SiteNexis is an AI Retrieval and Machine Trust Intelligence platform that models how AI systems — including ChatGPT, Gemini, Perplexity, and Claude — retrieve, interpret, trust, and recommend web content. It runs 16 intelligence agents across four layers and produces a 12-dimensional audit report with actionable recommendations.

How does SiteNexis improve AI visibility?

SiteNexis analyzes entity clarity, schema completeness, chunk extractability, semantic trust signals, and retrieval simulation across six stages. Each finding maps to a named issue with specific recommendations. Improvements to entity consistency, schema alignment, and content structure directly raise the probability of being retrieved and recommended by AI systems.

Which AI systems does SiteNexis analyze?

SiteNexis models retrieval behavior across Google AI Overviews, ChatGPT browsing and RAG modes, Perplexity, Gemini, Claude web search, voice assistants, and autonomous agents. Provider scoring weights are configurable and all scores are labeled as probabilistic estimates.

What is the Machine Trust Score?

The Machine Trust Score is a 0–100 composite that models AI system confidence in a website as a reliable source. It combines entity credibility consistency (30%), schema trust alignment (20%), external validation depth (25%), contradiction absence (15%), and trust degradation resistance (10%).

What is the Intelligence Report?

The Intelligence Report is a Grok-style editorial narrative generated for every audit. It synthesizes all twelve scores and the top issues into six named sections: a Signal Summary, SEO Posture, AI Visibility standing, Machine Trust analysis, Entity Health, and a Strategic Verdict with a composite X.X/10 score. Unlike dashboards that surface raw numbers, the Intelligence Report reads like a professional analyst's assessment — prose with precision.

What is the Decision Roadmap?

The Decision Roadmap is the output of the Decision Orchestrator — a v4 system that transforms a ranked issue list into an optimally sequenced action plan using Sequencing Value Score (SVS). It identifies the single best action to start with, groups subsequent actions into This Week, This Sprint, and Backlog bands, and calculates the projected score impact of completing each band. It also identifies parallel tracks for team-based execution.

SiteNexis has a free tier with 1 full AI visibility audit per month covering Layers 1–3. Layer 4 Machine Trust Intelligence — including Retrieval Simulation, Machine Trust Score, Temporal Authority, Recommendation Surface Mapping, and Synthetic Entity Detection — is available on Pro and Agency plans.

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AI Visibility Engineering 6 min readJun 16, 2026

Part 10 of 10AI Visibility Engineering series

The Retrieval Readiness Checklist

A practical checklist for assessing whether a page is structurally ready for AI retrieval — across crawl, extraction, chunk, and query-answer alignment layers.

Retrieval readiness is the composite measurement of how well a page is prepared for the full AI retrieval pipeline. It is not a single signal; it is the intersection of four layers: crawl readiness (can the AI reach the content?), extraction readiness (can the AI extract clean text?), chunk readiness (does the extracted text form quality chunks?), and query-answer alignment (do the chunks satisfy the query forms that matter for this page's topic?).

For sequencing this work beyond a single page audit, NexisHub describes a cross-functional GEO operating strategy that assigns crawl, content, schema, and measurement ownership across teams.

Crawl Layer Checklist

Page is reachable within 2 clicks from homepage
No noindex directive (unless intentional)
Not blocked in robots.txt for AI crawlers
Core content is present in rendered HTML (not JS-only)
Page load time under 3 seconds (slow pages deprioritized in crawl)
Canonical URL is self-referential or points to a preferred version

Extraction Layer Checklist

Boilerplate ratio below 30% (content text dominates)
Primary content not inside JavaScript-rendered elements
No paywall or login gate on content intended for AI visibility
Text is in readable semantic HTML elements (p, h1-h6, li, td)
Content is not primarily embedded in images or PDFs

Chunk Layer Checklist

Every paragraph names its subject entity in the first sentence
No context-dependent language ("As mentioned above," "It also offers")
Each factual claim is complete within a single paragraph
H2 and H3 headings describe the content that follows, not tease it
Lists and tables are self-contained units with labeled columns/items

Query-Answer Alignment Checklist

Primary entity is explicitly defined in the first 200 words
Page directly answers at least one of the six query types
FAQ schema present if the page answers multiple related questions
Schema markup type matches the page content category
Key facts (dates, numbers, names) are in the first 30% of the page

◆Run this checklist on your five highest-priority pages first: the pages you most want to appear in AI-generated responses. Fix the crawl and extraction layers before the chunk layer; a crawl failure makes all chunk optimization irrelevant.

AI Visibility Engineering

Chunk Engineering: Designing Content for AI Extraction Query Type Alignment: Writing for Definitional, Procedural, and Evaluative Queries Retrieval Simulation: Modeling AI Behavior Without Live Queries

AI Visibility Engineering — Part 10 of 10

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