Before your prospects visit your website, they ask an AI. ChatGPT, Perplexity, Gemini — they synthesise hundreds of sources you can't see, don't control, and may never have read. We audit what they're saying, identify why they're saying it, and systematically change it.
For two decades, online reputation management meant controlling what appeared on Google's first page. That model is now incomplete — and organisations that haven't adapted are being judged by a system they don't even know exists.
AI answer engines have changed the architecture of how people research other people and organisations. When a potential client, investor, board member, or partner wants to know whether to trust you, an increasing proportion of that research now begins not with a search engine query — but with a question typed directly into ChatGPT, Perplexity, or Gemini.
These AI systems don't retrieve a list of links for the user to evaluate. They synthesise hundreds of sources and deliver a single, confident narrative. That narrative either supports a decision to engage with you, or quietly undermines it. The user rarely questions it. They simply act on it.
The sources AI systems draw from include everything that has ever been written about you online — news articles from five years ago, forum posts you've never read, reviews on platforms you didn't know existed, Wikipedia content, Glassdoor entries, Reddit threads, and social media discussions. Most of this content was never written with any intent to damage you. But aggregated and synthesised by an AI, it can construct a narrative that is quietly devastating.
The critical problem is invisibility. Unlike Google, where you can search your own name and see your page 1 results, AI outputs vary by query, by platform, and over time. Most organisations have never systematically audited what AI says about them. Many discover it only when a deal falls through, an enquiry doesn't materialise, or someone mentions in passing what an AI told them.
Traditional ORM tactics — press releases, review responses, basic content creation — do not move AI outputs in any predictable way. Our methodology was developed specifically for how AI answer engines ingest, weight and synthesise information.
We monitor and work to influence outputs across every AI answer engine with meaningful market adoption — ensuring your reputation is consistent regardless of which AI your prospect happens to use.
The online reputation management industry built its entire methodology around Google's algorithm. The techniques that defined the field — link building, content placement, review management, press releases — were developed to influence a ranked list of search results that a human then evaluates. They were never designed to influence a synthesised AI narrative.
When you ask a traditional ORM agency about AI reputation management, you'll typically get one of two responses: a blank look, or a claim that their "content strategy" covers it. It doesn't. Content placement that ranks on Google page 1 does not automatically feed AI systems in the way that shapes their outputs. Wikipedia and Wikidata matter far more. Citation structures matter far more. The authority of the domains a piece of content lives on matters far more than its Google ranking.
We built ORM Geek specifically because the industry hadn't adapted. Our methodology exists because we spent years understanding how AI systems actually ingest and weight information — not how we wish they would, but how they demonstrably do.
Across industries and geographies, the pattern is consistent: an organisation discovers what AI is saying about them, engages us, and within months the narrative changes in a measurable and verifiable way.
Yes — though "control" is perhaps the wrong word. AI systems can't be directly instructed to say specific things about a person or organisation. What can be controlled is the information environment those AI systems draw from. Since AI outputs are synthesised from publicly available content, changing the content that exists — and changing which content is most prominently authoritative — changes the output.
This is not manipulation or deception. It is the legitimate practice of ensuring that accurate, current, and authoritative information about you is available and prominent — so that when AI systems synthesise a response about you, they're drawing from a fair and complete picture rather than an incomplete or distorted one. The goal is accuracy, not fabrication.
It depends on the severity and complexity of the current narrative, and the number of platforms affected. As a general guide: for straightforward cases where negative content is limited to one or two sources that can be removed or displaced, meaningful improvements in AI output are typically visible within 6 to 10 weeks.
For more complex situations — where negative content is distributed across many sources, or where the narrative has been ingested deeply into AI training data — significant improvement typically takes 3 to 6 months, with ongoing management required to maintain and build on those gains. We give you a realistic timeline in your audit report based on what we actually find, not a generic promise.
AI answer engines synthesise information from multiple sources, with different systems weighting different content types differently. In general, they favour content from high-authority sources — major publications, Wikipedia, established industry publications, structured data sources like Wikidata and schema markup — over content from lower-authority sources like personal blogs or minor forums.
Critically, negative content tends to be over-represented because it attracts more engagement (and therefore more links and citations) than neutral or positive content. A single negative news article cited by five other publications will carry more weight in an AI's response than ten positive articles that nobody linked to. Our methodology accounts for this asymmetry — building citation structures for positive content that match the organic amplification patterns that negative content often receives naturally.
This is one of the most common scenarios we encounter. A business dispute that was resolved in 2020. A regulatory inquiry that was closed without action. A review that reflected a service failure that has since been corrected. These situations are frustrating precisely because the underlying facts are real — but the AI is presenting an incomplete picture that omits the resolution.
In these cases, our approach focuses on two things: adding the resolution narrative to the information environment (where possible, updating the original source to include a resolution statement; where not, creating authoritative content that contextualises the situation) and building enough subsequent positive, authoritative content that AI systems weight the complete, current picture over the historical snapshot.
Yes — financial services, legal, and healthcare are among our most common client sectors. We understand the regulatory constraints that apply to reputation management in these industries: we don't create content that could be considered misleading financial promotion, we are careful about claims made in legal contexts, and we operate with full transparency about our methodologies.
For regulated clients who require it, we are happy to sign NDAs before any information is shared, and we can provide full documentation of our methodology for compliance review. All of our methods are fully compliant with platform terms of service and consistent with ethical marketing practice.
SEO optimises content to rank on Google for specific keywords. Content marketing creates content to build audience and brand awareness. Both are valuable — but neither is designed to influence AI reputation outputs, and applying them as if they were leads to disappointing results.
AI reputation management requires a fundamentally different approach: understanding how AI systems ingest and weight information, identifying which sources are actually influencing current outputs, building content in the formats and on the platforms that AI systems trust, and creating citation and authority structures that match the patterns AI systems are trained to interpret as credibility signals. It's a distinct discipline that requires its own methodology — which is why we built ORM Geek specifically around it.
We'll audit your reputation across all 8 AI platforms, document every response verbatim, map the sources driving the narrative, and deliver a full report with a prioritised action plan — completely free, within 4 hours.