A repeatable, three-phase discipline that took a specialty-care brand from a zero Visibility Score to measurable citation gains across four major LLMs — without bypassing MLR review.
Establish the baseline: what AI systems already say about the brand’s category, where the organization’s own content is exposed or absent, and which specific Topic + Intent combinations — Focuses — are worth prioritizing first. Independent research across 177 brands in healthcare, SaaS, and financial services found that roughly nine in ten have zero mentions across major AI answer engines — not low visibility, but none.
Convert prioritized Focuses into published, machine-readable Knowledge Artifacts. An LLM does not read a page top to bottom the way a human does — it retrieves the smallest chunk of text that answers a specific question, so content must be pre-chunked into citable units at the point of authoring, not left for a retrieval system to guess at.
Quantify whether Build actually changed what AI systems say, and feed findings back into Discover so the next prioritization cycle is evidence-based rather than intuition-based. The Visibility Score compresses citation performance into one number built from five components.