LLM Legibility
Your brand exists in conversations your team cannot see.
When a customer asks ChatGPT which bank to trust, tells Gemini to compare insurance policies, or instructs an agent to find the best telecom plan, they do not visit your website. They rely on what the model has absorbed, retained, and decided to recommend. LLM Legibility is the practice of diagnosing and actively managing how AI models represent your brand — ensuring that what they find, absorb, and recommend reflects the brand you have built.
Invisible to the model, invisible to the customer
As AI-assisted search and recommendation grow, a brand with low LLM legibility is systematically underrepresented in the moments that matter. The gap between brands that manage this and those that do not will widen every quarter — and no traditional analytics report covers it.
No baseline, no management
Most organisations have not measured how AI models represent them. They have no accuracy score, no consistency benchmark, no valence reading. What cannot be measured cannot be improved — and what cannot be improved will drift.
"AI is becoming the intermediary between brands and their customers. Visibility in that layer is the new SEO."
Sundar Pichai
CEO of Google
DIFFERENTIATING VALUE
The equivalent of technical SEO — for the age of AI
The LLM Legibility Framework is a systematic methodology for diagnosing and improving how AI models represent a brand. It begins with a multi-model diagnostic battery: the same brand queries asked across ChatGPT, Gemini, Perplexity, and Claude, scored across four dimensions: accuracy, consistency, valence, and context-fit.
The diagnosis produces a legibility score and a signal management programme — editorial, structural, and technical interventions designed to improve how models represent the brand over time. Continuous monitoring and quarterly reassessment track progress and detect drift before it becomes a competitive disadvantage.
Dimensions of brand legibility diagnosed: accuracy, consistency, valence, context-fit
AI models audited simultaneously: ChatGPT, Gemini, Perplexity, Claude
HOW WE CAN HELP
From unmanaged risk to managed asset.
LLM Legibility turns AI model representation into something your organisation can measure, manage, and improve.
Multi-Model Diagnostic Battery
Systematic brand audit across ChatGPT, Gemini, Perplexity, and Claude. Legibility score across four dimensions with a clear baseline.
Signal Management Programme
Editorial, structural, and technical interventions that improve how models represent the brand over time — tracked against the baseline.
Continuous Monitoring
Ongoing tracking of brand representation across models, with quarterly reassessment, drift alerts, and updated intervention priorities.
