// SERVICES
AI Readiness Score (ARS™) Assessment
Every AEO engagement starts with a comprehensive ARS assessment of your content portfolio. We evaluate each priority page against 32 scoring criteria across six dimensions: Header Alignment (do your H2/H3 tags match how people actually ask questions?), Summary-First Paragraphs (can the opening sentence stand alone as a complete answer?), Lexical Grounding (are core terms used early and clearly?), Chunk Reusability (can content blocks be extracted without losing meaning?), Internal Linking and Semantic Reinforcement, plus Schema Hierarchy and llms.txt bonus components. The result is a prioritized optimization roadmap that tells you exactly what to fix and why.
Content Structure Optimization for AI Retrieval
AI systems don’t read content the way humans do. They parse structure, extract key information, evaluate source credibility, and determine retrievability. We restructure your content using modular, citation-ready formatting: summary-first paragraphs that function as standalone answers, query-aligned headers that match natural search phrasing, self-contained content chunks that work as extracted citations, and clear lexical grounding that eliminates ambiguity. The goal is content that AI systems can confidently cite without editing.
Multi-Platform Visibility Tracking
We track your AI citation performance across Google AI Overviews, ChatGPT, Perplexity, Gemini, and Bing Copilot. This includes monitoring citation frequency, citation accuracy (is the right page being cited for the right query?), competitive share of citations (are you being cited more or less than competitors for your target topics?), and branded search lift attributable to AI mentions. This data informs every optimization decision and demonstrates measurable ROI.
llms.txt Framework Implementation
The llms.txt file is a structured markdown document placed in your site’s root directory that provides explicit instructions to AI systems on how to understand, prioritize, and cite your content. It’s essentially a robots.txt for AI platforms. We build custom llms.txt files that define your content hierarchy, establish citation formats, clarify geographic relevance, set tone and positioning guidance, and include necessary disclaimers. This single implementation can improve ARS scores by up to 7 points.
// CONTACT
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From invisible to cited. Across every platform.
One of our multi-location healthcare clients went from zero AI search presence to consistent citations across Google AI Overviews, ChatGPT, and Perplexity within months of implementing our AEO methodology. Their AI Visibility Score reached 35, with over 1,100 total mentions and 313 cited pages across platforms. This didn’t happen by accident — it happened because the content was restructured for retrievability, the schema architecture was built to establish entity relationships, and an llms.txt file gave AI systems explicit guidance on how to understand and cite the organization.
That’s the difference between hoping AI platforms find you and building a system that ensures they do.
The 32-point ARS™ scoring system.
The AI Readiness Score is our proprietary framework for measuring how retrievable your content is by AI systems. It evaluates six core dimensions plus bonus components:
- Standard ARS Components (0–25 points):
Header Alignment (0–5): Do your headers match how people actually search?
Summary-First Paragraphs (0–5): Can the opening of each section stand alone as a complete answer?
Lexical Grounding (0–5): Are core terms used early, with minimal vague language?
Chunk Reusability (0–5): Can content blocks be extracted and cited without edits?
Internal Linking & Semantic Reinforcement (0–5): Do your links and topic coverage demonstrate authority?
Bonus Components (0–7 points):
Schema Hierarchy / Entity Identification (0–3): Full @graph with @id for all entities
llms.txt Content Hierarchy (0–2): Comprehensive page prioritization for AI systems
Credential & Geographic Signals (0–2): Accreditation markup and location relevance
Pages scoring 18–25 are retrieval-ready for publishing. Pages scoring 28–32 represent elite AI readiness with comprehensive guidance. Most sites we audit score between 8–14 on their first assessment — which means significant, measurable improvement is available immediately.
// FAQS
Some frequently asked questions.
SEO optimizes for ranking in Google’s traditional search results. AEO optimizes for being selected as a cited source in AI-generated answers. The tactics are different: AI systems prioritize content structure, summary-first formatting, and lexical clarity over traditional signals like keyword density and backlinks. Our SIO™ framework integrates both because you need to rank in traditional results and get cited in AI responses.
We optimize and track performance across Google AI Overviews, ChatGPT, Perplexity, Gemini, and Bing Copilot. Our content structure optimizations work across all AI retrieval systems because they’re based on universal principles of content clarity, structure, and retrievability — not platform-specific hacks.
We track AI citation frequency (how often your content is referenced), citation accuracy (correct pages cited for correct queries), platform coverage (which AI systems cite you), competitive share of citations, and branded search lift attributable to AI mentions. These metrics are included in monthly reporting alongside traditional SEO performance data.
They’re complementary, not competing. AI systems often prioritize content that already ranks well in traditional search because ranking performance is one of their source credibility signals. The strongest approach is our SIO™ framework, which builds traditional search visibility as the foundation and layers AI optimization on top. Trying to do AEO without solid technical SEO is like building a second floor without a foundation.