AI Reputation Management: What to Do When ChatGPT Gets Your Brand Wrong

Reputation management has always been a cornerstone of brand strategy. For years, businesses focused their efforts on managing Google search results: pushing down negative reviews, cultivating positive press coverage, and monitoring what appeared on page one for their brand name. That discipline has not become less important, but it is no longer sufficient. A new and arguably more consequential arena for brand reputation has emerged, and most businesses have not yet noticed it exists.

When a potential customer asks ChatGPT, Perplexity, or Google's AI Overviews about your business, the response they receive is not a list of links they can evaluate. It is a single, confident, synthesised answer that reads as authoritative fact. If that answer contains incorrect information about your services, your pricing, your location, or your expertise, the user has no reason to question it. They simply move on to a competitor. This is the new frontier of brand reputation in the AI era, and it demands a fundamentally different approach.

The New Reputation Risk: AI Hallucinations About Your Brand

Large language models are extraordinarily capable, but they are not infallible. One of their most well-documented weaknesses is a phenomenon known as hallucination: the generation of plausible-sounding but factually incorrect information. When this hallucination involves your brand, the consequences can be significant. Unlike a negative review on Google, which sits alongside other reviews and context, an AI hallucination is delivered as a standalone statement of apparent fact.

The risk is compounded by the growing trust consumers place in AI-generated answers. Research consistently shows that users treat AI responses with a level of confidence that often exceeds their trust in traditional search results. When ChatGPT states that your restaurant closed in 2024, or that your accounting firm specialises in a service you do not offer, the user takes that information at face value and acts accordingly.

How AI Models Generate Incorrect Brand Information

Understanding why AI models get your brand wrong is the first step towards fixing it. There are several common causes. First, models draw from training data that may be outdated. If your business relocated, rebranded, or changed its service offerings after the model's training data cut-off, the AI may present stale information as current fact. Second, models may conflate your brand with similarly named entities. A consulting firm in Birmingham may find its AI profile contaminated with details from an unrelated company with a similar name in another city.

Third, and most insidiously, models sometimes fabricate information when they lack sufficient data about your brand. Rather than acknowledging uncertainty, an LLM may generate plausible-sounding details about your founding year, your team size, your specialisations, or your pricing. These hallucinations are particularly dangerous because they sound confident and specific, giving the user no reason to verify them independently.

Retrieval-augmented generation (RAG) systems, such as those powering Perplexity and Google AI Overviews, mitigate this by pulling in real-time web content. However, if the web content they retrieve is itself inconsistent, outdated, or thin, the AI response will reflect those weaknesses. This is why the quality of your broader digital footprint matters enormously for AI brand monitoring.

Real-World Examples of AI Brand Damage

Consider a mid-sized UK law firm that discovered ChatGPT was telling users it specialised in criminal defence work. In reality, the firm had exclusively practised commercial property law for over fifteen years. The error likely originated from a similarly named firm appearing in the same training data corpus. Before the firm became aware of the issue, an unknown number of potential commercial property clients had already been misdirected.

In another case, a boutique hotel in the Cotswolds found that Perplexity was citing an outdated TripAdvisor page suggesting the property had closed for renovations. The hotel had been fully operational for over a year, but the AI model was retrieving and citing the most prominent web mention it could find, which happened to be the outdated closure notice. These are not edge cases. They are increasingly common consequences of brands failing to manage their AI presence.

"Traditional reputation management focused on Google page one. AI reputation management requires a fundamentally different approach because you cannot simply outrank a wrong AI answer — you have to change the underlying data the model draws from."

— Andy Beal, Founder, Reputation Refinery

Monitoring What AI Says About Your Brand

You cannot fix what you do not know is broken. The first and most critical step in AI reputation management is establishing a systematic monitoring practice. Unlike traditional search monitoring, where a single Google search for your brand name reveals the landscape, AI monitoring requires querying multiple platforms with multiple prompt variations to build a comprehensive picture of how your brand is being represented.

The challenge is that AI responses are not static. The same query posed to ChatGPT today may yield a different response tomorrow, depending on model updates, retrieval sources, and the specific phrasing of the prompt. This variability means that one-off checks are insufficient. Effective monitoring requires consistent, repeated assessment across platforms and query types.

Manual Monitoring Across Platforms

Begin by creating a standardised set of prompts that reflect how your customers are likely to ask about your brand and industry. These should include direct brand queries ("Tell me about [your company name]"), category queries ("best [your service] in [your location]"), and comparative queries ("[your company] vs [competitor]"). Run these prompts across ChatGPT, Perplexity, Google AI Overviews, and Claude at regular intervals, documenting the responses each time.

Pay attention not only to whether your brand is mentioned, but to the accuracy and completeness of what is said. Is your location correct? Are your services described accurately? Is your brand confused with another entity? Are outdated details being presented as current? Each inaccuracy represents a potential customer interaction that goes wrong before you ever have the chance to engage directly.

Manual monitoring is essential for building initial awareness, but it does not scale. A business with multiple service lines, locations, or product categories would need to run dozens of queries across four or more platforms on a weekly basis to maintain comprehensive coverage. This is where automation becomes necessary.

Automated Monitoring with Aether AI

Automated monitoring tools track your brand's AI presence continuously, alerting you to changes in citation accuracy, sentiment, and frequency across all major AI platforms. Aether AI monitors how your brand appears in responses from ChatGPT, Perplexity, Google AI Overviews, and Claude, flagging inaccuracies and tracking correction progress over time.

Automated monitoring also enables benchmarking against competitors. Understanding not just how your brand appears, but how it appears relative to your competitive set, provides the strategic context necessary to prioritise correction efforts. If a competitor is being recommended where you should be, automated monitoring identifies these gaps in real time.

Correcting AI Misinformation About Your Business

Once you have identified inaccuracies, the question becomes how to correct them. This is where AI reputation management diverges most sharply from traditional approaches. You cannot contact ChatGPT's editorial team to request a correction. There is no AI equivalent of disputing a Google review or requesting a retraction from a journalist. Instead, correction requires a systematic approach to strengthening the data sources that AI models draw from.

Strengthening Your Entity Data

The foundation of AI correction is entity clarity. AI models build their understanding of your brand from structured and unstructured data sources across the web. The stronger and more consistent your entity data, the more likely models are to represent you accurately. Start with your knowledge graph and brand authority signals: ensure your Google Business Profile is fully completed and regularly updated, your schema markup is comprehensive and accurate, and your Wikipedia or Wikidata entries (if applicable) are current.

Structured data is particularly powerful for correction because it provides AI models with machine-readable, unambiguous facts about your business. When your Organisation schema clearly states your founding year, your location, your service categories, and your contact details, it becomes a trusted reference point that models can draw from with confidence. Inconsistencies between your structured data and other web mentions create the ambiguity that leads to hallucination.

The Content Correction Strategy

Beyond structured data, the content on your website and across third-party sources must actively reinforce the correct information. If AI models are getting your specialisation wrong, publish authoritative content that clearly and repeatedly establishes your actual expertise. Create detailed service pages, case studies, and thought leadership content that leaves no ambiguity about what your business does and does not do.

The content correction strategy extends beyond your own website. Ensure that your profiles on industry directories, professional associations, LinkedIn, and review platforms all present consistent, accurate information. AI models that use retrieval-augmented generation pull from these sources in real time. A single authoritative source is good; five consistent authoritative sources are significantly better. For deeper guidance, see our article on how ChatGPT makes brand recommendations.

Platform-Specific Correction Processes

Some AI platforms offer limited mechanisms for correction. OpenAI has introduced feedback tools that allow users to flag inaccurate responses, though the impact of individual reports is difficult to measure. Google's AI Overviews draw heavily from indexed web content, meaning that traditional SEO corrections (updating page content, fixing structured data, improving E-E-A-T signals) have a more direct and measurable effect.

Perplexity, as a retrieval-first platform, responds most quickly to corrections in the underlying web content it cites. If Perplexity is citing an inaccurate source about your brand, updating or replacing that source with accurate information can yield visible improvements within weeks rather than months. Each platform has its own correction dynamics, and an effective AI reputation strategy accounts for these differences.

"The businesses most vulnerable to AI misinformation are those with thin digital footprints. The less structured, authoritative data exists about your brand, the more likely AI models are to fill the gaps with hallucinated information."

— Aether Insights, 2026

Preventing AI Reputation Issues Before They Start

Correction is necessary, but prevention is far more efficient. The brands that invest in building a defensible digital presence today will spend far less time and resources correcting AI inaccuracies tomorrow. Prevention is not about controlling AI outputs directly; it is about making the correct information so abundant, consistent, and well-structured that AI models have no reason to generate anything else.

Building a Defensible Digital Presence

A defensible digital presence is one where the correct information about your brand is so comprehensively documented across so many authoritative sources that it overwhelms any potential source of inaccuracy. This means maintaining detailed, up-to-date content on your own website, ensuring consistency across all directory listings and third-party profiles, publishing regular thought leadership that reinforces your brand positioning, and building a network of authoritative backlinks and mentions from trusted sources.

It also means proactively creating content that addresses the types of questions AI users are likely to ask about your brand. If you know that customers commonly ask about your pricing model, your team qualifications, or your service areas, ensure that clear, accurate answers to these questions exist in structured, accessible formats on your website. FAQ pages with proper schema markup are particularly effective for this purpose.

The Role of Consistent NAP and Brand Signals

NAP consistency (Name, Address, Phone) has always mattered for local SEO, but it takes on new importance in the AI era. AI models that encounter inconsistent NAP data across the web lose confidence in the accuracy of any single source. If your business name appears in three different formats across ten different directories, models may struggle to resolve your entity correctly, leading to confused or hallucinated responses.

Beyond basic NAP data, brand signal consistency includes your service descriptions, your founding date, your team information, and your industry categorisation. Every touchpoint should tell the same story. Audit your presence across Google Business Profile, Bing Places, Yelp, industry-specific directories, LinkedIn, and any other platforms where your business appears. Correct inconsistencies and standardise your brand information everywhere it appears.

When to Escalate: Legal and Platform Options

In most cases, the strategies outlined above will resolve AI reputation issues over time. However, there are situations where the misinformation is sufficiently damaging or persistent to warrant escalation. If an AI model is generating defamatory content about your brand, attributing actions to your business that never occurred, or confusing your brand with an entity involved in legal disputes, you may need to explore additional options.

OpenAI, Google, and other AI platform providers have processes for reporting persistent factual errors, particularly those that cause demonstrable harm. While these processes are still evolving, documenting the inaccuracy thoroughly, including screenshots, timestamps, and evidence of the correct information, strengthens any escalation request. In the UK, businesses also have recourse through data protection regulations if AI-generated content constitutes inaccurate processing of personal data associated with identifiable individuals.

Legal action should be considered a last resort. The AI industry is navigating largely uncharted legal territory regarding liability for generated content, and the outcomes of early cases remain uncertain. For most businesses, a proactive data and content correction strategy will resolve the issue more quickly and cost-effectively than legal proceedings. However, consulting a solicitor experienced in digital and AI law is advisable if the reputational damage is significant and persistent.

Related reading: Brand Reputation in the AI Era | AI Brand Monitoring Tools | Knowledge Graph and Brand Authority | How ChatGPT Makes Brand Recommendations