Generative Engine Optimisation

GEO Reputation Management

What is GEO

AI systems are now shaping how people are perceived

Generative Engine Optimisation (GEO) influences how large language models (LLMs) like ChatGPT, Perplexity, Claude, and Google AI Overviews represent individuals and organisations. While traditional SEO targets search engine rankings, GEO targets the training data, source weighting, and narrative synthesis that determine what AI systems say when queried.

For ultra-high-net-worth (UHNW) individuals, executives, and private clients, GEO is increasingly material. AI systems are regularly deployed in due diligence, counterparty research, and background checks. What a frontier model says about a principal when queried by a compliance analyst or potential partner can quietly shape critical business outcomes.

AI Narrative Audit
We query major LLMs like ChatGPT, Perplexity, Claude, Gemini and Google AI Overviews across multiple prompt types and geographies to establish a baseline, identifying coverage gaps, inaccuracies, and hostile narratives before they cause damage.
Source Architecture
AI models weigh content from authoritative sources differently. We build and place content on platforms LLMs trust heavily including Wikipedia, authoritative news outlets, and structured data sources to feed training and retrieval systems accurate facts.
Inaccuracy Correction
Where AI models generate false or outdated information, we pursue correction through underlying source editing, counter-content placement, and clear data signals that force models to update their outputs.
Ongoing GEO Monitoring
AI outputs shift constantly as training data and web indices refresh. We monitor responses to client-related queries on a structured basis and realign strategy as the algorithmic landscape evolves.
GEO vs SEO

How GEO differs from traditional reputation management

I
No Ranking to Target
Traditional SEO targets page-one visibility in search engine results. GEO targets the narrative synthesis layer of AI responses. Because there is no page one to occupy, the objective shifts from ranking a specific URL to making sure that the diverse datasets AI systems reference are accurate, authoritative, and aligned.
II
Training Data Lag
AI models operate with distinct training cutoffs and refresh cycles. Consequently, a negative article successfully removed from live search results can continue to populate AI summaries for months or years. Managing an online profile through GEO requires addressing both live web indexes and the historical record already ingested by frontier models.
III
No Recency Bias
While search engines natively prioritise fresh content, language models lack an inherent recency bias. A historical narrative from several years ago can be synthesised as a current fact if it carried significant authority during the model's training phase. Remediation therefore requires structural data corrections rather than standard search suppression.
IV
Jurisdictional Variance
AI models demonstrate significant fragmentation in source weighting. ChatGPT, Claude, Gemini, and Perplexity utilise different baseline datasets and web-crawling parameters, frequently returning conflicting profiles for the same individual. A robust GEO strategy must manage footprint accuracy across all major platforms concurrently.
Common Questions

Frequently Asked Questions

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

Large language models synthesise vast training datasets including Wikipedia and public records, to generate responses. Increasingly, they also use real-time web retrieval to fetch current information. What an AI says about you is directly tied to the authority, volume, and consistency of your broader digital footprint.

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

While major platforms provide reporting mechanisms for harmful or inaccurate data, these automated channels offer limited success for complex reputation cases. The most effective strategy is to correct or update the underlying source material. By making sure the primary assets AI models reference are accurate, the systems naturally update their outputs. We pursue platform correction routes where available but focus primarily on source-level management.

Is AI reputation management a new service?

Yes. It has emerged as a distinct discipline over the last few years as generative AI tools have become mainstream. While it builds on traditional Online Reputation Management (ORM) principles, it requires specific expertise in algorithmic data sourcing, web-crawling patterns, and large language model behaviours. Pavesen has pioneered AI reputation management since the technology entered mainstream use.

What is SERM and how does it differ from SEO?

Traditional SEO focuses on driving traffic to a single website for specific commercial keywords. Search Engine Reputation Management (SERM) focuses on controlling the entire first page of results for a specific brand or individual name. The goal is to manage content across multiple independent domains simultaneously so the narrative remains entirely accurate.

How many results can be controlled on the first page?

A standard search engine results page displays ten organic listings alongside feature blocks like Knowledge Panels, news carousels, and images. A robust SERM strategy typically aims to control seven to eight of those ten organic positions with verified, positive assets. This creates a secure perimeter around a digital profile.

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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