AI-SEO & reputation in AI
We control what ChatGPT, Gemini, Perplexity and Claude say about your company and remove errors through source work.
AI systems shape opinion about brands without your involvement: they were trained on internet data, including outdated, inaccurate or negative information. We conduct regular monitoring of AI answers, detect disinformation and fix it through source work (publications, profiles, reviews) that language models rely on.
Brand reputation protection in AI answers means systematically monitoring what ChatGPT, Gemini, Perplexity and Claude say about your company and correcting errors through sources that language models trust. According to the Edelman Trust Barometer, trust in a business is directly linked to available information about it, which makes AI answer accuracy a reputational asset. Request an audit and get the first report within 24-48 hours.
Here we explain brand reputation protection in AI answers for companies across Spain. We cover why AI systems can spread incorrect information about your business, how to detect such errors, how they are fixed through source work and what continuous monitoring looks like. The reputation topic overlaps with AI-SEO here: language models (ChatGPT, Gemini, Perplexity, Claude), when answering questions about brands, rely on publications, directories, profiles, reviews and other external sources. If those sources contain outdated, incorrect or negative data, AI will reproduce them repeatedly. We conduct a structured audit of AI answers, compare them with real company data, identify disinformation and manage the sources that language models rely on. Below: the step-by-step process, a table of problem types and how we fix them, the Edelman Trust Barometer perspective, 10 FAQ and our AI-SEO packages with real prices.
Fixing errors in AI answers is not a one-off action but a managed process: language models update periodically and new sources can change what AI says about a company again. That is why we work in cycles: auditing current answers → identifying discrepancies → correcting sources → re-monitoring. Each cycle reduces errors and strengthens the positive information layer around the brand.
AI errors about a brand are rarely random: they reflect what is written in sources the language model considers authoritative. An outdated press release, a negative news story from three years ago, an inaccurate directory entry: all of this gets reproduced in ChatGPT or Gemini answers. Understanding the error type determines the correction tool. Some situations require updating Wikipedia, others need directory profile work, others call for publishing new authoritative content.
| Problem type | Example | Error source | How we fix it |
|---|---|---|---|
| Outdated data | AI names a closed office or an old product | Old directories, Wikipedia, news archives | We update profiles, edit Wikipedia, publish fresh content |
| Factual error | Wrong founding year, company size or service name | Incorrect media or catalogue publications | We work with editors, create correct authoritative pages |
| Negative framing | AI mentions an old scandal or neutral event with negative context | Negative news dominating brand sources | We grow the volume of positive authoritative sources to displace negative ones |
| Missing information | AI says "I don't know" or gives a minimal answer about the company | Company is poorly represented in authoritative sources | We build a baseline entity profile: Wikipedia, industry directories, media |
Brand reputation used to be shaped by media, word of mouth and search results. Today AI answers are added to the mix: millions of users ask ChatGPT what company X is, whether to trust it, how it differs from competitors. If AI reproduces incorrect or outdated information, the user gets a distorted picture before they even open your website. Controlling what language models say about a brand has become part of reputation management, just like review monitoring and PR.
«According to the Edelman Trust Barometer, trust in a business is largely shaped by the information available about it; as people increasingly learn about brands through AI, controlling the accuracy of that information directly protects reputation.»
Brand reputation protection in AI answers rests on three principles: systematic monitoring of what ChatGPT, Gemini, Perplexity and Claude say about the company; identifying disinformation and classifying it by type (outdated data, factual errors, negative framing, missing data); correction through sources that language models trust. This is not a one-off task but a continuous cycle: language models update and reputation control in AI answers requires regular reviews. According to the Edelman Trust Barometer, the availability of accurate information about a company directly affects trust in it, and this applies equally to AI answers. Request a quote and we will conduct the first AI answer audit for your brand and prepare the AI-SEO work plan within 24-48 hours.
AI Promotion
So ChatGPT, Gemini, Perplexity and Google AI Overview recommend your company when a customer asks “who’s the best in Valencia”. From €490 a month.
Ranking inside AI models works differently from classic SEO: what matters is how facts about your company are structured, how quotable they are, and whether you show up in the sources the models read. Every month we check what ChatGPT, Gemini and Perplexity say about you and fix whatever keeps you out of the answer. The field is young, so we work on subscription: models keep changing and a set-it-once setup doesn’t hold here. In Spain these services run €1,500-5,000 a month; we come in three times cheaper.
A short brief, and we’ll come back with a plan and quote within 24-48 h. No pressure.
It is the systematic work of monitoring what ChatGPT, Gemini, Perplexity and Claude say about the company, identifying errors and fixing them through source management (publications, profiles and reference pages that language models rely on). The check is run against a list of real customer questions about the company.
Language models were trained on internet data, including outdated articles, incorrect catalogue entries and old news. If those sources contain errors, AI reproduces them in answers. Models do not verify the currency of data at the moment of answering. So fixing the site alone is not enough: the sources the error came from need correcting.
Priority: ChatGPT (OpenAI), Gemini (Google), Perplexity and Claude (Anthropic) by audience reach. Additionally, Microsoft Copilot and Google search with AI Overviews. The set expands as new wide-audience systems appear. For a business in Spain we check answers in Spanish and English separately.
No, that is not how it works. Language models cannot be edited directly by users: answers can only change by modifying the sources they were trained on or that they use in search mode. That is why source work is the only reliable method.
Timelines depend on the error type and the speed of source updates. Updating Wikipedia and business profiles takes days, re-indexing weeks. A full correction cycle to a noticeable change in AI answers is usually 4-8 weeks. Monitoring continues after that.
Classic ORM focuses on reviews and search results. Reputation protection in AI answers works on a different layer: language models that give synthetic answers, not lists of links. Sources important for SEO and sources important for AI answers partially overlap but are not the same.
Regular monitoring is necessary. Language models update, their databases are fed new sources and AI answers about a brand can change. A one-time audit documents the current state but does not protect against future changes. Recommended frequency: every 30 days.
Monitoring and source work are conducted in three languages: Russian, Spanish and English. This is especially important for Spain: AI can give different answers to the same question in different languages. Reports and communication are in your preferred language. The sources are also language-specific: Spanish media and directories separately from international ones.
Wikipedia, authoritative industry media, Google Business Profile, LinkedIn, Crunchbase and other structured directories. For local business in Spain, Spanish news sites and catalogues are also important. The higher the source authority, the greater its weight in language model training. We start with the ones that already contain the error rather than all of them.
The package includes: initial audit of AI answers about the brand in three languages, a report classifying errors found, a source work plan, implementation of corrections and every 30 days monitoring with a report. The exact scope and price are in the AI-SEO direction in the packages block below.
