04

Fame Engine & Digital PR

Building authority in AI answers starts with the nodes that disproportionately shape them: authority is not distributed evenly but follows preferential‑attachment dynamics, where a small set of high‑degree hub nodes-specific publications, creators, platforms, and knowledge repositories - account for most citations in a category, so reaching those nodes generates compounding returns while broad, untargeted outreach creates noise. We map the fame engine for your category (the publications AI engines cite most, the knowledge hubs that anchor entity recognition, the expert content ecosystems that drive narrative authority, and the community platforms that provide peer validation), then design a targeted PR and authority‑building programme around those high‑leverage nodes. This delivers a fame‑engine map, hub‑node targeting analysis using network‑science principles to isolate the ten most influential journalists, publications, and creators in your media graph, original research concepts that generate quotable statistics and connect your brand to high‑gap prompt clusters, and a co‑citation elevation strategy that moves your brand from “also mentioned” to primary recommendation. A dedicated measurement and attribution layer underpins the work, including a hub‑node targeting matrix (current relationship, citation frequency, pitch type, and estimated effort‑to‑impact ratio) and a 90‑day implementation sequence with clear milestones, hub‑node sequencing, and expected citation movement.