The business, the choices, real sustainability.
Communication: media, events, website, reports, institutional relations.
By stakeholders, public opinion and, increasingly, by artificial intelligence models.
The Aurelian Lab AI Reputation Framework is a method that integrates corporate communication, stakeholder engagement, sustainability and analysis of the perception generated by LLMs to build, measure and influence corporate reputation in the age of artificial intelligence.
When these dimensions are consistent with each other, reputation is stable, even in the summaries generated by AI systems. When they diverge, the representation becomes fragile, partial and hard to govern.
We always start here: how stakeholders, media and AI see you today, and where doing, saying and perception don't match.
Values, positioning, key messages, concept, plan across all channels.
Content, events, relationships, website: the evidence that builds reputation.
We arrive here, and start again from here: results, stakeholder listening, adjustments.
Three proprietary tools: AEO Boost for visibility and citability, StakeGraph for entities and stakeholders, ESG Monitor for sustainability in AI.
Discover the tools →The Observatory that follows how AI is changing, with the glossary of terms.
Go to the Observatory →Software engineers, digital agencies, developers, event agencies, when the project requires it.
Sentiment, visibility, topic coverage, consistency between one model and another, risk of false or outdated information.
KPIs agreed at the start of every project and stakeholder listening.
First signals in 8-12 weeks, repositioning in six months.
Every data point is read and validated by Maria Luisa De Petris.
Articles from the editorial hub explaining the framework and how to make it operational.
AI Reputation is a priority for CEOs and Communication Directors because it shapes what stakeholders, customers, and talent learn about the company through AI answers. Governing it means measuring the gap between identity, evidence, and generated representation, then assigning clear responsibilities and actions.
In the LLM era, corporate communication must address both people and the systems that select, connect, and summarize information. Persuasive messages must therefore be supported by an architecture of content and evidence that is readable and verifiable.
AI Reputation is the representation of a company generated by artificial intelligence systems from its website, media coverage, reports, and third-party sources. It is formed through inference, may diverge from the declared identity, and can be measured and governed.
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.
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.
From the first step: seeing. Before intervening, you need to know how the leading AI systems describe your company today — what emerges, what is missing, what is distorted. Aurelian Lab begins every engagement with an Audit, using the tools in the Aurelian Intelligence suite best suited to the scope: Aurelian AEO Boost for visibility and citability, Aurelian StakeGraph for entities and stakeholders, and Aurelian ESG Monitor for sustainability reputation across AI systems. From there begins the six-month path through the four phases of the Aurelian Method (Analyse, Design, Activate, Measure), realigning identity, external evidence and algorithmic representation.
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 path through the four phases of the Aurelian 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.