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

Entity SEO 8 min readApril 30, 2026

How to Build a Knowledge Graph That AI Systems Trust

A well-built knowledge graph is the foundation of AI visibility. This guide explains what knowledge graphs are, why they matter for AI retrieval, and how to build one for your domain.

A knowledge graph is a structured representation of entities — named, real-world objects — and the typed relationships between them. Google maintains the Google Knowledge Graph, which contains hundreds of billions of facts about entities. Wikidata contains over 100 million entity records. When AI systems process your website, they attempt to build a partial knowledge graph of your domain: a map of the entities you describe, the relationships between them, and the confidence with which those entities can be identified. The strength of this local knowledge graph directly determines your AI visibility.

What Is a Domain Knowledge Graph?

A domain knowledge graph is the set of entities described on your website, their attributes, and the relationships between them. For a SaaS company like SiteNexis, the domain knowledge graph includes: the Organisation entity (SiteNexis), its product entities (the audit modules), conceptual entities (Machine Trust Score, AI Visibility, GEO Score), and person entities (team members and authors). Each entity has attributes (name, type, description, founding date) and relationships to other entities (SiteNexis offers Machine Trust analysis; Machine Trust is a component of AI Visibility).

How AI Systems Use Your Knowledge Graph

AI retrieval systems use entity graphs to answer three types of queries: entity-based queries ("What is SiteNexis?"), attribute queries ("When was SiteNexis founded?"), and relationship queries ("What does SiteNexis analyse?"). For each query type, the AI system looks for an entity record that matches the query subject, then retrieves the relevant attributes or relationships. If your entity record is incomplete, inconsistent, or disconnected from the broader knowledge graph through sameAs links, the AI system's response will be vague, inaccurate, or absent.

The Five Elements of a Strong Knowledge Graph

1Entity completeness: every key entity on your domain has a name, type, description, and key attributes defined.
2Attribute consistency: entity attributes are identical across schema markup, body text, and all pages where the entity appears.
3External validation: sameAs links connect your entities to external authority sources (Wikipedia, Wikidata, LinkedIn).
4Relationship density: entities are connected to each other through typed relationships that are evidenced in body text.
5Disambiguation: each entity is clearly distinguishable from entities with similar names using unique attributes.

●The SiteNexis AI Perception Graph visualises your domain knowledge graph as AI systems see it — nodes are entities, edges are typed relationships, and node size reflects citation readiness. Use it to identify disconnected entities and missing relationships.

Building Your Knowledge Graph: Step by Step

1Identify your primary entity: your organisation, product, or personal brand. This is the root node of your graph.
2List all secondary entities: products, services, concepts, people, locations that your content covers.
3Define each entity's attributes: type, name, description, founding date (for organisations), category.
4Map the relationships: for each entity pair, state the typed relationship (offers, isA, partOf, authorOf).
5Implement in schema: use Organisation, Product, Person, Concept schema with sameAs and @id properties.
6Add sameAs links: connect each entity to at least one external authority source that confirms its identity.
7Cross-check consistency: verify that every entity attribute in schema also appears verbatim in body text.

Knowledge Graph Metrics to Track

Knowledge graph health can be measured through four metrics: entity count (how many unique named entities are defined on your domain), entity confidence score (weighted average of consistency and disambiguation scores across all entities), graph density (ratio of typed relationships to total entities), and external validation rate (percentage of entities with at least one sameAs link to an external authority source). SiteNexis tracks all four metrics across audit runs, enabling you to measure knowledge graph growth over time.

Tags: Knowledge Graph Entity SEO Schema Markup AI Visibility

All articles Run free audit