At its core sits the
AICE Decision Model: an observational framework that maps how AI platforms appear to
weight which brands to cite — Entity Authority, Consensus Strength, Contextual
Relevance, and Sentiment & Framing. Not a scoring formula Growth.pro manipulates — a
guide to what to make accurate, expertise-grounded, and discoverable about your brand.
The four dimensions reinforce
each other. Strong entity definition does not compensate for absence from third-party sources. Strong
content does not compensate for inconsistent brand representation. Gaps in one dimension cap the leverage
of the others — which is why AICE addresses all four together, sequenced across the four PAVA pillars.
The language of this practice
matters. Engineering would imply acting on the system; Enablement describes the work
accurately. We do not work against how AI evaluates. We enable AI to do its citation work well, by
improving what is true and discoverable about the brand.
Structural & semantic
Schema, structured data, entity definitions, knowledge graph integration — primarily addressing Entity
Authority.
Editorial
Third-party publication placement, expert commentary, authority-grade content — primarily addressing
Consensus and Sentiment.
Technical
Answer-extractable formatting, retrieval-ready infrastructure, platform-specific optimisation —
primarily Contextual Relevance.
Competitive
Source ecosystem coverage, share-of-voice management, earning a more accurate position in the answer —
defending all four dimensions over time.