A company asks an artificial intelligence system about its sector and finds that the answer combines news, comparison sites and corporate pages. Asesores turns that new form of intermediation into a commercial proposition: a guide for communication directors, a form for requesting it and an audit that takes the diagnosis to the boardroom. The dedicated report page, published on 13 September, organizes the entrance to an algorithmic reputation service supported by the technological capabilities of Good Rebels .
The document is presented as Decálogo de inquietudes de los dircom ante los LLM, a guide to ten concerns for communication directors. It starts with a recognizable problem: how a company appears when ChatGPT, Gemini or Perplexity talk about it. The content formulates that need, and the journey then asks for a name, company and email address, alongside acceptance of the privacy policy. The request for information becomes associated with a specific professional context, rather than remaining an anonymous visit to a service page.
The proposition has a quantitative antecedent. Analysis by Good Rebels and Asesores, reported on 1 July, examined the weight of sources in answers about banking, insurance, hotels and telecommunications. Its communicated findings include a brand-mention multiplier associated with news media of 1.78 in banking, compared with 1.05 in telecommunications. September's guide and the current audit offer give that research a commercial outlet: understanding which sources build a brand's image and deciding where to work on that image through communication.
Ten concerns create an entry point
The guide's page asks why one brand appears before another, which media influence models, how public relations and search optimization should work together and why different systems produce different accounts of the same company. It also mentions historical content, reviews, owned channels and the difficulty of justifying investment. The report brings together questions that can be distributed across communication, marketing and technology teams, presenting them as one area of business decision-making. It defines a professional problem before introducing the agency's response.
The preview promises three kinds of material: a diagnosis of those concerns, data on the weight of different sources and four recommendations for beginning to manage algorithmic reputation. It adds Asesores' working method, described as a seven-phase cycle connecting listening, source analysis, communication territories, public-relations activation and continuous measurement. This promise explains the value of the document before the reader requests it. The questions become associated with a specific capability offered by the agency, linking information about the topic to a possible consulting relationship.
The form associated with the guide makes a short request: name, company and email. Its button says Download study. All three fields are marked as required, as is acceptance of the privacy policy. These details identify a person and the organization from which they express an interest in the subject. The site's own policy lists handling enquiries, offering services and maintaining commercial relationships among the purposes of its forms. The report becomes a professional contact point around a need the agency has defined through research and its own content.
Downloading information and making a commercial enquiry retain different destinations. The report page also offers a preview through a link that takes the reader to a section of the same page describing its content and approach. The algorithmic reputation page ends with an invitation to get in touch and discover how AI responds about the company. A reader arriving through the guide can start by understanding the problem. Someone entering through the service finds a bounded diagnosis and a deliverable for internal discussion, addressing a different moment in the same commercial conversation.
What the agency turns into a measurement
Asesores presents generative AI as a fourth territory of reputation, alongside media, search engines and social networks. The commercial distinction is that an answer can describe, compare or recommend an organization before a person visits its website. The agency locates its four public metrics at that point. The first, Share of Answer, records how frequently a brand appears when someone asks about its category or sector. The generated answer is the unit of observation, with the brand as the subject being examined across that set of responses.
Share of Citation looks at another part of the process: the sources a model uses to construct its answer. That distinction separates appearing in a response from identifying the material contributing to it. A company may have a precise official description and still find that the model draws on a comparison site, review or news story. The service proposes examining that combination to decide which sources to work on. Measurement thereby connects with the established responsibilities of producing owned content and managing relationships with third parties who describe the organization.
The third dimension is generative sentiment. The page says it classifies descriptions as positive, neutral or negative, weighted by the relevance of each appearance. The fourth is consistency of language: coherence between a company's website, LinkedIn, Wikipedia, press coverage and newsroom. Within this offer, visibility extends beyond counting names. It includes associated attributes, the tone of descriptions and differences between the ways in which an organization's identity appears. Those observations give a communication team several different aspects to examine within the same response environment.
The agency offers reporting at the frequency its client needs and places the measurements within communication work. The advertised scope combines presence, sources and narrative. Asesores describes these indicators as an extension of traditional public-relations metrics, which remain part of evaluation. The diagnosis has a recognizable internal use: bringing together what models say about a brand, the material supporting those answers and the points where they differ. It moves a discussion spread across several platforms into a set of observations about the client organization.
Editorial content carries different weight across sectors
The analysis reported in July provides a specific comparison. Its communicated editorial-presence multipliers are 1.78 in banking, 1.64 in insurance, 1.38 in hotels and 1.05 in telecommunications. They concern brand appearances in responses associated with those sources. The analysis brings together four sector studies and reports more than 40,000 answers from ChatGPT, Gemini, Perplexity and AI Overview, generated from more than 10,000 instructions per model. This corpus supports the authors' commercial argument about the diversity of the generative environment and the role played by sources within it.
Reported editorial multipliers vary by sector
Brand-mention multipliers associated with editorial sources in the four-sector analysis reported on July 1, 2026.
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Mention ratios communicated by the authors, not increases in sales or traffic. The source article does not give the exact measurement period, geography or complete multiplier calculation here; these are not invented in metadata.
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| Category | Reported multiplier |
|---|---|
| Banking | 1.78 × |
| Insurance | 1.64 × |
| Hotels | 1.38 × |
| Telecommunications | 1.05 × |
Banking records the highest value in that comparison. The analysis relates editorial material to products, conditions, solvency, service, innovation and reputation, aspects requiring more than a list of features. Comparison sites provide functional information, while media bring context and third-party assessments. The distinction connects with a historical public-relations task: encouraging others to describe an organization's attributes. The research places that work among the materials retrieved by an answering system, adding another destination for information produced through corporate communication and external coverage.
In insurance, the reported multiplier is 1.64. In hotels it falls to 1.38, although media remain among the three leading source categories considered. Telecommunications shows a different distribution: when a citation comes from media, corporate websites or comparison sites, the analysis puts brand mentions at 97% of responses. The three categories have the same reported weight there. The sector therefore changes what editorial material can contribute relative to other sources, rather than making the banking pattern an identical prescription for every company seeking generative visibility.
Carlos Hergueta, partner and business development director at Asesores, interprets the work through credibility attributed by third parties. In comments accompanying the analysis, he explains that the change lies in making that credibility readable by machines and combining it with other sources. The connection with the agency's business is direct: media presence becomes part of an examination of sources and attributes, alongside its role in relationships with readers, journalists and other audiences. The work remains rooted in communication while gaining another form of observation.
Platforms do not use the same mix of sources
Comparison between models introduces another difference. According to the findings communicated by the authors, corporate pages and comparison sites appear in more than 78% of Perplexity responses. For ChatGPT, 24.7% include at least one media reference. Each figure describes a different combination of platform and source category. The commercial implication is to choose what to observe within each tool, rather than treating all generative presence as one aggregate result that hides the role of individual channels. Source selection becomes part of the way a company interprets its visibility.
The analysis describes AI Overview as closer to organic search logic and Gemini as more selective about editorial sources. It connects those differences with coordination between SEO, content, public relations, owned media, comparison sites and social activity. The proposed work extends beyond writing a page for one model. It considers how information about the same company is distributed across spaces with different functions: official sources establish attributes, comparison supports evaluation, and editorial coverage contributes an external perspective. The mix is part of what the service aims to examine.
Iván Hernández, partner and director of Search, SEO, GEO and Media at Good Rebels, presents the research as a way to turn the opacity of these systems into information for action. His explanation focuses on which sources construct a narrative, in which models and under which attributes. The approach corresponds with Asesores' separate metrics for appearances, cited sources, description and coherence. The analysis organizes work between disciplines that may previously have measured their performance through different tools and objectives, while contributing information about the same brand.
Source diversity enters planning
The communicated research identifies between six and eight distinct cited sources in banking alongside mention rates ranging from 61% to 68%. In telecommunications, the range described is five to seven sources; in insurance, four to eight. These sector ranges describe the observed relationship between diversity and presence. They offer a complementary view to the question of which medium is most influential: how many different references participate in the material used by a model when it talks about a brand. The comparison adds breadth of sources to the discussion of individual authority.
Ranges of cited-source diversity differ between sectors
Lower and upper bounds of the distinct cited-source ranges identified for banking, insurance and telecommunications.
Cited sources
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Descriptive ranges communicated for different sectors. In banking they are associated with mention rates of 61% to 68%; they are not a guaranteed increase from adding sources. No hotel range is reported here.
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| Category | Lower bound | Upper bound |
|---|---|---|
| Banking | 6 Cited sources | 8 Cited sources |
| Insurance | 4 Cited sources | 8 Cited sources |
| Telecommunications | 5 Cited sources | 7 Cited sources |
The composition described by the analysis combines one corporate website, three to four category comparison sites and one or two quality editorial references. Comparison sites appear in 70% to 90% of answers containing brand mentions. That presence gives them a role in entering an evaluation, while editorial content can add attributes beyond those in a listing. The commercial value of combining sources lies in the variety of information, from offer characteristics to reputation and positioning. Different kinds of page contribute different descriptions of the company under examination.
This reading makes the agency's language-consistency metric more concrete. A company can describe itself on its website, appear in news coverage and occupy a listing in a category. If each space uses different attributes and names, the material available for synthesis also changes. Asesores therefore links source observation with the definition of reference language. Coherence becomes a communication task that can be reviewed by people who know the offer, brand and corporate narrative, connecting the diagnosis with decisions about the information distributed through each channel.
Four public steps and a report for management
The service page sets out four steps. Diagnosis establishes a baseline of presence, attributes and sources in the main models. Reference language defines the narrative the brand wants to keep coherent. Editorial activation brings together content, proprietary data and distribution across several sources for generative visibility. Continuous measurement then proposes quarterly monitoring of impact and adjustments to activity. The sequence connects observation, decisions, production and renewed measurement within the same communication service, giving a prospective client an account of how the work would proceed after the initial diagnosis.
AI Rank appears as the Good Rebels group's proprietary technology for measuring presence in AI engines. Asesores also says it can adapt to another monitoring platform and the organization's internal methodology. That separates the tool from the consulting work. The advertised commercial entry is a bounded GEO audit delivered as a report suitable for the board. Clients receive a format for discussing internal priorities, while the agency offers a way to continue from diagnosis into activation and monitoring. Technology supports the proposition without being presented as the only route into the service.
The combination fits the profile Asesores gives of its team. Luis Dorado is identified as co-founder and director since 1995, with experience in reputation and crisis situations. Hergueta contributes business development and a background in media and consulting; Iván Bermejo directs marketing and projects; Juanjo Mayor combines B2B consulting with systems and digital transformation. The algorithmic proposition brings communication and technology capabilities that the agency already identifies separately together for a new destination of brand information. Their roles help explain the service's combination of research, narrative and implementation.
The guide supplies the beginning of that journey, turning a broad concern about artificial intelligence into working questions for a communication director. Its form introduces the person and company; the service page shows what would be measured and what management would receive. Sector research provides reasons to consider sources and models differently. This is a content and acquisition strategy built around the provider's own knowledge, with a diagnostic product through which to begin a specific commercial conversation. The path runs from a question about visibility towards a defined professional service.
Who's who and how to read the figures
Asesores is the Spanish agency whose website belongs to Asesores de Relaciones Públicas y Comunicación, S.L. Good Rebels participates in the research and is the group to which the service page attributes AI Rank. Carlos Hergueta represents public relations and business development; Iván Hernández represents Search, SEO, GEO and Media. LLM means large language model, while GEO refers here to work on a brand's presence in generative-engine responses. The named companies and roles identify who offers the service and who explains its research background.
September's guide is the acquisition content; the analysis communicated in July supplies statistical context; and the offer consulted in October describes the service. Multipliers compare mentions associated with sources, percentages use different response bases and ranges count cited sources. Share of Answer observes appearance, while Share of Citation examines provenance. Keeping those roles distinct makes it possible to follow the commercial journey and understand what information the agency proposes to take from an initial enquiry into its diagnostic report, without confusing the report's units with the customer's own business results.
In this article
Marian Vasile
Marian Vasile is a MarTech and growth professional. His work spans product marketing, SEO, paid advertising and deep online tracking, including behavioral analytics and attribution.
