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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Machine Trust 8 min readMay 22, 2026

What Is Machine Trust Score and Why It's Replacing Domain Authority

Domain Authority was a proxy for link equity. Machine Trust Score measures something entirely different — and more important for AI-driven search.

Domain Authority, as a metric, models the link equity flowing through a domain — a proxy for how much trust the web's link graph assigns to a source. For years, this was a reasonable approximation of search authority. But AI retrieval systems do not operate on link graphs. They operate on information graphs: networks of entities, claims, and validation signals. In this model, a site with Domain Authority 85 but inconsistent entity data and contradictory schema will underperform a site with DA 40 that has impeccable information consistency.

How AI Systems Form Trust

Trust in AI systems is a composite formed from cross-source validation, entity consistency, internal coherence, and the absence of contradictory claims. It is not formed from a single signal — it is accumulated through the convergence of many signals across many pages over time. A single contradiction on a high-value page can undermine the trust accumulated by dozens of well-optimized pages. Trust is asymmetric: easy to lose, slow to rebuild.

The Five Components of Machine Trust

1Entity Credibility Consistency (30%): Entity attributes consistent across all pages, schema, and external sources
2Schema Trust Alignment (20%): Schema markup accurately describes page content without over-claiming
3External Validation Depth (25%): sameAs links resolve to expected entities with confirmed attributes
4Contradiction Absence Score (15%): No factual conflicts between pages or between schema and body text
5Trust Degradation Resistance (10%): No signs of previous trust signals being removed or changed

Trust vs. Authority: The Key Difference

Authority, in the traditional SEO sense, flows from external links. A single high-authority backlink can significantly move Domain Authority. Machine Trust cannot be acquired this way. It is built from the inside out, from the quality and consistency of information on your own domain. No external link can compensate for contradictory entity data or schema that misrepresents your content. This fundamentally changes the optimization calculus for AI visibility.

●Machine Trust is measured per-domain, not per-page. Inconsistent data on a single important page can drag down the trust score for your entire domain, not just that page.

Building Machine Trust

1Audit all pages where your primary entity is described — ensure zero attribute conflicts
2Add sameAs links to authoritative external sources that validate your entity claims
3Review schema markup across your entire domain for over-claiming and type mismatches
4Implement a content review process that flags entity attribute changes
5Add dateModified schema to all important pages and update it when content changes

Tags: Machine Trust Domain Authority AI Search Trust Signals

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