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Generative Engine Optimisation

GEO Reputation Management

What is GEO

AI systems are now shaping how people are perceived

Generative Engine Optimisation (GEO) is the discipline of influencing how large language models (LLMs) such as ChatGPT, Perplexity, Claude and Google AI Overviews represent individuals and organisations in their responses. Unlike traditional SEO, which targets search engine rankings, GEO targets the training data, source weighting and narrative synthesis that determine what AI systems say about a client when asked.

For UHNW individuals, executives and private clients, GEO is increasingly material. AI systems are used in due diligence, counterparty research, background checks and media research. What a frontier model says about a principal when queried by a compliance analyst, a journalist or a potential partner can shape decisions in ways that never appear in a formal file.

AI Narrative Audit
We query ChatGPT, Perplexity, Claude, Gemini and Google AI Overviews across multiple prompt types and geographies to establish a baseline of what AI systems currently say about a client. This audit identifies gaps, inaccuracies and hostile narratives before they cause damage.
Source Architecture
AI models weight content from authoritative sources differently. We build and place content on sources that LLMs are known to weight heavily, including Wikipedia, authoritative news outlets, professional directories and structured data sources, to ensure accurate narratives are available for training and retrieval.
Inaccuracy Correction
Where AI models are generating false, outdated or misleading information about a client, we pursue correction through source correction, counter-content placement and structured data signals that allow models to update their responses over time.
Ongoing GEO Monitoring
AI model outputs change as training data updates. We monitor AI responses to client-related queries on a structured basis and adjust strategy as the landscape evolves.
GEO vs SEO

How GEO differs from traditional reputation management

I
No Ranking to Target
Traditional SEO targets a position in search results. GEO targets the narrative synthesis layer of AI responses. There is no page one to rank on. The goal is to ensure the sources AI systems draw from are accurate, authoritative and positive.
II
Training Data Lag
AI models have training cutoffs and update cycles. Content that has been removed from live search may persist in AI responses for months or years. GEO requires managing both live content and the historical record that has already been ingested by frontier models.
III
No Recency Bias
Google prioritises recent content. AI models do not share this bias. A negative article from five years ago can be presented as current fact by a frontier model because it was authoritative at the time of training. This requires a different remediation approach from search suppression.
IV
Jurisdictional Variance
Different AI models weight different source geographies. ChatGPT, Claude, Gemini and Perplexity return materially different narratives about the same individual. GEO programmes must address the full range of frontier models, not just the most prominent.
Common Questions

Frequently Asked Questions

How do AI systems like ChatGPT decide what to say about me?

Large language models are trained on vast datasets of web content, including news articles, Wikipedia, social media, and other publicly available material. When asked about an individual, they synthesise this training data to generate a response. They also increasingly use retrieval systems - searching the web in real time for current information. The accuracy of what they say is therefore directly tied to the quality, volume, and recency of positive, accurate content about you online.

Can I ask ChatGPT or Google to correct inaccurate information about me?

Both platforms have processes for reporting inaccurate or harmful content, though these are limited in their effectiveness for individual reputation cases. The more reliable approach is to address the underlying source content - creating accurate, authoritative material that the AI systems will prioritise, and suppressing or removing inaccurate sources they currently draw upon. We pursue platform correction routes where available but focus primarily on source-level management.

Is AI reputation management a new service?

Yes - AI reputation management as a distinct discipline has emerged in the last two to three years as AI-generated responses have become mainstream. It builds on established ORM techniques but requires specific knowledge of how AI systems source and process information. Pavesen has been developing and refining our AI reputation management approach since the technology entered mainstream use.

What is SERM and how does it differ from SEO?

Standard SEO (search engine optimisation) aims to rank a specific website or page highly for targeted keywords. SERM (search engine reputation management) aims to control the full set of results for a specific name - ensuring that the right content appears across multiple domains and platforms, not just optimising a single site. SERM typically requires managing content across many different platforms simultaneously.

How many results can be controlled on the first page?

Google typically shows ten organic results on the first page, plus features like Knowledge Panels, news carousels, and image results. An effective SERM strategy typically aims to control at least seven to eight of the ten organic positions with positive, accurate content. This requires significant content development and placement work but is achievable for most individuals.

Related Services
AI Reputation Management
Full AI reputation management service across all frontier models.
Search Engine Reputation Management
Managing your presence across Google, Bing and search results.
Reputation Risk Monitoring
Ongoing monitoring across search, AI and news channels.
Wikipedia Reputation Management
Wikipedia is a primary source for AI training data. What it says matters.

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