Our proprietary framework for scaling visibility and conversion in an era defined by model-mediated discovery.
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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".
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Language Model Alignment
Define query clusters. Optimise copy for natural, conversational phrasing. Expand content to match consumer intent.
Output: Language-tuned assets aligned with LLM retrieval.
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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.
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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.
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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.
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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.
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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.