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.

Back to Blog

Strategy 6 min readMay 5, 2026

Voice Search SEO: Structuring Content for AI Audio Retrieval

Voice queries have fundamentally different structural requirements than text queries. Most sites are structurally invisible to voice assistants.

Voice assistants — Siri, Google Assistant, Alexa, and their AI-enhanced successors — retrieve content differently from chat-based AI systems. They operate under tight answer length constraints (typically under 30 words for spoken responses), prioritize direct factual claims that can be stated aloud without context, and rely heavily on structured data for source selection. A site optimized for text-based AI retrieval is not automatically optimized for voice retrieval.

How Voice Assistants Select Content

Voice assistant source selection heavily weights the presence of speakable schema, the directness of answers, and the structured data completeness of business entities. A query for "What is [Company]'s phone number?" will return the answer from Organization schema, not from body text. A query for "Explain [Concept] simply" will look for content explicitly marked as speakable with a clear, concise definition. Text that requires visual presentation — tables, diagrams, lists — is systematically deprioritized.

The Speakable Schema Property

The SpeakableSpecification schema property tells voice assistants exactly which sections of your content are suitable for audio delivery. When implemented correctly, it identifies specific CSS selectors or xPath expressions that point to the retrievable sections. Effective speakable markup targets: the introductory definition in the first paragraph, direct answers to FAQ entries, key factual claims stated in a single sentence. Each speakable section should be under 30 words and self-contained.

◆Write your page's first paragraph as a direct, 20-25 word definition of the primary topic. This single section, when marked as speakable, can capture voice queries for the entire topical cluster your page covers.

Local Business Voice Optimization

For local businesses, voice optimization is tied directly to NAP consistency (Name, Address, Phone) across schema, Google Business Profile, and all directory listings. Voice assistants cross-reference these signals before delivering a spoken answer. Any inconsistency — a different address format, an old phone number, a different business name variant — creates a validation failure that can prevent voice retrieval entirely.

Voice Optimization Checklist

1Add SpeakableSpecification schema to pages with concise, self-contained factual claims
2Write FAQ answers in under 30 words, in a form that reads naturally aloud
3Ensure Organization / LocalBusiness schema name matches Google Business Profile exactly
4Verify NAP consistency across all directory listings
5Structure key definitions as standalone sentences in the first paragraph of each section

Tags: Voice Search AI Assistants Speakable Schema Local SEO

All articles Run free audit