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AI Reputation Management

LLM Reputation Management

What We Do

Managing what large language models say about you

Large language models have become a standard tool for due diligence. Compliance teams at banks and family offices, alongside investors and journalists, now routinely query ChatGPT, Perplexity, Claude and other LLMs about individuals before any meeting or deal. The answers these systems provide shape final outcomes, often without the person being searched ever knowing an AI was involved.

For UHNW individuals, executives and private clients, LLM reputation management is now a necessity rather than a niche concern. It is a fundamental part of a modern digital reputation strategy. These models present a unique challenge: they do not prioritise recent news, blend various sources into a single confident response, and traditional ORM tools simply cannot track what they say.

LLM Audit
We query every major LLM platform, ChatGPT, Claude, Perplexity, Gemini, Microsoft Copilot and Meta AI using structured prompts to find out exactly what each system says about you. We document and prioritise any discrepancies between platforms, inaccuracies or negative narratives.
Source Content Strategy
LLMs rely on indexed web content to build their answers. We identify which sources are shaping your current LLM narrative and create authoritative content to displace inaccurate or harmful sources in both the training pipeline and live retrieval.
Wikipedia & Knowledge Graph
LLMs treat Wikipedia as a primary source of facts. Maintaining accurate, well-sourced Wikipedia content is one of the most effective ways to manage your LLM reputation. We manage your presence while strictly adhering to editorial policies.
Platform Correction Submissions
When LLMs produce flatly false information, we seek corrections through each platform's specific feedback channels. This addresses the immediate output; however, lasting reputation management requires work at the source-content level.
RAG Layer Management
Models using retrieval-augmented generation (RAG) pull live web content into their answers. Managing these responses requires a dual-layer approach: controlling both current search results and the underlying training data.
Ongoing Monitoring
LLM responses shift as models update and web content evolves. We perform structured monitoring across all major platforms and alert you the moment a material change occurs in your AI-generated narrative.
Why LLMs Are Different

The specific risks large language models create

I
No Recency Bias
Unlike Google, LLMs do not prioritise the most recent content. Negative information that vanished from search results years ago may still exist in an LLM’s training data and appear in its answers today. Clients with clean Google profiles are regularly surprised by what these systems say about them.
II
Authoritative Presentation
LLMs present information as confident, synthesised statements rather than a list of links. Someone reading an LLM response often views it as more authoritative than a standard search result, yet there is no simple way to verify its accuracy or the sources it cites.
III
Cross-Platform Variance
Different LLMs give different weights to various sources. ChatGPT, Claude, Perplexity and Gemini may provide completely different answers to the same query about an individual. A thorough strategy involves monitoring every platform, not just the most popular ones.
LLM Reputation Management - Answered

Common questions about LLM reputation management

What is LLM reputation management?

LLM reputation management is the process of monitoring and controlling how large language models such as ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews portray an individual or organisation. These systems are now a primary source of information for compliance teams, investors, journalists and counterparties. What they say shapes high-level decisions just as search results once did, but with more perceived authority and far less transparency.

How do large language models decide what to say about a person?

LLMs are trained on vast amounts of web text, including news, Wikipedia, forum posts and other indexed content. When queried, the model synthesises this data into a narrative. Content that is authoritative, frequently cited and clearly attributed is more likely to be included. Conversely, negative or inaccurate information in the training data will appear in responses unless it is actively managed. Some models also use retrieval-augmented generation (RAG) to pull in live web search results.

Is LLM reputation management different from AI reputation management?

The two terms are closely linked. AI reputation management is a broader field covering all AI systems, including image generators and recommendation algorithms. LLM reputation management focuses specifically on large language models, the conversational platforms used for research and due diligence. For private clients and executives, this is the most commercially important part of a wider AI reputation strategy.

Which LLMs does Pavesen cover?

We cover all major platforms with significant user bases, including ChatGPT (OpenAI), Claude (Anthropic), Perplexity, Gemini (Google), Microsoft Copilot and Meta AI. Because each system uses different training data and source weighting, an overall approach does not work. We apply platform-specific monitoring and strategy to each one.

Can inaccurate LLM responses be corrected?

Yes, though it is not the same as a Google removal request. While each platform has its own feedback and correction channels, the most lasting solution is to ensure that accurate, authoritative source content is available and clearly indexed. This gives the LLM the correct facts to draw from. Pavesen manages both immediate corrections and the long-term content strategy that shapes these narratives.

How quickly do LLM responses change?

This depends on the platform. Some LLMs update in real time using live retrieval (RAG), while others follow fixed training cycles that happen quarterly or annually. It takes time for changes in web content to filter through to a model's core responses. A thorough programme addresses both this immediate retrieval layer and the long-term training pipeline.

Related Services
AI Reputation Management
Broad AI reputation management across all AI systems and channels.
Generative Engine Optimisation
Optimising how AI systems represent you - the methodology behind LLM reputation work.
Wikipedia Reputation Management
Wikipedia is the single most influential source for LLMs.
Reputation Risk Monitoring
Continuous monitoring across search, LLMs and news channels.

What LLMs say about you
is shaping decisions right now.

Find out what the major LLMs say about you with a confidential audit.

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