VISEON: Glossary

Agentic AI

AI systems that act, not just answer: autonomous agents that plan, call tools, retrieve data and complete multi-step tasks on a user’s behalf. VISEON makes an organisation legible and actionable to these agents – a governed entity graph they can discover (ARD, MCP Registry), query (MCP) and transact with (structured offers and commerce protocols) – rather than guessing through forms built for humans.

Also known as: AI Agents, Autonomous AI, Agentic Systems

Agentic Catalogue

Agentic Catalogue: A unified, machine-actionable knowledge graph specifically engineered for autonomous AI discovery, interaction, and context retrieval. Evolving beyond traditional catalogues built for human browsing or basic data indices, an agentic catalogue consolidates multi-domain enterprise footprint variables (including ERP, CRM, MDM, and proprietary Intent Layers) into strict Schema.org-compliant structures. By leveraging Semantic Personification, it encodes not only structural records, but a brand’s signature voice, pedagogical methodologies, and mechanical constraints, allowing external AI systems to parse, trust, and precisely represent the entity.

Also known as: Digital Catalogue, Semantic Catalogue, Agent Ready Graph

Agentic Commerce

AI agents perform complex, multi-step tasks such as shopping: comparing products, negotiating, and making buying decisions, including payments, within automated workflows, often resulting in disintermediation of traditional e-commerce platforms.

Also known as: AI Search Channel, AI Shopping

Agentic Commerce Protocol

An open, machine-readable orchestration protocol that governs multi-step interactions between autonomous software agents and service environments across the “Transact” pillar. ACP transforms standard digital interfaces into agent-actionable environments by formalizing compliance rules, temporal pricing structures, and real-time fulfilment data (Available To Promise/ATP). Backed by modern enterprise infrastructure and semantic data platforms, ACP ensures that transactions are validated dynamically through the knowledge graph, shifting interactions from manual checkouts to fully autonomous, zero-neuron Discovery to Dispatch workflows while maintaining strict merchant oversight. See ACP as an integration protocol, ‘EDI for AI’. Supported by payment infrastructure providers like Visa (Intelligent Commerce), Mastercard (Agent Pay), and PayPal (Agent Toolkit).

Also known as: ACP, Agentic Payments Protocol, Discovery-to-Dispatch Protocol

Agentic Data Platform

A bimodal data architecture (ADP) designed to serve structured, high-fidelity knowledge to both humans and autonomous agents. It utilises Semantic MDM, GraphRAG, and MCP protocols to resolve digital obscurity and enable Agent-to-Agent discovery, negotiation, and commerce.

Also known as: ADP, VISEON Platform

Agentic Engine Optimisation

The practice of making an organisation’s content and data readable, trustworthy and actionable for AI agents and AI-driven search interfaces, rather than tuned for keyword-based legacy search indexing. VISEON’s enterprise guide to agentic search optimisation presents it as the successor to traditional SEO and puts it into practice through a governed knowledge graph served from the edge.

Also known as: Agentic Engine Optimization, AEO

Agentic Resource Discovery

An open specification, announced in June 2026, that lets AI agents find tools, MCP servers, APIs and other agents at runtime. A publisher hosts a manifest at /.well-known/ard.json on its domain; registries crawl it, agents search them by intent, verify the publisher and then connect over MCP, A2A or an API.

Also known as: ARD, ard.json, ARD manifest

Agentic Search

A governed information-retrieval and interaction architecture through which humans or autonomous agents can navigate, verify, and use structured knowledge using natural-language requests.

In VISEON, Agentic Search is controlled by a Stateful Behaviour Tree rather than handed directly to a language model. The SBT identifies the semantic task, consults validated state and governing contracts, and selects the appropriate execution path.

That path may be:

• an exact knowledge graph or KV answer;
• deterministic relationship traversal;
• an authorised tool or source-system operation;
• GraphRAG evidence retrieval;
• or bounded AI synthesis where language generation is genuinely required.

The language model does not own routing, state, evidence authority, or permissions. Responses can retain evidence documents, graph node identifiers, relationship identifiers, and execution provenance so that exact answers can be distinguished from retrieved or synthesised answers.

Also known as: AI Agent Search, Governed Agentic Search, Semantic Interaction

Agentic Web

An evolution of the World Wide Web where AI agents autonomously discover, interpret, and interact with web content and services on behalf of users or organizations. Built on protocols like MCP (Model Context Protocol) and NLWeb, enabling agent-to-agent and agent-to-service communication through structured data formats like Schema.org and JSON-LD.

Also known as: Open Agentic Web (Microsoft terminology)

Status: Emerging concept (2025), not yet standardised

Key difference from Semantic Web: Semantic Web makes data machine-readable; Agentic Web makes services agent-actionable

AI and Data Engineering

The discipline of preparing and structuring data so AI systems can consume it reliably – the engineering that turns raw content into machine-readable, governed, trustworthy inputs. It is the core competency behind VISEON: Differentia Consulting’s AI data engineers build the governed Schema.org knowledge graph that makes an organisation legible to AI agents, rather than leaving models to infer meaning from unstructured pages.

Also known as: AI Engineering, Data Engineering, AI/ML Engineering

AI Discoverability

The capability of content to be found, understood, and recommended by AI-powered search engines and generative AI systems. Brands without AI discoverability face digital obscurity in the age of generative search.

Also known as: Generative AI Visibility, AI Search Optimisation

AI Discoverability Assessment

A comprehensive evaluation of how effectively artificial intelligence systems and search engines can discover, understand, and retrieve digital content.

Also known as: AI Discovery Audit, AI Visibility Analysis, AI Readiness Assessment

AI Hallucination

When an AI model generates content that is not in its input or in any real source: an invented citation, a fabricated product feature, a confident answer with no basis. It is an output problem, and it can sometimes be caught by grounding or retrieval checks. It differs from Semantic Equivocation, where the model acts faithfully on input whose meaning was already misaligned.

Also known as: Hallucination, LLM Hallucination, Confabulation

AI Overviews

Google’s AI-written summaries at the top of search results (formerly the Search Generative Experience). Instead of a list of links, Google generates an answer to the query and cites the sources it drew on. Sources with clear, structured Schema.org markup are easier for the system to recognise as authoritative and to cite, so structured data raises a page’s chance of being used in the answer.

Also known as: Google AI Overviews, AI Overview, Search Generative Experience, SGE

AI Strategy

A comprehensive framework defining how organisations adopt, implement, and leverage artificial intelligence technologies to achieve competitive advantage, operational efficiency, and strategic business goals.

Also known as: Artificial Intelligence Strategy, AI Implementation Strategy, Enterprise AI Strategy

AI-Ready Data

Data whose quality, lineage and compliance are evidenced well enough for AI systems and agents to act on it without inheriting its errors.

Also known as: AI Data Readiness, Data Readiness for AI, Trusted Data for AI

Analytics

The systematic analysis of data to find patterns and inform decisions. Increasingly this happens through AI agents querying governed data directly, which only works if the meaning is sound. VISEON for Qlik (vCat) governs what measures and dimensions mean, and VISEON Data Assurance tests the data itself, so agent-driven analytics is trustworthy.

Also known as: Data Analytics, Business Analytics, Analytical Methods, Data Analysis, Quantitative Analysis, Statistical Analysis, Predictive Analytics

autoMagically

Automation that happens seamlessly and effortlessly, combining automated processes with intelligent orchestration. Essential for hyperautomation and agentic RPA implementations.

Also known as: Automagic, Automatic and Magical

Automation Technology

Technologies that enable processes to operate automatically with minimal human intervention, from basic task automation to intelligent decision-making systems.

Also known as: Process Automation, Intelligent Automation, Automation Systems

Available to Promise

A supply chain and commerce commitment representing the quantity of a product or service that can be promised to a customer for delivery within a specified time window, based on current inventory, incoming supply, and committed demand. In agentic commerce, ATP is expressed as machine-readable Schema.org properties — including availabilityStarts, availabilityEnds, deliveryLeadTime, inventoryLevel, and priceValidUntil — enabling AI agents to autonomously verify fulfilment commitments before executing a transaction. ATP formula: (Quantity On Hand + Incoming Supply) minus Committed Demand.

Also known as: ATP, Available-to-Promise, ATP Transparency

Blackboard

A governed runtime state structure shared by the branches and leaves of a Stateful Behaviour Tree.

The Blackboard retains validated information needed to continue an interaction coherently, such as the active task, selected method, committed user intent, current knowledge entities, retrieved evidence, provenance identifiers, unresolved questions, and the interaction surface currently being presented.

Unlike a conventional chat history, the Blackboard does not treat assistant prose as an authoritative source of state. Information is admitted, validated, updated, and consumed according to explicit runtime contracts.

The Blackboard provides interaction continuity and semantic state. It does not replace authentication, authorisation, or role-based access control. Underlying source systems remain authoritative for user identity, permissions, and data access.

Also known as: SBT Blackboard, Semantic State Store, Governed Interaction State

Business Intelligence

The tools and practices for turning data into reporting and insight for decision-making. As BI shifts to natural-language and agent interfaces, its reliability depends on governed meaning: one canonical definition per measure. VISEON for Qlik (vCat) governs the semantics of master measures and dimensions so AI answers over BI estates are consistent and verifiable.

Also known as: BI, Analytics, Business Reporting

Category Theory

A branch of mathematics that describes structures purely in terms of objects and the relationships (morphisms) between them, and how those relationships compose. It is the formal underpinning of why knowledge graphs work: meaning is carried by relationships, not by things in isolation. VISEON applies this categorical view – canonical entities connected by typed, composable relationships with preserved structure – so AI can reason over an organisation’s meaning rather than match strings.

Change Data Capture

A data integration method that detects changes in a source system, such as a new price or stock level, and streams them to other systems as they happen, instead of copying whole data sets on a schedule. In VISEON, constants such as who you are and what you offer are declared in Schema.org, while transients such as price and quantity are delivered in real time through CDC-based tools, so answers stay live.

Also known as: CDC, Real-time data replication, Change data streaming

Cluster Article

Supporting content addressing specific subtopic, linked to pillar via subjectOf property and establishing parent-child relationship through isPartOf. Forms semantic network that AI agents can traverse.

Also known as: Supporting Content, Spoke Content, Subtopic Article

CMS Compatibility

The ability of software, plugins, or services to work seamlessly with various Content Management Systems without conflicts or technical limitations.

Also known as: Content Management System Compatibility, Platform Compatibility, CMS Integration

Commerce & Protocols

Standardised protocols and frameworks that enable digital commerce transactions, integrations, and automated purchasing across platforms and systems.

Also known as: Digital Commerce Protocols, E-commerce Standards, Commerce Integration Protocols

Company Background

Information about an organisation’s history, mission, values, expertise, and development over time.

Also known as: Company History, About Us, Company Overview

Competitive Advantage

The attributes or capabilities that allow an organisation to outperform its competitors through superior value delivery, differentiation, or operational efficiency.

Also known as: Market Advantage, Strategic Advantage, Business Edge

Compliance

Adherence to laws, regulations, standards, and internal policies governing business operations, data handling, and industry-specific requirements.

Also known as: Regulatory Compliance, Legal Compliance, Standards Compliance

Content Strategy

The planning, development, and management of content to achieve specific business objectives through structured information architecture and distribution.

Also known as: Content Planning, Content Organisation, Content Architecture

Content to Context Gap

The gap between an organisation’s content, written for humans to read, and the structured, machine-readable context an AI agent needs to find, understand and recommend it. AI agents do not read pages: they query structured data, traverse entity relationships and build answers from knowledge graphs. A digital footprint made only of human-readable content has no digital twin for them to find. VISEON’s Content to Context (C2C) process closes the gap by converting unstructured website content into AI-discoverable knowledge graphs.

Also known as: C2C Gap, Content-to-Context Gap, C2C

Context Gravity

The tendency for governed, authoritative, and well-connected semantic context to attract retrieval, interpretation, and agent attention toward the correct entities, relationships, definitions, and evidence. Strong Context Gravity reduces ambiguity by making the intended meaning easier for humans and AI systems to discover, select, and retain.

Context Gravity is the semantic-era evolution of Data Gravity: where Data Gravity attracts applications and services toward accumulated data, Context Gravity attracts retrieval and agent reasoning toward authoritative meaning.

Also known as: Semantic Context Gravity, Contextual Authority, Contextual Attraction

Cost Reduction

Strategies and measures implemented to decrease operational expenses whilst maintaining or improving service quality and business outcomes.

Also known as: Cost Savings, Cost Optimization, Expense Reduction

Data & Intelligence

The collection, processing, and analysis of data to generate actionable business insights and support strategic decision-making.

Also known as: Data Intelligence, Business Data Intelligence, Analytical Intelligence

Data Architecture

The design of how an organisation’s data is structured, integrated and served. For the agentic web, VISEON adds the missing layer: a bimodal, agent-ready architecture that serves governed, high-fidelity entities to both people and autonomous agents – a knowledge graph with canonical identity, relationships and provenance – over MCP and the web.

Also known as: Enterprise Data Architecture, Data Design, Information Architecture

Data Audit

A systematic assessment of data against rules, standards or regulations, producing evidence of what the data can and cannot demonstrate.

Also known as: Data Auditing, Data Quality Audit

Data Compliance

Evidence that data is collected, held and used in line with applicable regulations and standards, such as GDPR, ISO/IEC 27701 and BCBS 239.

Also known as: Data Regulatory Compliance, Data Protection Compliance

Data Governance

The framework of policies, ownership and controls that keep data accurate, consistent, trustworthy and compliant. VISEON extends governance to the outward-facing semantic layer: the entity definitions AI agents and search engines read at the organisation’s boundary – one canonical definition per entity, with provenance and change control – so machines meet the same governed meaning humans do.

Also known as: Data Management Governance, Information Governance, Data Stewardship

Data Influencer

Recognised thought leaders and content creators who shape discourse, trends, and best practices in data science, analytics, business intelligence, and AI domains.

Also known as: Data Thought Leader, Analytics Influencer, Data Science Influencer, BI Influencer, Data Community Leader, Data Industry Expert

Data Lineage

The record of where data comes from and what is built from it, so a problem in an output can be traced back to its source.

Also known as: Data Provenance, Data Dependencies

Data Observability

Continuous monitoring of data for changes in volume, schema, freshness and quality, so that incidents surface when they happen rather than when a report breaks.

Also known as: Data Monitoring, Data Reliability

Data Quality

The degree to which data is fit for its intended use, measured across dimensions such as completeness, validity, uniqueness, consistency, accuracy and timeliness.

Also known as: Data Quality Management, DQ, Fitness for use

Data Quality Assurance

The practice of continuously measuring data against defined quality rules, evidencing the result, and acting on failures before they reach reports, regulators or AI systems.

Also known as: DQA, Data Assurance, Data Quality Testing

Data Science

The interdisciplinary practice of extracting insight from data using statistics, programming and domain knowledge. In the agentic era its outputs are increasingly consumed by AI agents, which makes governed meaning essential: canonical entities, definitions and provenance. VISEON governs that semantic layer so data-science outputs are discoverable and correctly interpreted by machines.

Also known as: Data Analytics Science, Applied Data Science, Computational Data Science, Statistical Data Science, Machine Learning Science, Data Mining

Data Strategy

A comprehensive plan defining how an organisation will collect, manage, analyse, and leverage data assets to achieve business objectives, competitive advantage, and operational efficiency.

Also known as: Enterprise Data Strategy, Data Management Strategy, Information Strategy

Decision Intelligence

The discipline of connecting data and models to real decisions and actions. It depends on a trustworthy semantic foundation: agents and models must share one governed meaning of entities and metrics. VISEON supplies that foundation – a canonical, provenance-backed knowledge graph – so automated and augmented decisions rest on verifiable facts, not ambiguous data.

Also known as: DI, Decision Science, Intelligent Decision-Making

Deterministic Answer Path

An execution route that produces an exact answer without invoking a generative language model when the required facts, relationships, calculations, or rules are already available in governed systems.

A deterministic answer may be produced from a knowledge graph node, a relationship traversal, a validated KV contract, a calculation engine, a policy rule, or an authoritative source-system response.

Within VISEON DISCUSS, the Stateful Behaviour Tree checks for an exact deterministic path before admitting GraphRAG retrieval or bounded AI synthesis. This reduces latency, model cost, linguistic drift, and unnecessary inference.

“Zero-neuron” refers specifically to avoiding LLM inference. It does not imply that no computing resources are used.

Also known as: Exact Answer Path, Zero-Neuron Path, Deterministic Leaf

Differentia Consulting

A specialist AI, BI and ERP consulting firm providing expertise in semantic technology, structured data, and digital content optimisation strategies.

Also known as: Differentia

Digital Homogenisation

The effect of brands being described in generic, interchangeable language despite having meaningful differences. A brand can be findable yet poorly differentiated when its distinctive capabilities, methods and evidence are missing from the retrieved information. VISEON addresses this by making those distinctions explicit and connected in the knowledge graph. This differs from digital obscurity, where the organisation is absent or inaccessible in the relevant discovery workflow.

Also known as: Digital Homogenization, Brand Homogenisation, The Tin Man Problem

Digital Marketing

Marketing strategies and tactics executed through digital channels including search engines, websites, social media, and other online platforms.

Also known as: Online Marketing, Internet Marketing, Web Marketing

Digital Obscurity

The state of being invisible or undiscoverable to AI-powered search engines and generative AI systems due to lack of structured data. Digital obscurity is the new invisibility – brands without semantic markup are effectively invisible to AI-powered discovery.

Digital Twin

A machine-readable virtual representation of a real-world system kept in sync with it. VISEON builds a Language Model Optimised (LMO) digital twin of a brand’s public footprint: every entity – organisation, products, services, people, concepts – as a canonical, governed node in a Schema.org knowledge graph, continuously aligned to source via change data capture, so AI systems reason over an accurate mirror of the organisation.

Also known as: Digital Footprint

Diversity Maximisation

An objective in information retrieval and AI content selection that prioritises including sources with distinct, non-redundant information rather than multiple sources expressing the same facts. Algorithms such as GIST use diversity maximisation to reduce compute costs and improve response quality by selecting a maximally informative subset of available content.

Also known as: Diversity Maximization, Result Diversity, Semantic Diversity Optimisation

Enterprise Semantic Layer

The machine-readable, standards-based representation of an organisation’s external identity, products, and services — distinct from internal data governance platforms such as enterprise data catalogues, which address internal metadata management.

Also known as: Semantic Layer, External Semantic Layer

Enterprise Solutions

Comprehensive technology solutions designed to meet the complex needs of large organisations, typically featuring scalability, security, and integration capabilities.

Also known as: Enterprise Software, Enterprise Systems, Business Solutions

Entity Identifiers

Unique identifiers assigned to entities (people, places, organisations, concepts) that enable consistent recognition and linking across different platforms and knowledge bases.

Also known as: Entity IDs, Unique Identifiers, Entity References

Explicit Referenceability

Whether an organisation’s brand, products, services and capabilities are declared in structured form: typed with a Schema.org @type, identified with a canonical @id, described with machine-readable properties and connected by explicit graph edges to related entities. The alternative, implicit referenceability, is what an AI agent could infer by reading the content as a human would; but inference is unreliable, because agents traverse declared relationships and typed entities rather than guess. Each declared entity is a waypoint on the map that the organisation’s digital twin occupies.

Also known as: Implicit Referenceability, Implicit vs Explicit Referenceability

First Mover

The competitive advantage gained by being the first to enter a new market or adopt a new technology, often resulting in brand recognition and market share leadership.

Also known as: First-Mover Advantage, Market Pioneer, Early Adopter

Generative AI

AI that produces new content – text, images, code – from learned patterns. For organisations it is also the new discovery surface: people and agents increasingly ask ChatGPT, Claude, Gemini and Perplexity instead of searching. VISEON governs what these systems can say about a brand by serving them a validated entity graph, turning generative AI from a hallucination risk into a governed channel.

Also known as: Generative Artificial Intelligence, Gen AI

Generative Engine Optimisation

The practice of trying to influence what generative AI engines write about an organisation, mainly through content. Because those engines assemble answers by retrieval-augmented generation (RAG), which is probabilistic and costly, the results vary from one answer to the next.

Also known as: GEO, Generative Engine Optimization, SEO 2

Getting Started

The initial steps and processes required to begin using a service, platform, or implementing a solution.

Also known as: Onboarding, Initial Setup, Beginning Guide

GIST – Greedy Independent Set Thresholding

A data selection algorithm, adopted by Google for search and introduced by Matthew Fahrbach and colleagues, that chooses a training subset by balancing diversity (avoiding redundant examples) and utility (keeping examples relevant to the task), with a mathematical guarantee on solution quality. VISEON references GIST as a published methodology for improving how well AI understands a website – an objective of VISEON for Web – not a VISEON product or proprietary method.

Also known as: GIST, Greedy Independent Set Thresholding, GIST Algorithm

Google Rich Results

Enhanced search result displays in Google that include additional visual or interactive features beyond standard title, URL, and description, enabled through structured data markup to improve visibility and click-through rates.

Also known as: Google Rich Snippets, Enhanced Search Results, Rich Results, Rich Cards, Rich Snippets

Graph Reciprocity

The principle that Schema.org relationship properties which exist as formal inverse pairs must be declared in both directions to ensure full graph traversability. When an Article declares it is about a DefinedTerm via the about property, that DefinedTerm must declare the Article as its subjectOf. Without reciprocity, the graph is only traversable in one direction and an AI agent approaching from the opposite side finds a dead end. Key inverse pairs include about/subjectOf, hasPart/isPartOf, mainEntity/mainEntityOfPage, member/memberOf, alumni/alumniOf, and isBasedOn/isBasisFor.

Also known as: Inverse Edge Reciprocity, Bidirectional Edge Validation, Closed Inverse Edges

GraphRAG

A retrieval architecture that answers a question by traversing the explicit, typed relationships between canonical entities in a knowledge graph, rather than only matching vector-similar passages of text. Where vector RAG returns chunks that look similar to the query, GraphRAG follows named edges between @id nodes (about, mentions, worksFor, isPartOf) to assemble relationship-aware, provenance-backed context. In VISEON, GraphRAG runs over a governed Schema.org knowledge graph, which is what lets VISEON Ask and the MCP endpoint return deterministic, citable answers instead of probabilistic paraphrase.

GraphRAG can identify connected entities, traverse meaningful relationships, and retrieve supporting evidence that ordinary vector similarity may overlook.

Within VISEON, GraphRAG is an evidence capability, not the runtime controller and not the source of every answer. The Stateful Behaviour Tree determines whether GraphRAG is needed, which graph and content sources may be queried, and how the retrieved evidence may be used.

Exact knowledge graph, KV, relationship, calculation, or policy answers are resolved deterministically where possible. GraphRAG is used where broader supporting context is required. Bounded AI may then explain or synthesise the selected evidence without becoming the authority for the underlying facts.

Also known as: Graph-based RAG, Graph-Guided Retrieval, Relationship-Aware Retrieval

Grounded Authority

VISEON’s registry of canonical fact for business content that never became data: contracts, policies, terms, obligations and operating rules. The content is read, resolved against the organisation’s ontology and registered in one canonical, machine-checkable form, certified by the organisation as registrant and qualified continuously, so that AI operations teams and agents can act on it. Retrieval and tagging are not enough: an embedding returns a similarity score, not a fact, and a tag records that a document exists, not what it says.

Also known as: Grounded Authority for the Enterprise

Hyperautomation

Business-driven approach to rapidly identify, vet, and automate business and IT processes through orchestrated use of multiple technologies including AI and RPA.

Implementation

The process of putting a system, solution, or technology into operation within an existing infrastructure or workflow.

Also known as: Deployment, Integration, Installation

Industry Trends

Observable patterns and directional movements in technology, business practices, and market dynamics that shape industry evolution and strategic planning.

Also known as: Market Trends, Industry Patterns, Sector Trends, Business Trends, Technology Trends, Market Movements, Industry Developments

Intent Layer

Structured declarations above the factual graph, built from PropertyValue nodes, DefinedTermSets and typed Actions, that encode what makes a brand distinctive.

Also known as: Brand Intent Layer, Knowledge Graph Intent Layer

International SEO

The practice of optimising websites to rank in multiple countries and languages, considering regional search behaviours, technical implementations, and cultural nuances.

Also known as: Global SEO, Multi-Regional SEO, Cross-Border SEO

Inverse Edges

Schema.org properties that exist as formal pairs relating the same entities in opposite directions. For example, about and subjectOf are inverse edges — one declares topical focus from content to concept, the other declares coverage from concept to content. Implementing both directions ensures AI agents can traverse the knowledge graph from any starting node. Without inverse edges, the graph functions as a collection of one-way streets, limiting discovery to a single direction of navigation. Requiring every such pair to be declared in both directions, so that the loop is closed, is called inverse edge closure; it also pairs mainEntity with mainEntityOfPage.

Also known as: Inverse Properties, Bidirectional Relationships, Reciprocal Properties, Inverse Edge Closure, Schema Inverse Edges

JSON-LD

JSON for Linking Data: a JSON-based serialisation of Schema.org structured data that expresses entities, their properties and their relationships as linked data an AI can traverse. In VISEON, JSON-LD is the on-the-wire format of the governed knowledge graph – each entity carries a stable @id, typed edges and provenance – served to search engines for Rich Results and to agents over MCP.

Also known as: JSON for Linking Data

Knowledge Graph

A machine-readable network of an organisation’s entities – its organisation, products, services, people and concepts – expressed as canonical, disambiguated nodes with stable @id identifiers, typed relationships and verified provenance. In VISEON, the knowledge graph is the governed Schema.org layer that AI search engines and agents traverse to discover, understand, cite and act on a brand directly, instead of inferring meaning from unstructured pages.

Knowledge Graph Cluster

A topic authority pattern established through entity relationships across multiple authoritative domains rather than single-site content structure. Knowledge graph clusters enable AI discovery through semantic proximity and validated entity connections, independent of traditional on-page signals or internal linking architecture. Represents the evolution of topic clustering for the agentic web.

Also known as: Entity Cluster, Graph-Based Topic Cluster, Semantic Authority Cluster

Language Model Optimisation (LMO)

The strategic discipline of governing and structuring an entity’s data — specifically its Nouns (Entities) and Verbs (Relationships) — to ensure maximum deterministic fidelity and citation accuracy within AI models and autonomous agents.

Technical Context: Unlike traditional SEO, which optimises for probabilistic search engine ranking, LMO focuses on the “Inference Layer” of AI. It utilises structured data protocols (such as JSON-LD, OSI protocol, and Knowledge Graphs) to move a brand’s identity from a “Variable” (calculated guess) to a “Constant” (verified fact).

By providing a Single Source of Truth (SSoT) through machine-readable catalogs, LMO reduces model hallucination and secures authoritative citations in agentic workflows. This infrastructure allows a brand’s identity to transition from a probabilistic variable to a verified constant, enabling qualified transactions and auditability for autonomous agents.

Also known as: LMO, Language Model Optimization

Large Language Model (LLM)

An AI model trained on vast text that generates and understands language probabilistically. In the VISEON architecture an LLM is a bounded component, not the source of truth: it expresses answers grounded in a governed knowledge graph and constrained by Stateful Behaviour Trees, so it cannot invent facts about the organisation.

Also known as: LLM, Language Model

Machine Learning

A branch of AI in which systems learn patterns from data to make predictions or decisions without being explicitly programmed for each case. In the VISEON architecture, machine learning is the probabilistic layer above a deterministic foundation: a governed knowledge graph supplies verifiable facts and relationships, so ML and LLM outputs are grounded and bounded rather than hallucinated.

Also known as: ML, Statistical Learning, Predictive Modelling

Maintenance

Regular activities performed to keep systems, software, or services operating efficiently, including updates, monitoring, troubleshooting, and optimisation.

Also known as: System Maintenance, Ongoing Support, Service Maintenance

Managed Services

The practice of outsourcing specific business functions or IT operations to a third-party provider who assumes ongoing responsibility for maintenance, monitoring, and optimisation.

Also known as: Managed IT Services, Outsourced Services, Service Management

Master Data Management (MDM)

The discipline of defining and managing an organisation’s critical shared data — customers, products, suppliers, and employees — as golden records: a single, authoritative source of truth across all systems and processes. The semantic strategy equivalent for outward-facing AI discoverability applies the same rigour to machine-readable knowledge graphs.

Also known as: MDM, Enterprise Master Data

Mechanical Personalisation

The practice of translating an organisation’s distinctive operational logic, methodologies, interaction rules, and constraints into governed, executable runtime contracts.

Within VISEON, Mechanical Personalisation is implemented across the semantic architecture:

• the knowledge graph describes entities, concepts, methods, and relationships;
• semantic contracts define meaning, authority, constraints, and coverage;
• governed stores such as KV retain editable policies and approved language;
• and the Stateful Behaviour Tree executes the applicable rules through explicit branches and terminal leaves.

This separation allows an organisation’s behaviour to remain consistent without expecting a language model to infer operational rules from prompts, documents, or brand tone.

Mechanical Personalisation governs how the system behaves. Authentication and access permissions remain the responsibility of the connected source systems.

Also known as: Deterministic Brand Logic, Behavioural Semantic Control, Governed Runtime Personalisation

Metrics

Quantifiable measures used to track, assess, and analyse the performance, progress, or success of business processes, campaigns, or objectives.

Also known as: Performance Metrics, KPIs, Key Performance Indicators

Microdata

An HTML attribute syntax for nesting Schema.org structured data inside page markup. Like RDFa it couples data to presentation; VISEON standardises on JSON-LD, where each entity is a governed, canonical node with a stable @id, decoupled from the page and served consistently to search engines and AI agents.

Also known as: HTML Microdata, Microdata Format

Model Context Protocol (MCP)

An open protocol that enables AI assistants to securely access data and tools through standardised server connections. MCP servers and tools allows AI systems to dynamically retrieve information from knowledge graphs and external data sources.

Also known as: MCP

Multi-Language

Support for content, interfaces, or services in multiple languages to accommodate diverse user bases across different linguistic regions.

Also known as: Multilingual, Multiple Languages, Language Variants

Natural Language Search

Search that lets a person ask in ordinary language and get a direct, resolved answer rather than a list of links. In VISEON, natural-language questions are answered by VISEON Ask: Stateful Behaviour Trees route the query through deterministic knowledge-graph answers and GraphRAG evidence, with bounded AI only for phrasing, so answers are grounded and citable.

Also known as: Conversational Search, NLP Search, Natural Language Query

NLWeb

Microsoft Research protocol for web-scale natural language understanding optimised for LLM ingestion through structured JSON-LD. NLWeb demonstrates how major AI research teams are prioritising structured data for training language models.

Also known as: Natural Language Web

Ontology

A formal, machine-readable specification of the concepts in a domain, their properties and the relationships and constraints between them – the agreed meaning that lets different systems and agents reason over the same data consistently. In VISEON, the enterprise ontology governs what entities and metrics mean across an organisation, federating platform ontologies, semantic views and catalogues into one canonical model.

Also known as: Web Ontology

Open Semantic Interchange

An open framework for the structured exchange of semantic knowledge between AI systems, platforms, and agents. Open Semantic Interchange defines how entities, their relationships, and contextual metadata are serialised, transmitted, and consumed across heterogeneous systems using JSON-LD and Schema.org vocabularies — enabling interoperability between knowledge graphs, LLMs, and agentic infrastructure.

Also known as: OSI, Semantic Interchange

Pedagogical Branding

The systematic practice of encoding an organisation\’s signature teaching philosophies, conceptual frameworks, and distinctive transfer of knowledge into a machine-readable format. Rather than simply classifying factual brand data, pedagogical branding structures the rules, stances, and step-by-step methodologies unique to an author or institution, allowing external artificial intelligence systems and conversational agents to replicate and instruct via those specific frameworks without losing the creator’s intent.

Also known as: Educational Brand Alignment, Methodological Branding, Authored Concept Architecture

Pillar Content

Comprehensive foundational page covering a broad topic, linked to cluster articles via Schema.org about property pointing to cluster @IDs. Serves as the authoritative hub for a topic area.

Also known as: Pillar Page, Hub Content, Cornerstone Content

Privacy

The protection of personal information and the right of individuals to control how their data is collected, used, shared, and stored by organisations.

Also known as: Data Privacy, Information Privacy, User Privacy

Product Schema

Structured data markup that provides search engines and AI systems with detailed product information including pricing, availability, reviews, and specifications.

Also known as: Product Markup, Product Structured Data, Schema.org Product

Project Timeline

A chronological representation of project milestones, deliverables, and key dates from initiation to completion.

Also known as: Project Schedule, Implementation Timeline, Delivery Schedule

Provenance Contract

A machine-readable record describing how a response, decision, or action was produced.

A Provenance Contract can identify the Stateful Behaviour Tree route and terminal leaf used, the evidence documents retrieved, the knowledge graph nodes and relationships traversed, the deterministic engine or external tool invoked, and whether a language model contributed to the final expression.

This allows a consuming system to distinguish between:

• an exact deterministic answer;
• a graph-derived relationship answer;
• a retrieved evidence answer;
• a bounded AI synthesis;
• and an unsupported or unresolved request.

Provenance is retained alongside the answer rather than represented only as explanatory prose. This makes responses more inspectable, testable, and suitable for machine consumption.

Also known as: Answer Provenance, Evidence Contract, Execution Provenance

RDFa

A W3C syntax for embedding structured-data attributes directly inside HTML elements. It is one way to express Schema.org; VISEON standardises on JSON-LD instead – a separate, clean block per entity with a stable @id – which is easier to govern, validate and serve to agents than markup interleaved with presentation.

Also known as: Resource Description Framework in Attributes, RDF in Attributes

Resource Description Framework (RDF)

Resource Description Framework: the W3C model for expressing data as subject-predicate-object triples, the formal foundation of linked data and knowledge graphs. VISEON works in the pragmatic Schema.org / JSON-LD expression of the same idea – typed entities and relationships with stable @id identifiers – so organisations get graph-grade meaning without adopting a full RDF/OWL stack.

Also known as: RDF

Retrieval Augmented Generation (RAG)

A method for grounding an AI model’s answer in retrieved source material rather than its training data. Classic RAG retrieves text passages by vector similarity, so the answer is bounded by what similarity happens to surface. In VISEON the retrieval step is GraphRAG over a governed knowledge graph, so the model is grounded in explicit entities and relationships with provenance, not just similar-looking text.

Also known as: RAG, RAG AI, VectorRAG

ROI

A performance measure used to evaluate the efficiency and profitability of an investment, calculated as the ratio of net profit to investment cost.

Also known as: Return on Investment, Investment Return, Financial Return

Schema Audit

A systematic examination of an organisation’s existing Schema.org markup across its domain to find the faults that break AI understanding: duplicate or conflicting @id references, orphaned nodes, missing relationships, markup that contradicts the page, and rich-result-only schema with no entity model. In VISEON this is the Semantic Entity Assessment – the diagnostic that precedes governance, with results in minutes.

Also known as: Schema Review, Structured Data Audit, Schema Analysis

Schema.org

The shared cross-industry vocabulary (maintained by Google, Microsoft, Yandex and the W3C community) for describing entities – organisations, products, people, articles, offers – in a way search engines and AI systems understand. In VISEON it is the vocabulary the governed knowledge graph is built from: Schema.org types and properties, serialised as JSON-LD, with canonical @id identity and verified relationships.

Search Engine Optimisation (SEO)

Search Engine Optimisation (SEO 1.0): the original discipline of optimising web pages to rank in search engine results through keywords, links and technical signals. Still valid for classic search and Google Rich Results, but now the first of three generations – Search (1.0) to Generative Engine Optimisation, GEO (2.0) to Semantic Entity Optimisation (3.0) – as discovery shifts from ranking pages for search engines to serving governed entities to AI agents. At VISEON, ‘SEO’ today means Semantic Entity Optimisation.

Also known as: SEO, Search Optimisation

Security

Measures and practices implemented to protect systems, networks, and data from unauthorised access, breaches, attacks, and other cyber threats.

Also known as: Cybersecurity, Information Security, Data Security

Semantic Brand Personalisation

Encoding brand identity (voice, philosophy, methods, coined language and audience relationships) into a Schema.org knowledge graph so AI agents represent the brand accurately and distinctively.

Also known as: Semantic Branding, Brand Personalisation for AI

Semantic Contract

A governed, versioned, machine-readable asset that defines the business meaning, authority, constraints, coverage, and permitted use of data or knowledge exposed through an interface, API, knowledge graph, or Model Context Protocol connector.

A Semantic Contract tells a consuming system not merely what fields or entities exist, but what they mean, which source is authoritative, how terms and metrics are defined, where coverage is incomplete, which relationships are valid, and which questions cannot be answered reliably.

Within a Stateful Behaviour Tree, Semantic Contracts are consulted before an execution path is selected. They help the SBT determine whether a question can be answered deterministically, requires evidence retrieval, needs clarification, must be delegated to an underlying system, or lies outside the governed knowledge boundary.

Semantic Contracts govern meaning and use. They do not grant user permissions or override the authentication and role-based access controls of underlying systems.

Also known as: Semantic Data Contract, MCP Semantic Layer, Governed Semantic Definition

Semantic Control Plane

A governed architectural layer that controls how organisational meaning, context, authority, provenance, rules, and machine-readable contracts are exposed to and used by AI systems and agents.

A Semantic Control Plane coordinates semantic contracts, knowledge graphs, Stateful Behaviour Trees, governed retrieval, deterministic engines, and bounded AI. It determines which source is authoritative, which execution path is permitted, what evidence supports an answer, and where the reliable knowledge boundary ends.

It governs semantics and machine behaviour, not user permissions. Authentication, authorisation, role-based access control, and data-access rights remain authoritative in the connected source systems.

Also known as: Enterprise Semantic Control Plane, Governed Semantic Control Plane, Semantic Decisioning Layer

Semantic Drift

An established term in ontology research describing how the meaning of concepts within an ontology gradually changes across versions over time as a domain evolves. Semantic drift is measurable by comparing concept definitions, relationships, and usage across ontology versions. Distinct from Semantic Equivocation, which describes the gap between intended and inferred meaning at a given point in time rather than change across versions. In AI knowledge graph governance, semantic drift refers specifically to version-level definitional change, whilst the broader failure mode of meaning lost between organisational intent and AI interpretation is more precisely described as Semantic Equivocation.

Also known as: Meaning Drift, Ontology Drift, Definitional Drift

Semantic Entity Optimisation (SEO 3.0)

The third wave of SEO: optimising the entity rather than the page. Semantic Entity Optimisation governs an organisation’s entities (the organisation, its products, services, people and concepts) as canonical, disambiguated nodes in a Schema.org knowledge graph, each with a stable @id, verified sameAs links and provenance, so AI search engines and agents can discover, understand, cite and act on them directly.

SEO has moved through three waves:

• SEO 1.0, Search Engine Optimisation: pages ranked by keywords and links.
• SEO 2.0, Semantic Engine Optimisation: content structured for meaning and context, so reasoning engines understand it.
• SEO 3.0, Semantic Entity Optimisation: the entity itself, governed as a knowledge graph that AI agents traverse, cite and transact with directly, including over the Model Context Protocol.

In the first two waves the unit of optimisation is the page. In the third it is the entity: an AI agent answering a question or completing a purchase reads the knowledge graph, not the page. VISEON delivers Semantic Entity Optimisation across its three stages: DISCOVER builds and governs the entity graph, DISCUSS answers from it, and TRANSACT lets authorised agents act on it.

Also known as: SEO 3.0, Third-Wave SEO, Entity-First Optimisation, Entity SEO

Semantic Equivocation

The gap between the meaning an organisation intends and the meaning an AI system infers from data, metadata, and context. The structure may be valid and the records complete, yet the model can still act on a different interpretation than the one intended. Abbreviated as SemanticEQ — a deliberate parallel to the graphic equaliser (EQ) in audio engineering, where individual frequency bands are adjusted to compensate for signal loss or distortion introduced during transmission. In the same way, SemanticEQ identifies and corrects the meaning bands — data definitions, entity relationships, contextual declarations — that have been lost or distorted between organisational intent and AI interpretation. Grounded in Claude Shannon’s 1948 information theory, which demonstrated that the gap between transmitted and received meaning is not only inevitable but measurable — and therefore governable. Because Semantic Equivocation is measurable, it can be identified, quantified, and reduced through structured knowledge graph governance. Unlike a missing field or a broken reference — errors an AI can detect — Semantic Equivocation is invisible: the data arrives structurally intact but semantically misaligned. Distinct from Semantic Drift (version-level definitional change over time) and Concept Drift (statistical distribution shifts in training data). Coined by VISEON in the context of AI knowledge graph governance and Semantic Strategy.

Also known as: SemanticEQ, Semantic EQ, Lost in Translation, Meaning Gap, Interpretive Divergence

Semantic Firewall

The Semantic Firewall is VISEON’s governed verification mechanism that ensures AI answers are grounded in authoritative knowledge, supported by evidence and faithful to the question being asked.

It checks that every part of a question has been addressed, that relationships and claims are supported by the Knowledge Graph, and that missing or ambiguous information is not presented as fact.

When the evidence is insufficient, the Semantic Firewall ensures that the system acknowledges its limitations rather than inventing an answer.

Unlike a conventional network firewall, which controls access, the Semantic Firewall governs the integrity of information communicated by AI. It does not replace the access controls of underlying systems.

The governing principle:

Do not assert what cannot be substantiated.

Semantic Firewall vs Semantic Governor

• Semantic Firewall: Determines whether an answer has sufficient evidence to be released as verified.
• Semantic Governor: Identifies why semantic performance falls short and supports proportionate corrective improvement through human stewardship.

Together, they establish a foundation for reliable, accountable and continuously improving semantic intelligence within VISEON’s Semantic Control Plane., linked to Semantic Control Plane, Semantic Firewall, Knowledge Graph and Negative Feedback.

Also known as: Semantic Evidence Firewall, Semantic Verification Gate, Evidence-Based Answer Gate, Governed Answer Validation, Semantic Integrity Gate.

Semantic Governor

A governed negative-feedback mechanism that monitors semantic interpretation, evidence quality and execution outcomes, identifies deviations, and proposes proportionate corrections to improve system performance.

Authoritative changes to business meaning and knowledge remain subject to human stewardship.

The governing principle:

Improve understanding without weakening the evidence required to answer.

We also established an important distinction:

• Semantic Firewall: Determines whether an answer is sufficiently evidenced to be released.
• Semantic Governor: Identifies why semantic performance falls short and supports corrective improvement.

The intended cycle is:

Observe → Diagnose → Propose → Validate → Steward → Publish → Measure again.

I suggested publishing it as a DefinedTerm with its own persistent @id, linked to Semantic Control Plane, Semantic Firewall, Knowledge Graph and Negative Feedback.

Also known as: Semantic Feedback Controller, Semantic Governance Engine, Semantic Quality Governor, Governed Semantic Feedback Loop, Adaptive Semantic Governance, Semantic Self-Correction, Human-Governed Semantic Intelligence.

Semantic Intelligence

The discipline of transforming unstructured digital content into machine-readable, standards-based knowledge graphs — enabling AI agents, generative search engines, and agentic commerce systems to discover, understand, and act on an organisation’s identity, products, and services. Distinct from natural language processing, Semantic Intelligence applies structured data vocabularies such as Schema.org to make meaning explicit and externally traversable.

Also known as: Semantic Technology, AI Semantic Optimisation, Structured Semantic Intelligence

Semantic MDM

The application of Master Data Management disciplines — golden records, entity survivorship, schema stewardship, and change governance — to the outward-facing semantic layer of a website or digital platform. Where traditional MDM governs master data inside the enterprise (customers, products, suppliers, employees), Semantic MDM governs the machine-readable entity definitions that AI agents, crawlers, and agentic commerce systems consume at the AI-facing boundary. A Semantic MDM system produces a single canonical @id per entity (the golden entity record), resolves duplicate or conflicting definitions through survivorship rules, maintains alignment between operational meaning and declared semantics via change data capture, and publishes a continuously updated Semantic MDM Data Catalog — a machine-readable, AI-traversable index of all validated entity records. Coined by VISEON in the context of AI discoverability and knowledge graph governance.

Also known as: Semantic Master Data Management, Website Semantic MDM, AI-Facing Master Data Management, Knowledge Graph MDM

Semantic Orthogonality

The property of content or data whose meaning does not overlap with, duplicate, or contradict other sources in the same information space. In vector mathematics, orthogonal vectors are perpendicular — carrying independent information with no redundancy. Applied to knowledge graphs and AI discoverability, semantic orthogonality means each entity, term, or piece of content contributes unique informational value that cannot be found elsewhere. AI systems operating under selection algorithms such as GIST (Greedy Independent Set Thresholding) prioritise semantically orthogonal content — content whose meaning is maximally distinct from consensus sources — when constructing responses. Brands and content that achieve semantic orthogonality through precise entity definitions, proprietary DefinedTerms, and high-fidelity structured markup gain inclusion in AI-generated outputs where redundant or semantically similar content is excluded.

Also known as: Content Orthogonality, Information Orthogonality, Semantic Independence

Semantic Personification

The engineering practice of constructing a governed digital representation of an organisation, product, expert, or authored methodology that preserves not only factual identity but also distinctive language, reasoning methods, operational boundaries, and interaction behaviour.

Semantic Personification is implemented across several coordinated layers rather than being embedded entirely inside a knowledge graph.

The knowledge graph defines entities, concepts, relationships, and authored methods. Semantic contracts define meaning, coverage, and authority. Governed content stores retain approved voice and editable language. Mechanical Personalisation expresses operational constraints. The Stateful Behaviour Tree determines how those assets are applied during an interaction.

This architecture allows a conversational interface or AI agent to reflect an organisation’s recognised character and methodology without allowing a generic language model to invent its identity, infer its rules, or silently exceed its knowledge boundary.

Also known as: Governed Digital Personification, Mechanical Personalisation, Pedagogical Branding

Semantic Search

A search method that resolves a query by its meaning and the relationships between entities, not by matching keyword strings. In VISEON, semantic search is served from a governed Schema.org knowledge graph via GraphRAG, so results are relationship-aware and citable: the deterministic graph answers first, with bounded AI only for expression.

Also known as: Contextual Search, Intent-Based Search, Meaning-Based Search

Semantic SEO

An SEO approach that optimises for meaning and entity relationships rather than keyword density – structuring content and Schema.org markup so search and AI systems understand what a page is about, not just which words it contains. At VISEON, semantic SEO is the bridge into the three-wave model: the practice that matures into Semantic Entity Optimisation (SEO 3.0), where the unit of optimisation moves from the page to the governed entity.

Also known as: Semantic Search Optimisation

Semantic Strategy

A strategic approach to organising and exposing business data using semantic web technologies (Schema.org, JSON-LD, knowledge graphs) to ensure AI discoverability and enable agentic commerce. Semantic strategy transforms fragmented digital content into a unified, machine-actionable catalogue that AI agents can discover, understand, and transact with, eliminating digital obscurity.

Also known as: Semantic Web Strategy, Knowledge Graph Strategy, Structured Data Strategy

Semantic Web

The W3C vision of a web of linked, machine-readable data, where meaning is explicit in the data (via RDF, ontologies and shared vocabularies) rather than locked in human-readable prose. VISEON delivers the practical, commercial form of the semantic web: a governed Schema.org / JSON-LD knowledge graph served from an organisation’s own domain to people and AI agents.

Also known as: Web 3.0, Machine-Readable Web

Semantic Web Technology

The family of standards that make data machine-understandable – Schema.org, JSON-LD, RDF, ontologies and linked-data identifiers. VISEON applies these as a governed stack: Schema.org vocabulary, JSON-LD serialisation, canonical @id identity and provenance, assembled into a knowledge graph that AI search engines and agents consume directly.

Also known as: Semantic Technologies, Linked Data Technologies, Web 3.0 Technologies

SIDC Framework

A diagnostic for the ways AI fails, in four layers: Signal (the data layer: is the data accurate, complete and consistent?), Interpretation (the semantic layer: does the AI correctly understand the data and the objectives?), Drift (the temporal layer: has the world, context or data changed since training or deployment?) and Context (the systems layer: do all components, systems and agents share and maintain context?). Semantic Equivocation is the Interpretation-layer failure that knowledge graph governance is designed to address.

Also known as: SIDC, SIDC Diagnostic

Smart Assistants

AI-powered software agents that can perform tasks, answer questions, and provide services through natural language interaction, including voice-activated devices and chatbots.

Also known as: Virtual Assistants, AI Assistants, Digital Assistants, Intelligent Assistants

Social Commerce

The integration of e-commerce functionality directly within social media platforms, enabling users to discover and purchase products without leaving the social network.

Also known as: Social Shopping, Social Media Commerce, S-Commerce

SPARQL

The W3C query language for RDF graphs – the graph equivalent of SQL, used to retrieve and join linked data by pattern. It illustrates what a knowledge graph makes possible: querying by relationships, not keywords. VISEON exposes that queryability to AI through the Model Context Protocol (MCP) over its Schema.org graph, so agents traverse entities without needing a SPARQL endpoint.

Also known as: SPARQL Protocol and RDF Query Language

Standards

Established specifications, guidelines, or benchmarks that define accepted practices, quality levels, or technical requirements within an industry or field.

Also known as: Technical Standards, Industry Standards, Best Practices

Stateful Behaviour Tree

A governed execution architecture that routes an interaction through explicit semantic branches, conditions, capabilities, and terminal leaves.

A Stateful Behaviour Tree does not ask a language model to decide freely what should happen next. It evaluates the current request against validated intent, committed state, semantic contracts, available evidence, source authority, interaction surfaces, and operational constraints before selecting the capability permitted to answer.

Within VISEON DISCUSS, the SBT follows a governed hierarchy:

1. Deterministic — use an exact knowledge graph, KV, relationship, calculation, rule, or source-system answer where one exists.
2. Semantic — retrieve and assemble governed evidence through graph traversal, GraphRAG, content indexes, and semantic contracts.
3. AI — invoke bounded generative AI only where language synthesis, explanation, or natural conversation is required.

The SBT works with a Blackboard that retains validated state across turns. This allows the system to continue the active task, preserve prior decisions, obey the current interaction surface, and distinguish authoritative runtime state from ungoverned conversational prose.

The knowledge graph supplies meaning and relationships. Semantic contracts define authority and boundaries. Deterministic engines perform exact operations. GraphRAG retrieves evidence. The language model provides bounded expression. The SBT governs which of those capabilities is allowed to act.

Also known as: SBT, Governed Stateful Behaviour Tree, Semantic Decisioning Runtime

Structured Data

Machine-readable facts about an organisation’s entities – its organisation, products, services, people and concepts – expressed in a standard vocabulary (Schema.org) and serialised as JSON-LD, so AI systems and agents read meaning directly instead of inferring it from prose. In VISEON’s entity-first model, structured data describes entities with stable @id identifiers and typed relationships, not annotations bolted onto a page.

Also known as: Semantic Markup

Support

Assistance and services provided to users to help them resolve issues, answer questions, and optimise their use of products or services.

Also known as: Customer Support, Technical Support, Help Desk, Client Support

Target Customers

Specific groups of potential customers identified as the primary focus for products, services, or marketing efforts based on shared characteristics or needs.

Also known as: Target Audience, Target Market, Customer Segments

Taxonomy

A hierarchical classification that organises concepts or content into parent-child categories. In VISEON a taxonomy is one input to the knowledge graph, not the end state: categories become typed entities with stable @id identifiers and explicit relationships, so AI can traverse meaning rather than only walk a tree of labels.

Also known as: Classification System, Content Taxonomy, Hierarchical Structure

Topic Clusters

A content organisation strategy where related articles are structured around a central pillar page, connected through internal links and semantic relationships. In traditional SEO, topic clusters demonstrate topical authority through on-page content depth. In knowledge graph contexts, topic clusters are defined by entity relationships across multiple domains rather than single-site architecture, enabling AI systems to discover authority through graph traversal rather than page-level signals.

Also known as: Content Clusters, Content Cluster, Pillar-Cluster Model, Hub and Spoke Content, Hub and Spoke Model, Pillar Content Strategy, Semantic Network, Topic Cluster

Training Data Cutoff

The date after which a language model has seen no training data. Content published after a model’s cutoff is visible to it only through browsing or retrieval; a static model cannot see it. So the cutoff, and whether the model can search the live web, decide how soon new content can become discoverable through that model.

Also known as: Knowledge Cutoff, Training Cutoff, Training Cut-off

Triple

The atomic unit of a knowledge graph: a subject-predicate-object statement (for example, VISEON – develops – VISEON Ask) that asserts one fact as a typed relationship. VISEON’s governed graph is built from triples with canonical @id subjects and objects and verified provenance, which is what lets AI traverse explicit relationships rather than infer them from prose.

Also known as: RDF Triple

Universal Commerce Protocol

A semantic interchange standard that enables AI agents to discover, evaluate, and transact with commercial entities through machine-readable structured data. The Universal Commerce Protocol defines a common schema-based language for product offers, pricing, availability, and fulfilment metadata — allowing autonomous agents to compare and act on commercial data across vendors and platforms without human mediation.

Also known as: UCP, Universal Commerce Standard

Use Cases

Specific situations or scenarios that demonstrate how a product, service, or technology can be applied to solve real-world problems or achieve business objectives.

Also known as: Application Scenarios, Use Case Examples, Implementation Scenarios

Vector Embeddings

Numerical representations that place text or data in a space where proximity approximates similarity of meaning – the basis of vector search and classic RAG. In VISEON, embeddings are a secondary signal: retrieval is GraphRAG-first, following explicit relationships between canonical entities, with vector similarity used to broaden recall, not to decide facts.

Also known as: Embeddings, Semantic Vectors

Viseon Ask

An interactive query feature within the Viseon platform that allows users to ask questions and receive insights about their content optimisation and semantic intelligence.

Also known as: Ask Viseon, Viseon Query

VISEON Platform

The VISEON semantic intelligence platform, which builds governed, AI-discoverable Schema.org knowledge graphs from website and enterprise content, and includes VISEON Ask, VISEON Data Assurance and the FUSEON API.

Also known as: VISEON, VISEON Semantic Intelligence Platform

Voice Commerce

The use of voice-activated technology and virtual assistants to browse, search for, and purchase products or services through spoken commands.

Also known as: V-Commerce, Voice Shopping, Conversational Commerce

Voice Search

Search queries performed through voice commands, requiring natural language processing and conversational AI capabilities for optimal results.

Also known as: Voice Query, Spoken Search

WebMCP

An emerging way for a web page to expose tools to AI agents working inside the browser, so an agent can act on a site, for example search it, fill a form or add to a basket, through declared functions rather than by imitating clicks. It extends the Model Context Protocol to agents acting in the browser.

Also known as: Web Model Context Protocol, Browser MCP, In-browser MCP