appear on its own site.

They do read it — but they don't necessarily integrate it the way you'd like. An ESG report published once a year is a single signal. AI systems seek consistency distributed over time: repeated signals, across multiple sources, that mutually confirm each other. If your ESG information lives only in the annual report, with no media coverage, no quarterly data, no external confirmations, your sustainability narrative will be marginal in the algorithmic synthesis — even if your ESG strategy is solid and well documented.

Press releases, campaigns and media relations remain relevant — but they are no longer enough on their own. A new intermediary has slipped in between the message and the stakeholder: AI systems do not read communication the way a journalist does — they aggregate it, cross-check it with other sources and evaluate it for consistency and verifiability. A press release picked up by no outlet produces a weak signal. Communication must be rethought as ecosystem-building — not just message production.

AI Reputation cuts across communication, sustainability, HR, marketing and data: it does not belong to a single function and requires cross-functional governance with clear ownership. Strategically it sits under the CCO or CSO, but it involves the CEO, because it affects commercial, financial and talent-acquisition opportunities. That is why many organisations rely on a specialised external advisor: not to replace internal governance, but to bring method, measurement tools and a cross-functional vision.

Aurelian Lab is neither an agency nor a software: it is an independent advisory studio integrating three rarely combined competences — twenty years of corporate communication, Business Sustainability certification (Cambridge CISL) and Aurelian Intelligence, a suite of three proprietary tools for measuring and governing visibility, citability, entity and stakeholder relationships, and sustainability reputation across AI systems. An agency produces messages but does not measure how AI systems integrate them; an SEO tool tracks keywords but does not interpret the reputational meaning. Aurelian Lab holds measurement, strategy and governance together, with a proprietary method: the Aurelian Lab AI Reputation Framework.

From the analysis of the current situation: before intervening, you need to know how the leading AI systems describe your company today — what emerges, what is missing, what is distorted. That is why the first step is the 10-day Corporate & AI Reputation Audit, which uses the Aurelian Intelligence tools best suited to the scope. The analysis is proportionate to the request: for a single action, only what is needed is examined. Where the three levels — what the company does, what it communicates, what is perceived — do not match, we act with targeted interventions or with the six-month Reputation Governance, which works on the three dimensions of the method (what the company does, what it communicates, what is perceived).

The first measurable signals emerge within 8-12 weeks: new citations on third-party sources appear in LLM answers, previously absent dimensions become visible, sentiment stabilises. Structural repositioning takes six months — the duration of the Reputation Governance, across the three dimensions of the method. It is not a wait: it is the time needed for a coherent information ecosystem to be «read» and integrated by the models, which update their data in a distributed, non-instantaneous way. Timing depends on scope: a targeted intervention has its own duration, shown on the Method and Services page.

It depends on the starting point and the goal; the scope is defined before discussing investment. The path has three levels: the 10-day Corporate & AI Reputation Audit, which snapshots how the leading LLMs describe the company today and identifies priorities; targeted interventions — the two-month AI Communication Strategy and ESG Reputation, and tailor-made Reputation Events — to correct where the three levels do not match; and the six-month Reputation Governance, for those who want to govern AI Reputation as a structural function. The initial conversation is confidential and non-binding.