The Seven Pillars of LLM Visibility
Our proprietary framework for scaling visibility and conversion in an era defined by model-mediated discovery.
Signal Architecture & Baseline Audit
Audit brand visibility across ChatGPT, Gemini, Claude and Perplexity.
Enrich and standardise product metadata across all endpoints.
Implement structured data and ensure brand consistency.
Output: A unified data foundation and baseline "signal map".
Language Model Alignment
Define query clusters with Lexym.
Optimise copy for natural, conversational phrasing.
Expand content to match consumer intent.
Output: Language-tuned assets aligned with LLM retrieval.
Contextual Authority
Secure mentions on trusted editorial and review sites.
Publish crawlable FAQs and brand knowledge content.
Seed or support expert and UGC discussions.
Output: Authority footprint across model-referenced sources.
Model Surface Monitoring
Track brand recall and competitor presence across models.
Benchmark shifts against the baseline audit.
Flag visibility drops or misattributions.
Output: A live dashboard of brand vs competitor mentions.
Optimisation Loops
Run monthly tests to measure ranking shifts.
Compare on-site/off-site changes with model baselines.
Trial A/B content variants on high-impact queries.
Output: Refined assets and stronger semantic signals.
Visibility Leverage Points
Pinpoint high-volume or high-impact queries.
Target authority mentions and influencers.
Syndicate content across influential channels.
Output: Priority actions with outsized visibility gains.
AI-Native Brand Positioning
Shape USPs as direct answers to model queries.
Refine a natural, conversational brand voice.
Frame the brand narrative in model-agnostic terms.
Output: A durable, AI-native brand story across models.
Process Flow Summary
Always-on tracking begins with a baseline audit.
Monitoring benchmarks brand vs competitors on key queries.
Insights drive regular optimisation and reporting.
The result: continuous reinforcement through our 7 step process.