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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Strategy 6 min readApril 24, 2026

Local Business AI Visibility: Schema, NAP Consistency, and Voice Search

Local businesses have unique AI visibility requirements. Voice queries, LocalBusiness schema, and NAP consistency are the three pillars of local AI search presence.

Local businesses face a distinct AI visibility challenge. A majority of local business queries arrive through voice assistants, Siri, Google Assistant, Alexa, and are answered directly from structured data rather than web content. "What time does [business] open?" "Is [business] open now?" "What is [business]'s phone number?" These queries never generate a click. They are answered from LocalBusiness schema, Google Business Profile data, and directory listings. For local businesses, AI visibility is primarily a structured data problem, not a content strategy problem.

The Three Pillars of Local AI Visibility

1LocalBusiness schema completeness: name, address, telephone, openingHours, geo coordinates, priceRange, and sameAs links all implemented accurately.
2NAP consistency: Name, Address, and Phone (NAP) are identical across Google Business Profile, LocalBusiness schema, Yelp, Apple Maps, and all directory listings.
3Voice query structure: FAQ answers and direct factual statements formatted for sub-30-word audio delivery, marked with speakable schema.

NAP Consistency: Why It Matters More Than You Think

AI systems that answer local queries cross-reference structured data from multiple sources: your website schema, Google Business Profile, Yelp, Apple Maps, Bing Places, and major industry directories. When the same business appears with different addresses, phone number formats, or name variants across these sources, AI systems flag the inconsistency as a trust degradation signal. A business named "The Coffee House" on its website but "Coffee House LLC" in Yelp creates a disambiguation failure. A phone number formatted as (212) 555-0100 on the website and 212-555-0100 in Google Business Profile creates a validation conflict. Both reduce citation confidence for that entity.

LocalBusiness Schema Implementation Guide

1Use @type LocalBusiness or a specific subtype (Restaurant, Dentist, LegalService) — always more specific than the generic type.
2Set name to exactly the same string used in Google Business Profile, without legal suffixes unless they appear in the GBP name.
3Include address as PostalAddress with all sub-fields: streetAddress, addressLocality, addressRegion, postalCode, addressCountry.
4Add telephone in E.164 format (+12125550100) and also in the local display format (212-555-0100) as the alternateName.
5Add openingHoursSpecification with dayOfWeek, opens, and closes for every operating day.
6Add sameAs links to Google Maps, Yelp, TripAdvisor (if applicable), and industry-specific directories.

▲The single most common local AI visibility failure is a business that has been relocated but has not updated all directory listings. A single old address in a major directory is enough to create a validation conflict that reduces voice assistant citation confidence.

Voice Search Query Optimisation for Local Businesses

Voice queries for local businesses follow predictable patterns: hours, location, contact, and services. Each answer should be under 30 words, formatted as a complete sentence, and marked with speakable schema. "The Coffee House on Park Avenue is open Monday through Saturday, 7am to 8pm, and Sunday 8am to 6pm." is a perfect voice-retrievable answer. The full sentence including the entity name, the operating type, and the specific attribute value makes it self-contained and directly speakable.

Tags: Local SEO Local AI Visibility Voice Search LocalBusiness Schema

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