Signal Architecture & Baseline Audit
Capture an initial LLM visibility audit (ChatGPT, Gemini, Claude, Perplexity) to set the benchmark.
Normalise and enrich product metadata across catalogue, CMS and retail/press partners.
Implement or update schema.org / structured data and validate brand identity consistency.
Output:
A unified data foundation and baseline "signal map" ready for LLM ingestion.
Language Model Alignment
Use our propriety tool Lexym to define query clusters from category discovery flows
Optimise titles, bullets and copy for natural, conversational phrasing and long-tail variants.
Expand content coverage to mirror consumer intent in the query clusters.
Output:
Language-tuned brand assets aligned with how LLMs retrieve and rank.
Contextual Authority
Place mentions and backlinks on trusted editorial and review platforms that have authority.
Publish crawlable FAQs and knowledge content to reinforce authority on key brand attributes.
Seed or collaborate on UGC and expert discussions in high-authority forums and channels.
Output:
Distributed authority footprint across the surfaces LLMs reference.
Model Surface Monitoring
Continually track brand recall and competitors across ChatGPT, Gemini, Claude Perplexity.
Benchmark shifts against the baseline set in Step 1 and log model/version changes.
Detect shifts such as visibility drops or misattributions for rapid response.
Output:
A visibility dashboard with brand vs competitor mentions and trendlines.
Optimisation Loops
Run monthly testing cycles to track shifts in ranking from actions in Steps 1-3.
Track changes in content on and offsite versus baseline visibility in models.
Test A/B variants on high impact content that surfaces in LLM retrieval.
Output:
Iteratively refined content and digital footprint for stronger LLM signals.
Visibility Leverage Points
Identify the highest volume or high-impact queries using clustering and frequency data.
Target authority mentions (influencers, publications) for over-indexing in LLMs.
Syndicate structured content across influential channels
Output:
Prioritise actions that deliver outsized visibility gains.
AI-Native Brand Positioning
Review and structure USPs as direct answers to model-friendly queries.
Refine a conversational brand voice that feels natural in LLM outputs.
Position your brand narrative in model-agnostic terms that answer "why you?".
Output:
A durable, AI-native brand story that persists across evolving models.
Process Flow Summary
Always-on tracking begins with a baseline audit of brand visibility.
Ongoing monitoring benchmarks performance against competitors and key queries.
Insights feed into regular reporting and optimisation loops.
The result: continuous reinforcement of LLM visibility through our 7 step process.