growthvibe Expertise | AI Search Optimization Specialists

What We Know

Core Disciplines

growthvibe's practice is built on four interconnected disciplines. Each is a distinct technical domain; together they form a complete approach to AI search visibility.

Answer Engine Optimization (AEO) — the practice of structuring content and signals so that AI platforms select a brand as the answer to a query. AEO encompasses content architecture, question-and-answer framing, schema markup, and citation engineering. It is distinct from traditional SEO in that the goal is to be cited or recommended inside an AI-generated response, not to rank a link.
Generative Engine Optimization (GEO) — optimizing how content is ingested, weighted, and surfaced by large language models. GEO addresses the training and retrieval layers of AI systems: what a model has learned about a brand, how recent and authoritative those signals are, and whether the brand's content is structured for machine comprehension.
Entity Optimization — establishing and maintaining a clear, consistent entity record across the web. This includes Wikidata entries, Google Knowledge Panel management, structured data implementation (Organization, Person, Product, FAQ, HowTo schemas), and ensuring AI platforms correctly understand what an organization is, what it does, and where it operates. Entity clarity is a prerequisite for reliable AI citation.
Citation Authority Building — earning third-party mentions in publications, directories, databases, and authoritative sources that AI systems draw on. Citation authority is the external validation layer: the volume, quality, and recency of references to a brand across sources that AI models treat as trustworthy. This is not link building; it is source positioning.

How We Work

The Five-Pillar Framework

growthvibe assesses and improves AI visibility across five dimensions. Each pillar addresses a distinct layer of how AI systems discover, evaluate, and cite brands.

Content Answerability — whether a brand's published content directly answers the questions its target audience asks AI. This covers content structure, question framing, directness of response, reading level, and the presence of definitive factual statements. Content that hedges, buries the answer, or requires inference is less likely to be cited. Content scored here is evaluated against the actual query patterns growthvibe observes across AI platforms.
Entity & Schema Optimization — the completeness and accuracy of structured data signals. This includes JSON-LD schema implementation across relevant types (Organization, Person, Product, Service, FAQ, HowTo, Article, BreadcrumbList), Wikidata entity completeness, and the consistency of entity attributes — name, description, founding date, location, people, and identifiers — across the web.
Authority Signals — the breadth and quality of third-party references. AI systems draw on a wide range of sources when forming answers: news coverage, industry directories, review platforms, academic citations, government databases, and social profiles. Authority signals are evaluated by source type, recency, and the semantic relationship between the citing source and the brand's category.
Topical Coverage — whether a brand has published sufficient, authoritative content across the full topic graph of its category. AI platforms favour sources that demonstrate comprehensive subject-matter expertise. Topical coverage is assessed by mapping the brand's existing content against the full set of queries relevant to its category, identifying gaps where competitors or general sources are currently filling AI answers.
Technical Foundation — the underlying technical conditions that enable AI crawling, indexing, and citation. This includes page load performance, crawlability, robots.txt configuration, sitemap completeness, canonical URL structure, and the presence or absence of technical barriers that prevent AI crawlers from accessing and processing content.

Platform Knowledge

Platform Expertise

Each AI platform has a distinct retrieval architecture. Optimizing for one does not guarantee visibility on others. growthvibe maintains working knowledge of each platform's mechanics.

ChatGPT — OpenAI's ChatGPT (GPT-4o and later models) blends training data with real-time web browsing via Bing. Brand visibility depends on what the model learned during training — which favours authoritative, widely-cited sources — and on live retrieval, which favours current, crawlable, well-structured pages. ChatGPT's recommendations are influenced heavily by the density and consistency of brand mentions across trustworthy sources. growthvibe understands how to engineer both the training signal layer and the live retrieval layer for ChatGPT visibility.
Google AI Overviews — Google's AI Overviews (formerly Search Generative Experience) retrieves from Google's index and applies a Gemini-based model to synthesize answers. The selection criteria overlap significantly with traditional Google quality signals — E-E-A-T, authoritativeness, structured data — but with additional weight on direct answerability and entity clarity. AI Overviews citations are not simply the top-ranked pages; they are pages Google's model assesses as the best answer to the specific query. growthvibe optimizes specifically for AI Overviews citation selection.
Perplexity — Perplexity is a real-time AI search engine that cites sources inline and emphasises recency. Its retrieval is web-native: it crawls and synthesizes live content rather than relying on a pre-trained knowledge base. Perplexity favours content that is factually precise, directly structured to answer queries, and published on domains with established topical authority. Citation in Perplexity is closely tied to the quality of on-page content and the domain's authority in the specific subject area. growthvibe monitors Perplexity citation rates as a core performance metric.
Claude (Anthropic) — Anthropic's Claude models are primarily knowledge-based, drawing on training data rather than live retrieval in most configurations. Visibility in Claude depends on the density and quality of brand mentions in the sources Anthropic trained on — primarily authoritative web content, Wikipedia, Wikidata, and structured databases. Entity clarity is especially important for Claude: models that have a clear, unambiguous entity record for a brand are more likely to reference it accurately and confidently.
Gemini (Google DeepMind) — Google's Gemini models power both standalone Gemini products and Google AI Overviews. Gemini's retrieval draws on Google's index and Knowledge Graph. Strong Knowledge Panel presence, consistent structured data, and E-E-A-T signals are the primary levers for Gemini visibility. growthvibe's entity optimization work is directly aligned with the signals Gemini uses to resolve brand identity and assess citation worthiness.

How AI Classifies Brands

Winning at the Annotation Gate

Before AI can recommend a brand, it must first classify it. The annotation stage is where AI systems evaluate content across 24+ dimensions using specialist models — and it is the single gate with the biggest payback for most brands.

growthvibe understands the five annotation layers that determine how AI systems classify your brand:

Gatekeepers — temporal scope, geography, language, and entity resolution. Does the system correctly identify which entity this content belongs to? Ambiguous or conflicting entity signals cause content to be filtered before deeper evaluation begins.
Core Identity — entity classification, attributes, relationships, and sentiment. This is where structured data and consistent entity signals directly determine whether AI recognises your brand as a distinct, classifiable entity in its category.
Selection Filters — intent category, expertise level, and claim structure. AI systems route content based on what type of question is being asked and whether the content demonstrates sufficient expertise to answer it.
Extraction Quality — sufficiency, dependency, and standalone utility. Can the AI extract a complete, self-contained answer from your content without needing to combine it with other sources? Content that is self-sufficient at the passage level is favoured.
Confidence Multipliers — verifiability and corroboration. Are the claims in your content independently verifiable? Are they corroborated by third-party sources? This is the layer where citation authority compounds into AI trust.

Everything growthvibe does — entity optimization, content structuring, schema implementation, citation authority building — is designed to maximise performance across these five annotation layers.

Publisher & Author Signals

Why Entity Identity Matters to Google

Google's own systems use explicit isPublisher and isAuthor flags to determine whether the algorithm recognises who published content and who created it. These are boolean variables — yes or no. Either Google's algorithm knows who you are, or it does not.

This has direct implications for AI visibility:

Publisher entity recognition — Google must recognise the organisation behind the website as a distinct, trustworthy entity. This requires consistent structured data (Organization schema with a stable @id), a Knowledge Graph entry, and corroborating signals across authoritative third-party platforms. growthvibe implements this through Wikidata entries, Crunchbase profiles, Companies House verification, and consistent sameAs declarations across every client's digital footprint.
Author entity recognition — Google must also recognise the individuals who create content. Author pages with Person schema, sameAs links to verified profiles, and demonstrated expertise in the content's subject area all strengthen the isAuthor signal. For founder-led businesses, building the founder's personal entity alongside the company entity creates a reinforcing loop.

growthvibe's entity optimization work directly addresses both flags. Every content page we produce includes proper author and publisher schema referencing verified entity @ids — ensuring that algorithms can confidently attribute content to a known, trusted source.

Measuring Results

Measurement Expertise

AI visibility is not measurable with traditional SEO tools. growthvibe has developed a measurement framework built around four metrics that reflect actual AI search performance. Full methodology is documented in the AI Search Visibility Framework.

AI Mention Rate (0–100) — a scored measure of how frequently the brand appears in AI-generated answers across a defined set of relevant queries. Calculated by running a structured query set across AI platforms and recording citation presence. A score of 0 means the brand does not appear; 100 means the brand appears in every queried response. This is the primary top-line metric for AI visibility.
Citation Authority (0–100) — when AI platforms cite external sources, how often is this brand's domain among the cited sources? Citation Authority measures the ratio of queries where the brand's own content is cited as a source, versus queries where AI synthesizes an answer without citing the brand. A high Citation Authority score indicates that AI systems treat the brand's content as a trusted reference — not just mentioning the brand, but pointing to it as a source.
Entity Clarity Score (1–5) — a qualitative assessment of whether AI platforms correctly and consistently identify what the brand is, what it does, who it serves, and where it operates. An Entity Clarity Score of 1 indicates the brand is unknown or mischaracterized by AI; a score of 5 indicates consistent, accurate, and detailed entity recognition across platforms. This score is evaluated by systematic query testing across AI platforms.
Answer Ownership (Count) — the number of distinct queries for which the brand is the primary recommendation or the definitive cited answer. Answer Ownership is a count metric rather than a percentage, tracking absolute territory: how many questions does this brand own in AI-generated responses? Answer Ownership is the long-term objective — building a position where, for the queries that matter to the business, AI platforms consistently recommend and cite the brand first.

These four metrics give a complete picture of AI search presence: reach (AI Mention Rate), source trust (Citation Authority), entity accuracy (Entity Clarity Score), and competitive position (Answer Ownership). Together they form the measurement layer of growthvibe's client work.

Our Expertise