User-Agent: * Format: schema.txt Version: 13.4 Purpose: schema.org JSON-LD Entity Index Domain: viseon.io Generated: 2026-10-11T00:01:09.926216Z (UTC) Last-Modified: 2026-10-11T00:01:09.926216Z (UTC) Total-Entities: 197 Content-Language: en-GB Multi-Language: false Encoding: UTF-8 JSON-LD-Context: https://schema.org JSON-LD-Version: 1.1 Content-Type: application/ld+json;charset=UTF-8 Cache-TTL: 3600s # Organization Data @type: Organization @id: - id: https://viseon.io/entity/#viseon url: https://viseon.io/ name: VISEON description: VISEON is a semantic intelligence platform, canonical by design. It builds the knowledge graph AI reads about an organisation and serves it to people through VISEON Ask and to AI agents through Model Context Protocol (MCP) servers: MCP for websites with VISEON for Web, a canonical enterprise ontology and hub for all your MCP servers with VISEON for Enterprise and VISEON for Qlik, and tested data underneath with VISEON Data Assurance. - id: https://viseon.io/entity/#differentia-consulting url: https://www.differentia.consulting/ name: Differentia Consulting description: Business intelligence and data analytics consultancy specialising in Qlik technologies, ERP systems, and AI-driven solutions. - id: https://www.differentia.consulting/qlik/#department url: https://www.differentia.consulting/qlik/ name: Qlik Practice description: - id: https://www.differentia.consulting/jdedwards/#department url: https://www.differentia.consulting/jdedwards/ name: JD Edwards Practice description: - id: https://www.differentia.consulting/alteryx/#department url: https://www.differentia.consulting/alteryx/ name: Alteryx Practice description: - id: https://viseon.io/#client-alfanar url: https://viseon.io/ name: Alfanar description: Alfanar is a VISEON for Web client. - id: https://viseon.io/#client-lauras-house url: https://viseon.io/ name: Laura’s House description: Laura’s House is a VISEON for Web client. - id: https://viseon.io/#client-qlickxl url: https://viseon.io/ name: QlickXL description: QlickXL is a VISEON for Web client. - id: https://viseon.io/#client-harris-poll-uk url: https://www.theharrispoll.co.uk name: The Harris Poll UK description: The Harris Poll UK is a VISEON for Web client. - id: https://viseon.io/#client-anna-elliot url: https://viseon.io/ name: Anna Elliot description: Anna Elliot is a VISEON for Web client. - id: https://viseon.io/#client-atp-construction url: https://viseon.io/ name: ATP Construction LLC description: ATP Construction LLC is a VISEON for Web client. - id: https://viseon.io/#client-lovely-barns url: https://viseon.io/ name: The Lovely Barns description: The Lovely Barns is a VISEON for Web client. alternate: https://data-lake-seo.s3.eu-north-1.amazonaws.com/viseon-v2/https---viseon-io/schema-txt-json-files/organizations.json # Product Data @type: Product @id: - id: https://viseon.io/#offer-catalog url: https://viseon.io/services/ name: VISEON Services & Products description: Complete catalogue of VISEON semantic intelligence services, platform subscriptions, and API execution products. - id: https://viseon.io/#solutions url: https://viseon.io/ name: VISEON solutions description: The four VISEON solutions: VISEON for Web, VISEON for Enterprise, VISEON Data Assurance and VISEON for Qlik (vCat). - id: https://viseon.io/entity/#viseon-platform url: https://viseon.io/pricing/ name: VISEON Platform description: VISEON is an enterprise semantic intelligence platform that builds governed, AI-discoverable knowledge graphs from website and enterprise content, enabling brands to be found by AI agents, eliminate digital obscurity, and participate in agentic commerce. - id: https://viseon.io/products/#fuseon-api-credits url: https://viseon.io/pricing/ name: FUSEON API Credits description: API execution credits for the FUSEON semantic intelligence engine. Each credit represents one knowledge graph build execution. Available to VISEON platform subscribers as individual credits or in bundles. - id: https://viseon.io/entity/#viseon-for-web url: https://viseon.io/viseon-for-web/ name: VISEON for Web description: VISEON for Web delivers SEO 3.0, Semantic Entity Optimisation, for an organisation's websites. It validates and governs the Schema.org knowledge graph behind every page, including Google Rich Results, and serves it from the organisation's own domain through Interact, one hub for every interaction: people ask through VISEON Ask, AI agents query through a Model Context Protocol (MCP) endpoint, and agents in the browser act through WebMCP. Agent registries discover it through an Agentic Resource Discovery (ARD) manifest and the official MCP Registry. - id: https://viseon.io/entity/#viseon-for-enterprise url: https://viseon.io/viseon-for-enterprise/ name: VISEON for Enterprise description: VISEON for Enterprise builds one governed ontology of an organisation, covering its entities, relationships, rules and the documents that explain them, over the data estate it already runs. People query it through VISEON Ask; AI agents query it through a Model Context Protocol (MCP) endpoint. Both read the same canonical graph, so both get the same answer, with its provenance. - id: https://viseon.io/entity/#viseon-data-assurance url: https://viseon.io/viseon-data-assurance/ name: VISEON Data Assurance description: VISEON Data Assurance proves your data can be trusted by people, auditors, regulators and AI. It scores every row, not a sample, inside your own environment against nine data quality dimensions drawn from DAMA-DMBOK and aligned to ISO/IEC 25012, then keeps watching: exact failing-record counts, self-closing incidents, lineage, and compliance findings that cite the clause they relate to. An MCP-native AI assistant answers from the same figures, within the permissions of the person asking. - id: https://viseon.io/entity/#viseon-for-qlik url: https://viseon.io/viseon-for-qlik-vcat/ name: VISEON for Qlik description: VISEON for Qlik, vCat for short, is the top-level navigation system for enterprise AI in a Qlik Cloud Analytics tenant. It maintains the canonical master map of the tenant (live reporting, master items and operating rules) so AI agents understand the estate before they act. Its extension scans the master measures, dimensions and fields in your apps and drafts descriptions; your data steward approves each one in the vCat portal before it is published. At runtime, vCat MCP resolves intent, meaning and the right metric before the Qlik MCP Server computes the answer. alternate: https://data-lake-seo.s3.eu-north-1.amazonaws.com/viseon-v2/https---viseon-io/schema-txt-json-files/products.json # Person Data @type: Person @id: - id: https://viseon.io/person/adrian-parker/ url: https://viseon.io/person/adrian-parker/ name: Adrian Parker description: British AI strategist and accountant with over 20 years of experience in business intelligence, ERP integration, and semantic web technologies. Founder of Differentia Consulting and creator of the VISEON semantic intelligence platform. Pioneer of knowledge graph-driven AI discoverability, advocating that structured data quality and entity graph integrity are the foundational requirements for reliable LLM reasoning and confident agentic commerce decisions. - id: https://viseon.io/#reviewer-alfanar url: https://viseon.io/ name: Sara Lobo description: - id: https://viseon.io/#reviewer-lauras-house url: https://viseon.io/ name: Margaret Bayston description: - id: https://viseon.io/#reviewer-harris-poll-uk url: https://viseon.io/ name: Sarah Beams description: - id: https://viseon.io/#reviewer-atp-construction url: https://viseon.io/ name: John description: - id: https://viseon.io/#reviewer-lovely-barns url: https://viseon.io/ name: Rob description: - id: https://viseon.io/person/katelin-devencenty/ url: https://viseon.io/person/katelin-devencenty/ name: Katelin DeVencenty description: AI Consultant, Data scientist, LLM and knowledge graph product specialist at VISEON, responsible for translating Schema.org data quality standards into scalable platform workflows. Focuses on the intersection of structured data governance and LLM-readable entity design, ensuring that VISEON knowledge graphs meet the integrity thresholds required for high-confidence AI agent reasoning, MCP integration, and agentic commerce readiness. - id: https://viseon.io/person/simon-phillips/ url: https://viseon.io/person/simon-phillips/ name: Simon Phillips description: Account Manager at Differentia Consulting, AI, VISEON, JD Edwards ERP managed services, and enterprise data pipelines and analytics. - id: https://viseon.io/person/hamza-ashraf/ url: https://viseon.io/person/hamza-ashraf/ name: Hamza Ashraf description: Data scientist, knowledge graph engineer and AI systems specialist at VISEON, with deep expertise in systems, open commerce protocols, GraphRAG architecture, Model Context Protocol implementation, and the structural data quality requirements that determine LLM retrieval confidence. Builds the FUSEON technical infrastructure that converts Schema.org entity graphs into reliable, traversable knowledge structures for autonomous AI agent operations. - id: https://viseon.io/person/ihsanullah-sadiq/ url: https://viseon.io/person/ihsanullah-sadiq/ name: Ihsanullah Sadiq description: VISEON Software Engineer at Differentia Consulting, focused on agentic AI frameworks, JSON-LD structured data, and large language model integration for the VISEON semantic intelligence platform. - id: https://viseon.io/person/alex-mckenna/ url: https://viseon.io/person/alex-mckenna/ name: Alex McKenna description: Head of VISEON Sales at Differentia Consulting, responsible for bringing the VISEON semantic intelligence platform, VISEON for Enterprise and VISEON Data Assurance to market. - id: https://viseon.io/person/kit-mundy/ url: https://viseon.io/person/kit-mundy/ name: Kit Mundy description: Managing Director of Differentia Consulting and one of its founders, leading sales for Differentia's business intelligence, ERP and AI services and for the VISEON semantic intelligence platform. - id: https://viseon.io/person/cassie-kozyrkov/ url: https://kozyrkov.com name: Cassie Kozyrkov description: Leading voice in decision intelligence, blending data science with behavioural science to help organisations make smarter, evidence-based decisions. - id: https://viseon.io/person/monica-rogati/ url: https://www.monicarogati.com name: Monica Rogati description: Best known for creating the Data Science Hierarchy of Needs. Former VP of Data at Jawbone and early LinkedIn data scientist. - id: https://viseon.io/person/joe-caserta/ url: https://www.caserta.com name: Joe Caserta description: Founder of NYC-based data and analytics consulting firm specialising in data architecture, engineering, and enterprise transformation. - id: https://viseon.io/person/kirk-borne/ url: https://www.kirkborne.com name: Kirk Borne description: One of the most influential figures in big data and data science. Former NASA astrophysicist known for explaining emerging tech trends. - id: https://viseon.io/person/bernard-marr/ url: https://www.bernardmarr.com name: Bernard Marr description: Best-selling author and strategic consultant bridging analytics and business impact, writing extensively on data strategy and digital transformation. Keynote speaker at Differentia Consulting Qlik Customer Day 2018. - id: https://viseon.io/person/doug-laney/ url: https://www.douglaney.com name: Doug Laney description: Author of Infonomics and pioneer of treating data as an actual business asset. Expert in data monetisation, governance, and valuation. - id: https://viseon.io/person/usama-fayyad/ url: https://www.usamafayyad.com name: Usama Fayyad description: Foundational figure in data mining development. Former Chief Data Officer at Yahoo! with deep expertise in AI, data platforms, and enterprise analytics. - id: https://viseon.io/person/carla-gentry/ url: https://datainc.tech name: Carla Gentry description: Data scientist with decades of experience in analytics, segmentation, and statistical modelling, advocating for practical, business-first data applications. - id: https://viseon.io/person/hilary-mason/ url: https://hilarymason.com name: Hilary Mason description: Founder of Fast Forward Labs (acquired by Cloudera), recognised voice in applied machine learning balancing innovation with responsible AI practices. - id: https://viseon.io/person/ben-lorica/ url: https://www.benlorica.com name: Ben Lorica description: Former Chief Data Scientist at O'Reilly Media and co-chair of major AI/ML conferences, focusing on AI research, ML platforms, and scalable data architectures. - id: https://viseon.io/person/kate-strachnyi/ url: https://datacated.com name: Kate Strachnyi description: Founder of DATAcated, host of the DATAcated Conference and DATAcated On Air podcast, and author of ColorWise, focused on data visualization, data storytelling, and brand amplification for AI, ML, and data science companies. alternate: https://data-lake-seo.s3.eu-north-1.amazonaws.com/viseon-v2/https---viseon-io/schema-txt-json-files/persons.json # Article Data @type: Article @id: - id: https://viseon.io/articles/ai-search-optimisation/#article url: https://viseon.io/articles/ai-search-optimisation/ name: AI Search Optimisation: The Complete Guide description: How to optimise your brand for AI-powered search engines through structured data and knowledge graph implementation. This guide covers the transition from traditional SEO to AI Discoverability and Agentic Search readiness. - id: https://viseon.io/articles/architecting-for-agentic-commerce/#article url: https://viseon.io/articles/architecting-for-agentic-commerce/ name: Architecting For Agentic Commerce description: The transition from SEO to agentic commerce demands machine-verifiable utility, not human-readable copy. This article explains semantic orthogonality against Google Research's GIST framework, Available To Promise (ATP) inventory and delivery transparency, and Universal Commerce Protocol (UCP) commercial contracts — the three layers that let an AI agent move from discovering a brand to autonomously transacting with it. - id: https://viseon.io/articles/agentic-search-solves-intent-problem/#article url: https://viseon.io/articles/agentic-search-solves-intent-problem/ name: Agentic Search Solves Search Intent Problem description: Why keyword search structurally fails at scale and how GraphRAG-backed natural language search resolves the intent problem by querying structured Schema.org knowledge graphs for accurate, relationship-aware responses. - id: https://viseon.io/articles/building-json-ld-machine-trustable-knowledge-graphs/#article url: https://viseon.io/articles/building-json-ld-machine-trustable-knowledge-graphs/ name: Building Machine-Trustable Knowledge Graphs: A Practical Guide to JSON-LD and Schema.org for AI Discoverability description: A comprehensive technical guide to implementing JSON-LD and Schema.org for AI discoverability. Learn edge architecture, entity connections, and best practices for building machine-readable knowledge graphs that AI agents can trust and traverse. Includes practical examples, common pitfalls, and validation strategies. - id: https://viseon.io/articles/knowledge-graphs-evolution-semantic-web-ai-categorical-intelligence/#article url: https://viseon.io/articles/knowledge-graphs-evolution-semantic-web-ai-categorical-intelligence/ name: Knowledge Graphs: From Semantic Web to AI Categorical Intelligence description: How knowledge graphs evolved from the semantic web to AI categorical intelligence, using JSON-LD entity relationships to power discovery and agentic commerce. - id: https://viseon.io/top-10-reasons-athletic-footwear-needs-schema/#article url: https://viseon.io/top-10-reasons-athletic-footwear-needs-schema/ name: Top 10 Reasons Athletic Footwear Needs Schema description: Ten reasons why athletic footwear brands require Schema.org structured data to overcome naming inconsistencies (trainers vs sneakers) across generations and regions, improving AI discoverability. - id: https://viseon.io/articles/ontologies-persisting-in-schemas-for-data-management/#article url: https://viseon.io/articles/ontologies-persisting-in-schemas-for-data-management/ name: Ontologies Persisting in Schemas for Data Management description: How ontologies embedded in Schema.org structured data create persistent, machine-readable frameworks for data management and consistent AI entity classification. - id: https://viseon.io/articles/digital-obscurity/#article url: https://viseon.io/articles/digital-obscurity/ name: Digital Obscurity: When AI Cannot Find Your Brand, Products, or Services description: Digital obscurity occurs when a brand, products, or services exist online but remain invisible to AI agents due to absent structured data. Learn how explicit referenceability resolves the Content to Context gap. - id: https://viseon.io/articles/beyond-on-page-topic-clusters/#article url: https://viseon.io/articles/beyond-on-page-topic-clusters/ name: Beyond On-Page Topic Clusters description: The decade-old SEO playbook of pillar content, supporting articles, and internal linking builds authority only within a single site. Knowledge graph-based topic clusters work across domains through validated entity relationships, graph proximity to authoritative sources, inverse-edge reciprocity, and freedom from semantic equivocation. As AI systems become the primary discovery mechanism, graph position — not on-page structure — determines discoverability. - id: https://viseon.io/articles/enterprise-guide-to-data-driven-agentic-search-optimization/#article url: https://viseon.io/articles/enterprise-guide-to-data-driven-agentic-search-optimization/ name: Enterprise Guide to Data Driven Agentic Search Optimization (AEO) description: The VISEON enterprise guide to Agentic Engine Optimization (AEO): how to replace legacy SEO infrastructure with a real-time, Schema.org-backed semantic graph that AI agents can discover, discuss, and transact with. - id: https://viseon.io/articles/enterprise-semantic-layer-internal-external-context/#article url: https://viseon.io/articles/enterprise-semantic-layer-internal-external-context/ name: Your Data Catalogue Governs the Inside. Nothing Governs the Outside. description: Atlan, Snowflake Horizon, and Databricks Unity Catalog govern what internal data means to AI agents inside the enterprise perimeter. None of that context is visible to Google AI Mode, ChatGPT, or an agent researching your organisation externally. The external semantic layer — a governed Schema.org knowledge graph published in JSON-LD — is the missing half of enterprise context governance. - id: https://viseon.io/articles/how-llms-discover-content-and-context-from-queries/#article url: https://viseon.io/articles/how-llms-discover-content-and-context-from-queries/ name: How LLMs Discover Content and Context from Queries description: How large language models resolve function-based queries through semantic parsing, web retrieval, and knowledge graph traversal—and why Schema.org markup is essential for discoverability across GPT-5, Gemini, Claude, Perplexity and Grok. - id: https://viseon.io/articles/personalise-your-brand-with-heart-and-soul/#article url: https://viseon.io/articles/personalise-your-brand-with-heart-and-soul/ name: Escape the Herd: Personalise Your Brand with Heart & Soul description: AI does not hallucinate your brand — it flattens it. Standard Schema.org encodes classification but not character. This article introduces the Tin Man problem and shows how encoding an intent layer makes brand personality visible to AI agents. - id: https://viseon.io/articles/semantic-equivocation/#article url: https://viseon.io/articles/semantic-equivocation/ name: Semantic Equivocation: Why Your AI Is Confidently, Plausibly Wrong description: Unlike AI hallucination, Semantic Equivocation occurs when structurally valid data is semantically misaligned. Introduces the SIDC diagnostic framework (Signal, Interpretation, Drift, Context) and applies Claude Shannon's 1948 equivocation principle to AI governance. - id: https://viseon.io/top-10-llms-october-2025/#article url: https://viseon.io/top-10-llms-october-2025/ name: Top 10 LLMs October 2025 description: Comparative industry report of the top ten large language models in October 2025, evaluating native web discoverability, Google Rich Snippet recognition, and training data cut-offs. - id: https://viseon.io/top-10-data-influencers-2026/#article url: https://viseon.io/top-10-data-influencers-2026/ name: Top 10 Data Influencers of 2026 description: Annual report showcasing 10 influential leaders shaping the data, AI, and analytics landscape in 2026. alternate: https://data-lake-seo.s3.eu-north-1.amazonaws.com/viseon-v2/https---viseon-io/schema-txt-json-files/articles.json # BlogPosting Data @type: BlogPosting @id: - None found alternate: https://data-lake-seo.s3.eu-north-1.amazonaws.com/viseon-v2/https---viseon-io/schema-txt-json-files/blogpostings.json # Event Data @type: Event @id: - id: https://www.differentia.consulting/event/qlik-customer-day-15th-october-2026/#session-vcat url: https://www.differentia.consulting/event/qlik-customer-day-15th-october-2026/ name: VISEON for Qlik: vCat - Context Engineering: Semantic Catalogue for Qlik Cloud Analytics description: How vCat gives AI agents the business meaning behind a Qlik estate before they compute an answer: governed descriptions, human sign-off, and context first, implementation second. - id: https://www.differentia.consulting/event/qlik-customer-day-15th-october-2026/#session-viseon-web url: https://www.differentia.consulting/event/qlik-customer-day-15th-october-2026/ name: VISEON for Web: How AI Understands Your Website, Your Digital Twin description: How VISEON for Web turns a website into a knowledge graph AI can discover, discuss and transact with, served through VISEON Ask and its own MCP server. - id: https://www.differentia.consulting/event/qlik-customer-day-15th-october-2026/#session-data-assurance url: https://www.differentia.consulting/event/qlik-customer-day-15th-october-2026/ name: VISEON Data Assurance for Qlik: Data Quality and Compliance for Qlik description: Launch of VISEON Data Assurance for Qlik: affordable data quality and compliance for Qlik, AI-enabled via MCP. - id: https://www.differentia.consulting/event/qlik-customer-day-15th-october-2026/#session-keynote url: https://www.differentia.consulting/event/qlik-customer-day-15th-october-2026/ name: Keynote: If AI Can't Understand Your Business, It Can't Recommend You description: Sarah Beams, Managing Director of The Harris Poll UK, with Differentia Consulting, on how VISEON for Web keeps more than 25 years of expertise visible, understood and accurately represented as AI reshapes discovery, and why traditional SEO is no longer enough. - id: https://www.differentia.consulting/event/qlik-customer-day-15th-october-2026/#session-welcome url: https://www.differentia.consulting/event/qlik-customer-day-15th-october-2026/ name: Welcome and introduction description: Welcome and introduction by Kit Mundy and Simon Phillips, Differentia Consulting. - id: https://www.differentia.consulting/event/qlik-customer-day-15th-october-2026/#event url: https://www.differentia.consulting/event/qlik-customer-day-15th-october-2026/ name: Qlik Customer Day – 15th October 2026 – Reading description: Differentia Consulting's free Qlik Customer Day: the ABCs of AI (Audit, Build, Connect), with a keynote from The Harris Poll UK on VISEON for Web, and VISEON sessions on vCat, VISEON for Web and the launch of VISEON Data Assurance for Qlik. - id: https://www.differentia.consulting/event/qlik-customer-day-2018-march-21st-reading-agenda/#event url: name: Qlik Customer Day 2018 – Untapping Latent Business Intelligence with #SmarterBI description: Keynote session by Bernard Marr at Differentia Consulting's Qlik Customer Day 2018, covering business intelligence strategy and #SmarterBI. alternate: https://data-lake-seo.s3.eu-north-1.amazonaws.com/viseon-v2/https---viseon-io/schema-txt-json-files/events.json # Review Data @type: Review @id: - id: https://viseon.io/#review-alfanar url: https://viseon.io/ reviewBody: At Alfanar, our mission depends on being found by the right people – donors, social entrepreneurs, and partners across the Arab World. VISEON helped us understand how AI systems see and represent our organisation online. For a charity working across multiple regions, making sure our work is accurately discoverable by AI is central to reaching the people we exist to serve. - id: https://viseon.io/#review-lauras-house url: https://viseon.io/ reviewBody: Since working with VISEON, Laura’s House is now visible; its appropriate assets are appearing on the first page of Google and Bing’s results. Significant schema markup was added, improving how well AI engines can find and understand what we do, evidenced by more comprehensive and accurate AI responses. - id: https://viseon.io/#review-qlickxl url: https://viseon.io/ reviewBody: Thank you for helping us fix our Schema for AI Semantic Search. Since adding our Schema catalog knowledge graph catalog we’re now winning business and appearing alongside bigger companies in search. - id: https://viseon.io/#review-harris-poll-uk url: https://viseon.io/ reviewBody: When we became The Harris Poll UK in 2025, we were very conscious of carrying forward more than 25 years of expertise built under a different name. The value we deliver hadn’t changed, but the way organisations discover that expertise certainly had. As AI reshapes discovery, VISEON has helped ensure our expertise remains visible, understood and accurately represented to the organisations looking for an insight partner like The Harris Poll UK. - id: https://viseon.io/#review-anna-elliot url: https://viseon.io/ reviewBody: As a digital agency, we know how fast the search landscape is changing, and VISEON is ahead of the curve. Most businesses still don’t realise that AI agents can’t properly discover or recommend them because their site lacks the structured data to make that possible. VISEON fixes that. It’s the kind of solution we’d point any client to who wants to stay visible as AI-powered search becomes the norm. - id: https://viseon.io/#review-atp-construction url: https://viseon.io/ reviewBody: After partnering with VISEON, ATP Construction LLC has received much more attention than it used to. We used to get confused with a company in Idaho with the same name, but now, local residents are able to find us easily and that has changed everything for us. - id: https://viseon.io/#review-lovely-barns url: https://viseon.io/ reviewBody: After working with VISEON, The Lovely Barns is finally showing up properly online, and AI now gives clear, detailed answers about our Algarve barns instead of confusing us with other properties. The schema they set up explains our two restored barns, what they include, and where we are, so AI, and guests, clearly understand what we offer and feel confident booking with us. alternate: https://data-lake-seo.s3.eu-north-1.amazonaws.com/viseon-v2/https---viseon-io/schema-txt-json-files/reviews.json # FAQPage Data @type: FAQPage @id: - id: https://viseon.io/faq/what-is-semantic-orthogonality/ url: https://viseon.io/faq/what-is-semantic-orthogonality/ question: What is semantic orthogonality in AI search? answer: Semantic orthogonality describes distinctive information that adds useful value beyond repetitive or generic content. For VISEON, the practical aim is to make your differentiators explicit and evidenced: specific capabilities, use cases, methods and relationships. The mathematical analogy does not mean website content has a universal orthogonality score or a guaranteed route into an AI answer. - id: https://viseon.io/faq/why-does-gist-framework-matter/ url: https://viseon.io/faq/why-does-gist-framework-matter/ question: Why does Google's GIST framework matter for SEO? answer: GIST (Greedy Independent Set Thresholding) is Google Research work on selecting data that balances diversity and utility. The practical lesson for your content is to publish distinctive, relevant and evidenced information rather than repeat generic descriptions. VISEON makes those distinctions explicit and connected in your knowledge graph. This is a content principle, not a claim that all AI systems use GIST or that a particular Schema.org type guarantees inclusion. - id: https://viseon.io/faq/how-do-ai-agents-verify-atp/ url: https://viseon.io/faq/how-do-ai-agents-verify-atp/ question: How do AI agents verify Available To Promise (ATP)? answer: Available To Promise (ATP) is the quantity a business can commit for a specified time, based on current stock, incoming supply and existing commitments. Agents need an authoritative, current operational source to verify it. Schema.org availability, inventory and delivery properties can describe relevant information, but do not themselves calculate or certify ATP. A simple illustrative calculation is stock plus incoming supply minus committed demand for the relevant period; actual rules depend on the business. - id: https://viseon.io/faq/what-schema-org-properties-enable-ucp/ url: https://viseon.io/faq/what-schema-org-properties-enable-ucp/ question: How does Schema.org support UCP readiness? answer: Schema.org properties such as hasMerchantReturnPolicy, shippingDetails and priceSpecification can describe product offers and policies that support commerce readiness. They do not establish Universal Commerce Protocol (UCP) compliance by themselves. UCP has its own profiles, capability definitions, data models and integration requirements. VISEON helps align the governed product and policy information with the requirements of the intended commerce integration. - id: https://viseon.io/faq/what-is-semantic-equivocation/ url: https://viseon.io/faq/what-is-semantic-equivocation/ question: What is Semantic Equivocation and why does it matter for AI? answer: VISEON uses Semantic Equivocation, or SemanticEQ, to describe the gap between the meaning an organisation intends and the meaning an AI system infers from its data and context. A field can be present and structurally valid while its practical meaning remains ambiguous. For example, two systems may use the same term for different business rules. SemanticEQ focuses on making those definitions, relationships and contextual assumptions explicit, testing them against realistic questions, and correcting mismatches through governance. It differs from semantic drift, where definitions change over time, and concept drift, where statistical patterns change. The equaliser analogy describes adjusting the representation of meaning; it is not a claim that Shannon proved business meaning can always be measured or completely recovered. - id: https://viseon.io/faq/what-does-ai-say-about-my-brand/ url: https://viseon.io/faq/what-does-ai-say-about-my-brand/ question: What does AI say about my brand? answer: If an AI description of your brand sounds interchangeable with your competitors, it may be missing useful distinctions in the information it retrieves. VISEON examines whether your products, expertise, methods and supporting evidence are clearly represented and connected in the knowledge graph. Publishing that knowledge through MCP gives connected agents an authoritative source to query. A Knowledge Graph Assessment identifies gaps in this representation; external AI responses also depend on the sources and tools each system uses. - id: https://viseon.io/faq/why-does-my-brand-sound-generic-in-ai/ url: https://viseon.io/faq/why-does-my-brand-sound-generic-in-ai/ question: Why does my brand sound generic in AI? answer: A generic description can result when the available information says what category your organisation belongs to but gives little evidence of why a customer would choose it. Distinctive methods, capabilities, use cases, expertise and culture need to be clearly expressed and supported. VISEON connects these details in your knowledge graph so agents can retrieve more than a name, industry and location. - id: https://viseon.io/faq/what-is-an-intent-layer/ url: https://viseon.io/faq/what-is-an-intent-layer/ question: What is an intent layer in a knowledge graph? answer: An intent layer records the meaning behind your organisation's facts: its philosophy, methods, terminology, audience relationships and characteristic way of working. VISEON connects these declarations with their supporting content so agents can retrieve them alongside products, services and expertise. Publishing them provides context for external agents; Ask Premium can also use agreed organisational expectations to shape its personality and role behaviour. - id: https://viseon.io/faq/can-i-control-how-ai-describes-my-brand/ url: https://viseon.io/faq/can-i-control-how-ai-describes-my-brand/ question: Can I control how AI describes my brand? answer: You can govern the information you publish and make it directly accessible through your knowledge graph and MCP endpoint. Clear definitions, evidenced distinctions and connected context give external agents a stronger source for representing your brand, although their final wording and source selection remain their own. With your own Ask Premium interface, you can also tailor personality, communication style and role behaviour to your organisation's expectations. - id: https://viseon.io/faq/what-is-semantic-brand-personalisation/ url: https://viseon.io/faq/what-is-semantic-brand-personalisation/ question: What is Semantic Brand Personalisation? answer: Semantic Brand Personalisation makes your organisation's distinctive identity explicit in its knowledge graph: its voice, philosophy, methods, terminology, audience relationships and supporting evidence. This gives connected agents useful context for representing the brand. Ask Premium extends that foundation with tailored personality and role behaviour, reflecting your culture, training and working practices. - id: https://viseon.io/faq/what-is-digital-homogenisation/ url: https://viseon.io/faq/what-is-digital-homogenisation/ question: What is digital homogenisation? answer: Digital homogenisation is 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. - id: https://viseon.io/articles/personalise-your-brand-with-heart-and-soul/#faq url: https://viseon.io/articles/personalise-your-brand-with-heart-and-soul/#faq question: answer: - id: https://viseon.io/discuss/#faq-needs-knowledge-graph url: https://viseon.io/discuss/#faq-needs-knowledge-graph question: Do we need a knowledge graph already in place to use VISEON:Discuss? answer: You do not need to have a knowledge graph before getting started. VISEON:Discuss requires a validated knowledge graph before deployment. If you do not already have one, start with a VISEON Semantic Entity Assessment of your website’s structured data. VISEON then builds and validates the knowledge graph that VISEON:Discuss uses to answer questions. - id: https://viseon.io/discuss/#faq-deployment-technical url: https://viseon.io/discuss/#faq-deployment-technical question: What does VISEON:Discuss deployment involve technically? answer: VISEON:Discuss embeds via a lightweight JavaScript component added to your site. It connects to your VISEON knowledge graph endpoint and content index. No server-side infrastructure is required on your part. Compatible with WordPress and other common CMS platforms. - id: https://viseon.io/discuss/#faq url: https://viseon.io/discuss/#faq question: answer: - id: https://viseon.io/faq/discuss-vs-standard-chatbot/ url: https://viseon.io/faq/discuss-vs-standard-chatbot/ question: What is the difference between VISEON:Discuss and a standard chatbot? answer: VISEON:Discuss gives visitors a conversational way to explore your organisation’s knowledge graph and supporting site content. VISEON Ask follows the connections between your products, services, people and expertise to answer questions with relevant sources. It retains context within the conversation, so visitors can ask follow-up questions naturally. The distinction is a connected, organisation-specific knowledge foundation rather than generic responses or fixed scripts. - id: https://viseon.io/faq/discuss-multiple-domains/ url: https://viseon.io/faq/discuss-multiple-domains/ question: Can VISEON:Discuss cover multiple domains or brands? answer: Yes. VISEON:Discuss can be scoped to a single domain or configured to reason across a federated knowledge graph — querying entities from a parent organisation, subsidiary brands, or product lines simultaneously. - id: https://viseon.io/faq/discuss-agent-discoverability/ url: https://viseon.io/faq/discuss-agent-discoverability/ question: How does VISEON:Discuss help with AI agent discoverability? answer: VISEON:Discuss uses the same governed knowledge foundation for visitor conversations and direct agent access through MCP. A connected agent can query your products, services and supporting evidence through the endpoint. Discovery listings and manifests help expose that route to compatible clients; actual access depends on their discovery process and configuration. - id: https://viseon.io/faq/who-develops-and-supports-viseon/ url: https://viseon.io/faq/who-develops-and-supports-viseon/ question: Who develops and supports the VISEON platform? answer: VISEON is developed, delivered, and managed by Differentia Consulting, a premier enterprise AI data engineering and business intelligence consultancy. With decades of experience deploying enterprise-grade data orchestration, master data management (MDM), and analytics infrastructure, Differentia Consulting bridges the gap between internal operational data and external AI-facing semantic layers. As elite partners across core data platforms like Qlik, Differentia Consulting provides the strategic oversight, deployment technical resource, and ongoing schema stewardship required to operate VISEON at an enterprise scale. - id: https://viseon.io/faq/what-is-semantic-mdm/ url: https://viseon.io/faq/what-is-semantic-mdm/ question: What is Semantic MDM? answer: Semantic MDM (Semantic Master Data Management) applies the disciplines of enterprise MDM — 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, search engines, and agentic commerce systems encounter at the AI-facing boundary. The process runs in three stages. First, Audit: accessible entity definitions across the agreed domain scope are crawled, validated against Schema.org, and assessed for duplicate @id references, conflicting definitions, orphaned nodes, and semantic misalignment between declared meaning and operational intent. This is the Semantic Entity Assessment, the diagnostic equivalent of a data quality assessment before MDM implementation, with results in minutes. Second, Enrich: entity conflicts are resolved, canonical @id references are established, and survivorship rules are applied to produce a single authoritative definition per entity — the Golden Entity Record. Third, Publish: the validated entity layer is compiled into a Semantic MDM Data Catalog — a machine-readable, AI-traversable index of all golden entity records, maintained through agreed updates and governance; where configured, change data capture supports alignment with operational sources. In VISEON, Semantic MDM for the website is delivered by VISEON for Web, which serves the golden entity records to people through VISEON Ask and to AI agents through its own MCP server. VISEON for Enterprise applies the same discipline inside the organisation: one canonical identifier and definition per entity, linked to every system's version of it. For Qlik Cloud estates, VISEON for Qlik (vCat) governs the meaning of master measures and dimensions, and VISEON Data Assurance tests the master data itself, row by row. The parallel to traditional MDM is direct: just as MDM eliminated duplicate customer records across ERP and CRM systems, Semantic MDM eliminates duplicate entity definitions across the knowledge graph, giving connected AI agents a shared authoritative source of entity definitions and evidence. - id: https://viseon.io/faq/how-long-does-a-viseon-discovery-audit-take/ url: https://viseon.io/faq/how-long-does-a-viseon-discovery-audit-take/ question: How long does the Semantic Entity Assessment take? answer: Minutes. The Semantic Entity Assessment is fully automated: once you submit your domain, our audit crawls accessible entity definitions across your site — validating @id integrity, identifying duplicate or conflicting records, flagging potential semantic misalignments for review, and scoring your knowledge graph against Schema.org. The result is your Semantic Entity Assessment: domain-wide health scores, AI discoverability gaps and severity-ranked issues, with recommendations for each finding. It is free, once per domain. Deploying VISEON for Web on your domain then takes one to three days, included in the paid plans; fix specifications, the implementation roadmap, enrichment and data catalogue publication build on the assessment from there. - id: https://viseon.io/faq/do-we-need-a-developer-to-use-the-viseon-data-platform/ url: https://viseon.io/faq/do-we-need-a-developer-to-use-the-viseon-data-platform/ question: Do we need a developer to use the VISEON data platform? answer: For the Enrich stage of the Semantic MDM process, yes — resolving entity conflicts, establishing Golden Entity Records, and implementing schema corrections requires technical resource. This is a deliberate, expert-led process: each entity definition is reviewed and enriched against your operational reality before it is published to the catalog. VISEON provides implementation-ready specifications — exact @id corrections, entity definitions, and structured data snippets — that your development team can apply directly. If you do not have internal resource available, VISEON can implement on your behalf as part of a managed engagement. - id: https://viseon.io/faq/will-viseon-work-with-wordpress-seo-plugins/ url: https://viseon.io/faq/will-viseon-work-with-wordpress-seo-plugins/ question: Will VISEON work with WordPress SEO plugins? answer: Yes, and nothing is replaced. VISEON audits and builds on the schema that Yoast, Rank Math and other plugins produce, resolves conflicts and gaps across the whole domain, and adds what plugins do not provide: one governed graph, VISEON Ask, an MCP endpoint and discovery listings. - id: https://viseon.io/faq/can-viseon-manage-the-full-process/ url: https://viseon.io/faq/can-viseon-manage-the-full-process/ question: Can VISEON manage the full process? answer: Yes. You can engage VISEON for Audit only, or extend to full Semantic MDM delivery — covering Audit, Enrich, Govern, Maintain, and Publish. The Maintain service provides ongoing schema stewardship: keeping your Golden Entity Records aligned with operational reality as your products, services, and organisation evolve. For enterprise deployments, VISEON integrates with your existing BOAT infrastructure (Business Orchestration and Automation Technologies — Gartner) to govern and maintain the semantic layer over time. Where BOAT integration is in place, the catalog can be published in alignment with the Open Semantic Interchange (OSI) standard, enabling interoperability with AI agents, BI platforms, and analytics tools across any compliant platform. - id: https://viseon.io/faq/what-is-semantic-strategy/ url: https://viseon.io/faq/what-is-semantic-strategy/ question: What is Semantic Strategy? answer: Semantic Strategy is your organisation's plan for creating, managing and governing machine-readable data about your business, products and services. Just as Master Data Management became essential for ERP systems, Semantic Strategy is now essential as AI agents become the primary way customers and systems discover and interact with businesses. Semantic Strategy is one component within a broader ecosystem of strategies enabling the Information Superhighway - working alongside your data governance, API strategy and digital transformation initiatives. - id: https://viseon.io/faq/why-organisations-need-semantic-strategy/ url: https://viseon.io/faq/why-organisations-need-semantic-strategy/ question: Why does my organisation need a Semantic Strategy? answer: A Semantic Strategy gives AI agents a consistent, machine-readable account of your organisation, products and services. For agents working through MCP, an accessible endpoint is the route to querying that knowledge directly; a website alone does not make your organisation available in an MCP-only workflow. Your Agentic Catalogue combines governed entity definitions with the relationships and evidence needed to evaluate your offerings. It prepares your business for discovery and commerce through agent interfaces alongside your existing web presence. - id: https://viseon.io/faq/semantic-strategy-vs-existing-data-strategies/ url: https://viseon.io/faq/semantic-strategy-vs-existing-data-strategies/ question: How does Semantic Strategy relate to our existing data strategies? answer: Semantic Strategy operates at the intersection of your existing MDM, data governance and digital strategies. Think of it as "external MDM" - applying the same rigour to your outward-facing data products that you've applied internally for decades. Your Chief Data Officer or Chief AI Officer typically provides oversight, but successful implementation requires collaboration between data governance, marketing, product management and IT teams. - id: https://viseon.io/faq/how-to-implement-semantic-strategy/ url: https://viseon.io/faq/how-to-implement-semantic-strategy/ question: How do we implement a Semantic Strategy? answer: Implementation requires both strategic framework and enabling technology. The VISEON platform facilitates your Digital Catalogue, providing the infrastructure to create, manage and maintain the digital footprint of your brand in machine-readable formats that AI systems require. Your semantic data products need the same lifecycle management, quality assurance and governance processes you've established for internal master data - including version control, audit trails and validation against standards. - id: https://viseon.io/faq/risk-of-no-semantic-strategy/ url: https://viseon.io/faq/risk-of-no-semantic-strategy/ question: What happens if we don't have a Semantic Strategy? answer: Without a Semantic Strategy, your organisation risks being represented through incomplete, inconsistent or third-party information. In MCP-only workflows, an organisation without an accessible MCP endpoint cannot be queried directly through that channel. As agent-mediated research and purchasing grow, this can mean missed opportunities. Governing your entity definitions and publishing an Agentic Catalogue gives agents an authoritative source for understanding what you offer. - id: https://viseon.io/faq/what-is-viseon/ url: https://viseon.io/faq/what-is-viseon/ question: What is VISEON? answer: VISEON is a semantic intelligence platform developed and operated by Differentia Consulting. It audits and enriches your organisation's knowledge graph, resolves conflicting entity definitions and connects products, services, people and expertise into a governed, machine-readable catalogue. VISEON Ask lets visitors explore that knowledge conversationally and retains context for follow-up questions. Your MCP endpoint makes the graph directly queryable by compatible AI agents. The platform supports Discover, Discuss and Transact: agent discovery, conversational access and readiness for agentic commerce. - id: https://viseon.io/faq/what-is-digital-obscurity/ url: https://viseon.io/faq/what-is-digital-obscurity/ question: What is digital obscurity and why does it matter for AI search? answer: Digital obscurity is the risk that your organisation is absent, poorly understood or difficult to evaluate in an AI-mediated customer journey. In an MCP-only workflow, an organisation without an accessible MCP endpoint is unavailable for direct retrieval through that channel. VISEON addresses this by publishing a governed knowledge graph through MCP, with clear entity identities, connected relationships and evidence about your offerings. This gives compatible agents an authoritative source to query and cite. - id: https://viseon.io/faq/why-traditional-seo-fails/ url: https://viseon.io/faq/why-traditional-seo-fails/ question: How does VISEON extend traditional SEO for AI agents? answer: Traditional SEO focuses on ranking pages. VISEON also makes your organisation directly queryable by AI agents through MCP, using a governed knowledge graph. Clear entity definitions, connected relationships and evidenced distinctions help agents retrieve and compare your offerings without reconstructing them from scattered pages. - id: https://viseon.io/faq/what-viseon-does/ url: https://viseon.io/faq/what-viseon-does/ question: What does VISEON do to make brands discoverable to AI search engines? answer: VISEON audits and enriches Schema.org knowledge graphs across your digital presence. It identifies conflicting definitions, broken references and missing relationships, then establishes canonical entity records with supporting evidence. The resulting catalogue is published through MCP for direct agent retrieval and through VISEON Ask for conversational access by visitors. This gives agents a governed source for discovering and evaluating your products, services and expertise, and provides a foundation for agentic commerce. - id: https://viseon.io/faq/digital-twin/ url: https://viseon.io/faq/digital-twin/ question: What is a digital twin in the context of AI discoverability? answer: In VISEON, a digital twin is a machine-readable representation of the agreed scope of your organisation: its products, services, people and other relevant entities, with their properties and relationships. It uses canonical identifiers so an entity is defined once and referenced consistently across the graph. VISEON validates and maintains this representation, giving visitors and compatible AI agents a connected source of organisational knowledge. Its usefulness depends on the coverage, evidence and freshness of the underlying content. - id: https://viseon.io/faq/which-ai-engines/ url: https://viseon.io/faq/which-ai-engines/ question: Which AI search engines does VISEON optimise for? answer: VISEON publishes a governed knowledge graph using open standards and makes it queryable through MCP. It is intended for compatible AI assistants and agents, including clients that can connect to an MCP server. Availability depends on the client, its enabled tools and its connection to your endpoint. Published structured data also supports systems that consume it from the web. VISEON gives these systems an authoritative source; each external platform determines its own retrieval, citations and recommendations. - id: https://viseon.io/faq/graphrag/ url: https://viseon.io/faq/graphrag/ question: What is GraphRAG and how does VISEON enable it? answer: GraphRAG uses relationships in a knowledge graph to retrieve connected information for an AI answer. A question about a product can lead to its capabilities, related services, people and supporting evidence, rather than isolated passages. VISEON enriches and connects your graph so VISEON Ask and compatible agents can follow those relationships. This provides a stronger factual foundation for responses, while the quality of a generated answer still depends on the retrieved content and its interpretation. - id: https://viseon.io/faq/agentic-commerce/ url: https://viseon.io/faq/agentic-commerce/ question: What is agentic commerce and why does it require knowledge graph validation? answer: Agentic commerce is the use of AI agents to discover, evaluate and carry out purchases on behalf of users. A governed knowledge graph gives agents clear product identities, offers, policies and relationships to use in that process. VISEON validates and connects this information as an Agentic Catalogue. Completing a transaction also requires the relevant commerce integration, current operational data and permission to act; structured product information alone does not provide a checkout service. - id: https://viseon.io/faq/schema-org-validation/ url: https://viseon.io/faq/schema-org-validation/ question: Why is Schema.org validation critical for AI discoverability? answer: Schema.org validation checks whether your structured data uses appropriate types and properties. VISEON also checks canonical identifiers, references, duplicate definitions and relationship consistency across the graph. Requirements for a particular consumer, such as a Google rich result, are assessed separately. Valid markup makes information easier to interpret, but does not prove that a business claim is true: definitions and claims must also match your content and operational reality. - id: https://viseon.io/faq/cross-domain-schema/ url: https://viseon.io/faq/cross-domain-schema/ question: How does VISEON handle cross-domain knowledge graph consistency? answer: VISEON operates across all your domains to ensure consistent entity definitions and relationships. Many organisations have the same entities (Organization, Products, People) defined differently across multiple websites, creating conflicting information that confuses AI agents. VISEON implements a "define once, reference everywhere" approach using canonical @id URIs. We audit your entire digital footprint, identify duplicate or conflicting entities, establish authoritative definitions, and ensure all references point to the canonical source. This creates a unified knowledge graph that AI systems can trust, regardless of which domain they encounter first. - id: https://viseon.io/faq/mcp-protocol/ url: https://viseon.io/faq/mcp-protocol/ question: What is Model Context Protocol (MCP) and how does VISEON use it? answer: Model Context Protocol (MCP) is an open standard through which AI applications connect to tools and data sources. VISEON uses it to expose your organisation's governed knowledge graph, so connected agents can query entities, follow relationships and retrieve supporting information. The client must support MCP and have access to your endpoint. In an MCP-only workflow, this endpoint provides the direct retrieval route to your organisation. - id: https://viseon.io/faq/reduce-advertising/ url: https://viseon.io/faq/reduce-advertising/ question: How does VISEON help reduce advertising dependency? answer: VISEON adds a route to discovery through a governed knowledge graph and direct MCP access, alongside paid advertising. Connected agents can retrieve and evaluate your offerings in response to relevant customer questions. This can reduce dependence on paid placement where it generates useful enquiries and sales. The effect should be measured against your existing acquisition costs and conversion rates; publication alone does not guarantee recommendations or savings. - id: https://viseon.io/faq/how-to-get-started/ url: https://viseon.io/faq/how-to-get-started/ question: How do I get started with VISEON? answer: For VISEON for Web, getting started begins with a free Semantic Entity Assessment of your domain. It audits your site’s current knowledge graph, identifying missing, invalid or inconsistent Schema.org entities and properties, and includes Google Rich Result analysis, reachability checks and ontology compliance validation. Within minutes you’ll receive your Semantic Entity Assessment, highlighting specific gaps and risks, with recommendations for SEO 3.0, Semantic Entity Optimisation. For VISEON for Enterprise, we start with a fixed-fee Enterprise AI Readiness Assessment of one business function, credited against the pilot. Contact us at info@viseon.io or call +44 1494 622 600 to begin. - id: https://viseon.io/faq/is-there-a-free-trial/ url: https://viseon.io/faq/is-there-a-free-trial/ question: Is there a free trial of VISEON? answer: VISEON for Web starts free: the Semantic Entity Assessment is a free, fully automated audit of your domain’s Schema.org knowledge graph, delivered in minutes, with Google Rich Result analysis, reachability checks and ontology compliance validation, and recommendations for SEO 3.0, Semantic Entity Optimisation. VISEON for Enterprise starts with a fixed-fee Enterprise AI Readiness Assessment of one business function, credited against the pilot. Contact us at info@viseon.io or call +44 1494 622 600. - id: https://viseon.io/faq/does-viseon-have-an-mcp-server/ url: https://viseon.io/faq/does-viseon-have-an-mcp-server/ question: Does VISEON have an MCP server? answer: Yes. AI agents query VISEON’s knowledge graph through its Model Context Protocol (MCP) endpoint at https://viseon.io/mcp, with six read-only tools: get_entity, fuzzy_find_and_traverse, traverse_path, query_relationships, find_by_type and search_content. The endpoint is listed in VISEON’s ARD manifest and in the MCP Registry, so agents can discover it. People ask the same knowledge graph through VISEON Ask. VISEON for Web gives your own website the same: an MCP endpoint and VISEON Ask, answering from your knowledge graph. - id: https://viseon.io/faq/is-my-data-safe/ url: https://viseon.io/faq/is-my-data-safe/ question: Is my data safe with VISEON? answer: VISEON Data Assurance checks your data where it is: warehouse and lakehouse checks run as SQL inside your own engine, and files and cloud storage are processed in a temporary engine that is deleted when the run completes. Only the results come back: scores, counts, reasons and the IDs of failing rows, never the failing values. Personal data is reported as present rather than returned. VISEON for Web works with the Schema.org knowledge graph your website already publishes. For your deployment, contact info@viseon.io. - id: https://viseon.io/faq/who-are-viseons-customers/ url: https://viseon.io/faq/who-are-viseons-customers/ question: Who are VISEON’s customers? answer: VISEON for Web clients include The Harris Poll UK, Alfanar, Laura’s House, ATP Construction LLC, Anna Elliot, QlickXL and The Lovely Barns. Each has published a review of VISEON for Web on viseon.io. - id: https://viseon.io/faq/viseon-case-studies/ url: https://viseon.io/faq/viseon-case-studies/ question: Do you have VISEON case studies? answer: Seven VISEON for Web clients have published reviews of their results on viseon.io: The Harris Poll UK, Alfanar, Laura’s House, ATP Construction LLC, Anna Elliot, QlickXL and The Lovely Barns. Ask about any of them to read what they said. - id: https://viseon.io/faq/upcoming-viseon-events/ url: https://viseon.io/faq/upcoming-viseon-events/ question: What VISEON events are coming up? answer: Qlik Customer Day, Thursday 15 October 2026, at the Green Park Conference Centre in Reading: Differentia Consulting’s free day on the ABCs of AI (Audit, Build, Connect), with a keynote from The Harris Poll UK on VISEON for Web, and VISEON sessions on VISEON for Qlik (vCat), VISEON for Web and the launch of VISEON Data Assurance for Qlik. - id: https://viseon.io/faq/discoverability-assessment-details/ url: https://viseon.io/faq/discoverability-assessment-details/ question: What does the Semantic Entity Assessment include? answer: The Semantic Entity Assessment is a free audit of your domain’s JSON-LD Schema.org knowledge graph. It counts missing, invalid, or inconsistent entities and properties; analyses Google Rich Result compliance and index status; performs reachability analysis to identify orphaned entities; validates cross-domain schema consistency; checks @id reference integrity; assesses entity relationship accuracy; and evaluates ontology compliance with Schema.org standards. The assessment delivers a pass/fail rating with specific remediation guidance, highlighting which issues require immediate attention to strengthen AI discoverability. - id: https://viseon.io/faq/implementation-timeline/ url: https://viseon.io/faq/implementation-timeline/ question: How long does VISEON implementation take? answer: VISEON for Web is delivered in four stages. Assess: a free, fully automated Semantic Entity Assessment of your domain, in minutes. Deploy: one to three days on your domain, included in the paid plans: we agree the change with you, configure sync and a validation baseline, release VISEON Ask and your MCP endpoint, with WebMCP where included in the agreed scope, publish your ARD manifest and MCP Registry listing, then validate and test. Build: we draft your ontology straight away, and the timeline is set by how quickly your team agrees it, typically one to three months. Run: ongoing governance, weekly catalogue health reports, change monitoring, with live prices and stock where the appropriate change data capture integration is included. For VISEON for Enterprise, delivery starts with a fixed-fee Enterprise AI Readiness Assessment and an eight-week pilot in one business function. - id: https://viseon.io/faq/who-uses-viseon/ url: https://viseon.io/faq/who-uses-viseon/ question: Which types of organisations benefit from VISEON? answer: VISEON serves growing businesses spending significant advertising budgets to compete with larger competitors; B2B organisations where decision-makers research solutions using AI search engines; seasonal companies needing organic discovery during off-peak advertising periods; international brands requiring consistent visibility across multiple regions and markets; enterprise organisations with complex digital footprints spanning multiple domains; CMOs, webmasters, and SEO professionals managing continuous AI optimisation; and CDOs responsible for Agentic Commerce strategy. - id: https://viseon.io/faq/why-json-ld/ url: https://viseon.io/faq/why-json-ld/ question: Why does VISEON prioritise JSON-LD over other Schema.org formats? answer: JSON-LD lets VISEON express entity definitions and relationships in a machine-readable format separate from page presentation. Stable @id references support linking across pages and domains, while a consistent format simplifies validation and maintenance. It is a practical foundation for a governed knowledge graph and for publishing connected information to compatible consumers. - id: https://viseon.io/faq/google-rich-results/ url: https://viseon.io/faq/google-rich-results/ question: Does VISEON help with Google Rich Results? answer: Yes. VISEON assesses structured data against the requirements for applicable Google rich-result features. Eligibility depends on the content type, current Google policies and the quality of the page; valid markup does not guarantee that Google will display an enhanced result. VISEON also governs the entities as one connected graph and serves that knowledge directly to AI agents through MCP. - id: https://viseon.io/faq/differentia-consulting/ url: https://viseon.io/faq/differentia-consulting/ question: What is the relationship between VISEON and Differentia Consulting? answer: VISEON is developed and operated by Differentia Consulting, a UK-based business intelligence consultancy established in 2002 and Qlik Elite Partner since 2008. Differentia has helped organisations prepare for AI and AI-driven search since before it became mainstream, serving 500+ clients globally. The team's deep expertise in data analytics, semantic web technologies, and enterprise architecture informed VISEON's design as a semantic intelligence platform. - id: https://viseon.io/faq/marketing-cost-reduction/ url: https://viseon.io/faq/marketing-cost-reduction/ question: How much can VISEON reduce my marketing costs? answer: There is no fixed saving that applies to every organisation. VISEON creates an additional route to discovery and enquiry through your knowledge graph, VISEON Ask and MCP access. Measure the effect using qualified leads, conversions and acquisition costs against a baseline, including the cost of implementation and ongoing governance. Those results show whether you can reduce advertising dependency while maintaining useful demand. - id: https://viseon.io/faq/agentic-catalogue/ url: https://viseon.io/faq/agentic-catalogue/ question: What is an agentic catalogue and how does VISEON create one? answer: An Agentic Catalogue is a governed, machine-readable account of your organisation and its offerings, connected for agent retrieval. VISEON creates it by auditing entity definitions, resolving conflicts, establishing canonical identifiers and enriching relationships with evidence. It supports Discover through direct agent access, Discuss through VISEON Ask, and Transact readiness through clear product, offer and policy information. Live commerce actions require the appropriate operational integrations. - id: https://viseon.io/faq/voice-commerce-schema/ url: https://viseon.io/faq/voice-commerce-schema/ question: How does VISEON support voice commerce and smart assistants? answer: VISEON prepares connected product, offer and organisation information that compatible voice assistants and agents can retrieve. Clear identifiers, current pricing, availability and delivery information help an assistant evaluate an offer. Voice-platform access and transaction execution depend on the relevant integrations and permissions; the knowledge graph provides the information foundation. - id: https://viseon.io/faq/social-commerce-schema/ url: https://viseon.io/faq/social-commerce-schema/ question: Does VISEON help with social commerce platforms like TikTok Shop? answer: VISEON helps establish consistent product identifiers, descriptions, categories, offers and policies that can support social-commerce catalogues. Each channel has its own feed or API requirements, so synchronisation requires a channel-specific integration. A governed product definition helps keep those feeds aligned with the same source information. - id: https://viseon.io/faq/international-schema/ url: https://viseon.io/faq/international-schema/ question: How does VISEON support international brands across multiple markets? answer: International expansion requires consistent Schema.org implementation across different countries, languages, and regional domains. VISEON audits knowledge graphs globally, ensuring your Organisation entity maintains canonical identity whilst regional variations are properly structured. - id: https://viseon.io/faq/viseon-ask-widget/ url: https://viseon.io/faq/viseon-ask-widget/ question: What is VISEON Ask and how does it work? answer: VISEON Ask is the conversational interface for your organisation's knowledge graph. VISEON enriches the graph and connects your products, services, people and expertise so visitors can explore them through natural-language questions. Ask answers from that knowledge and retains context within the conversation, so visitors can ask follow-up questions naturally. Ask Premium adds tailored personality and role behaviour, reflecting your organisation's culture, training and working practices. - id: https://viseon.io/faq/pedagogical-mechanical-personalisation/ url: https://viseon.io/faq/pedagogical-mechanical-personalisation/ question: What does Ask Premium add to VISEON Ask? answer: Ask Premium builds on VISEON Ask with a tailored personality and way of working. It can be shaped around your organisation's culture, training, communication style and role expectations: for example, a sales adviser, product specialist or support engineer. Pedagogy includes how it explains, guides and helps people learn; role behaviour includes how it approaches questions and works within agreed boundaries. These are tailored capabilities of Ask Premium, rather than features of every basic Ask deployment. - id: https://viseon.io/faq/enterprise-features/ url: https://viseon.io/faq/enterprise-features/ question: What enterprise features does VISEON offer for complex organisations? answer: VISEON enterprise solutions address complex multi-domain digital footprints with advanced features: cross-domain knowledge graph orchestration ensuring entity consistency across dozens or hundreds of websites; automated schema synchronisation detecting and reconciling conflicts; governance workflows with approval chains for entity definitions; change tracking and audit trails for compliance; API access for integration with existing martech stacks. - id: https://viseon.io/faq/microsoft-nlweb/ url: https://viseon.io/faq/microsoft-nlweb/ question: How does VISEON align with Microsoft NLWeb principles? answer: VISEON and Microsoft NLWeb share the goal of making published web knowledge accessible through natural-language interfaces. VISEON focuses on a governed, connected entity graph and provides conversational access through Ask and agent access through MCP. Compatibility and deployment requirements should be assessed for the specific NLWeb integration. - id: https://viseon.io/faq/json-ld-vs-microdata/ url: https://viseon.io/faq/json-ld-vs-microdata/ question: Should I use JSON-LD, Microdata, or RDFa for Schema.org markup? answer: JSON-LD, Microdata and RDFa are supported ways to express Schema.org structured data. VISEON prioritises JSON-LD because it separates entity definitions from presentation, supports explicit identifiers and connected references, and simplifies validation and maintenance across a website. Choose a format your team can keep accurate and consistent; the format alone does not determine whether an AI system will retrieve or cite your content. - id: https://viseon.io/faq/knowledge-graph-vs-taxonomy/ url: https://viseon.io/faq/knowledge-graph-vs-taxonomy/ question: What is the difference between a knowledge graph and a taxonomy? answer: A taxonomy provides hierarchical classification (e.g., Products > Electronics > Laptops), whilst a knowledge graph represents entities and their multi-dimensional relationships. Taxonomies answer "what category?" whilst knowledge graphs answer "what is this, how does it relate to other things, and what are its properties?" - id: https://viseon.io/faq/entity-id-importance/ url: https://viseon.io/faq/entity-id-importance/ question: Why are unique entity IDs (@id) so important for AI discoverability? answer: Unique entity IDs (@id) give agents an explicit way to recognise the same entity across pages and distinguish it from related products or variants. Consistent references reduce ambiguity and support traversal of the knowledge graph without relying solely on matching names or descriptions. - id: https://viseon.io/faq/competitive-advantage/ url: https://viseon.io/faq/competitive-advantage/ question: How does AI discoverability provide competitive advantage? answer: A governed knowledge graph and accessible MCP endpoint give connected agents a direct route to evaluate your offerings. Distinctive, evidenced capabilities and clear relationships help them compare products and services in response to customer needs. In an MCP-only workflow, this access is essential to direct retrieval. The advantage comes from being accessible, useful and current; an external agent still chooses which sources to use and recommend. - id: https://viseon.io/faq/ongoing-maintenance/ url: https://viseon.io/faq/ongoing-maintenance/ question: Does VISEON require ongoing maintenance after initial implementation? answer: Yes. Knowledge graphs require continuous maintenance as your organisation evolves. Products are added or discontinued, services expand, personnel changes, contact information updates, events occur, and policies shift. VISEON offers ongoing managed services to ensure your knowledge graph remains current, accurate, and compliant with evolving Schema.org standards. - id: https://viseon.io/faq/cms-compatibility/ url: https://viseon.io/faq/cms-compatibility/ question: Does VISEON only work with WordPress or support other CMS platforms? answer: VISEON audits published Schema.org knowledge graphs regardless of the CMS that generates them, including WordPress, Shopify, Drupal, Joomla and custom websites. Implementation depends on how your platform publishes and maintains its structured data. The assessment identifies corrections and enrichment needed across that output; platform-specific deployment is agreed as part of the engagement. - id: https://viseon.io/faq/security-privacy/ url: https://viseon.io/faq/security-privacy/ question: How does VISEON handle security and data privacy? answer: The free public-website Semantic Entity Assessment analyses publicly accessible pages and structured data; it does not require website admin credentials or access to private systems. Enterprise and Data Assurance deployments have a different scope and use agreed access controls and deployment arrangements. Their handling of private data should be assessed against the selected product and mode, rather than inferred from the public website audit. - id: https://viseon.io/faq/measuring-success/ url: https://viseon.io/faq/measuring-success/ question: How do I measure VISEON success and ROI? answer: Start with a baseline for graph quality, agent accessibility and business outcomes. Compare entity coverage, reference integrity and relationship completeness after enrichment, then test whether realistic customer questions can be answered with relevant evidence through MCP and Ask. Where measurement is available, track useful queries, qualified enquiries, conversions and acquisition costs. Google rich-result eligibility and AI referral traffic are additional measures, rather than the whole value of direct agent access. - id: https://viseon.io/faq/schema-version-compatibility/ url: https://viseon.io/faq/schema-version-compatibility/ question: Which Schema.org versions does VISEON support? answer: VISEON validates against current Schema.org specifications whilst maintaining compatibility with legacy implementations. Schema.org evolves continuously with new entity types and properties added regularly. - id: https://viseon.io/faq/support-contact/ url: https://viseon.io/faq/support-contact/ question: How do I get support or contact VISEON? answer: For VISEON enquiries, support, or to schedule your AI Discoverability Assessment, contact us at info@viseon.io or call +44 1494 622 600. Our team responds to all enquiries within one business day. - id: https://viseon.io/faq/topic-clusters-vs-traditional-seo/ url: https://viseon.io/faq/topic-clusters-vs-traditional-seo/ question: What is the difference between topic clusters and traditional SEO? answer: Topic clusters organise a subject into a pillar and useful supporting articles, connected through internal links. VISEON adds explicit topic and content relationships to the knowledge graph so connected agents can traverse that material through MCP. This complements page-level SEO by making the relationships between your expertise, offerings and supporting evidence directly queryable. - id: https://viseon.io/faq/ai-agents-discover-topic-clusters/ url: https://viseon.io/faq/ai-agents-discover-topic-clusters/ question: How do AI agents discover topic cluster relationships? answer: Connected agents can follow explicit relationships in the knowledge graph to find related content. isPartOf and hasPart can express content hierarchy; about identifies the subject of a page, subjectOf links a subject to content about it, and mentions records other referenced entities. VISEON checks those links for consistency so its traversal tools can retrieve relevant supporting material. - id: https://viseon.io/faq/schema-properties-topic-clusters/ url: https://viseon.io/faq/schema-properties-topic-clusters/ question: What Schema.org properties link pillar pages to cluster content? answer: Use isPartOf and hasPart where pages genuinely form a content hierarchy. Use about to identify the topic or entity an article concerns, subjectOf for the corresponding link from that subject to the article, and mentions for other referenced entities. These properties express different relationships and should not be used interchangeably. VISEON can maintain reciprocal links to support traversal; explicitly publishing every inverse edge is a graph-governance choice, rather than a universal Schema.org requirement. - id: https://viseon.io/faq/cluster-articles-per-pillar/ url: https://viseon.io/faq/cluster-articles-per-pillar/ question: How many cluster articles should support each pillar page? answer: As a planning guideline, many pillars work well with roughly 5-10 substantive supporting articles, each addressing a useful subtopic in enough depth. Around 1,500 words may suit some subjects, but neither article count nor word count is a threshold. Prioritise distinctive evidence and customer questions, then connect each article to its subject and related content in the knowledge graph. - id: https://viseon.io/faq/topic-clusters-without-schema/ url: https://viseon.io/faq/topic-clusters-without-schema/ question: Can topic clusters work without Schema.org markup? answer: Yes. Internal links and clear content structure support topic clusters for visitors and web-based retrieval. Explicit graph relationships add a direct traversal route for agents using MCP. VISEON connects the subjects and supporting content so those agents can retrieve the relationships without reconstructing them from separate pages. - id: https://viseon.io/faq/validate-topic-cluster-schema/ url: https://viseon.io/faq/validate-topic-cluster-schema/ question: How do I validate my topic cluster Schema implementation? answer: Use validator.schema.org to inspect structured-data syntax, types and properties. Then check canonical identifiers, resolved references, hierarchy and topic relationships across the graph. VISEON's assessment identifies orphaned entities and reference issues, and traversal tests check whether related content can actually be retrieved. Assess Google rich-result eligibility separately for the content types where a supported feature applies. - id: https://viseon.io/faq/how-long-does-schema-org-implementation-take/ url: https://viseon.io/faq/how-long-does-schema-org-implementation-take/ question: How long does Schema.org implementation take? answer: The time depends on the scope and the quality of your existing content and entity definitions. A small markup correction is different from building an agreed ontology across multiple domains. For VISEON for Web, initial deployment typically takes one to three days; ontology development and agreement typically take one to three months. VISEON for Enterprise begins with an AI Readiness Assessment and an eight-week pilot in one business function. The engagement sets the scope and delivery milestones. - id: https://viseon.io/faq/what-is-the-difference-between-atp-and-ucp/ url: https://viseon.io/faq/what-is-the-difference-between-atp-and-ucp/ question: What is the difference between ATP and UCP? answer: ATP (Available To Promise) is an operational calculation of what quantity can be committed and when. UCP (Universal Commerce Protocol) defines how commerce systems and agents discover supported capabilities and interact through agreed data models and interfaces. Current ATP information can support an offer or checkout workflow, but calculating availability and implementing a commerce protocol are different responsibilities. - id: https://viseon.io/faq/what-is-semantic-drift/ url: https://viseon.io/faq/what-is-semantic-drift/ question: What is semantic drift and why does it matter for AI systems? answer: Semantic drift is an established term in ontology research describing how the meaning of concepts gradually changes across ontology versions over time as a domain evolves. A term defined one way in version 1 may carry a subtly different meaning by version 5, as usage, business context, or domain understanding shifts. This is a versioning and maintenance problem: the ontology becomes documentation of what the organisation used to mean, not what it currently means. In AI systems, semantic drift creates compounding risk because AI agents treat ontology definitions as ground truth. For the broader failure mode of meaning lost between organisational intent and AI interpretation at a given point in time — regardless of versioning — see Semantic Equivocation. - id: https://viseon.io/faq/does-data-assurance-move-our-data/ url: https://viseon.io/faq/does-data-assurance-move-our-data/ question: Does VISEON Data Assurance move our data out of our environment? answer: Warehouse and lakehouse checks run as SQL inside your own engine. Files and cloud storage are processed at the edge, in a temporary engine that is deleted when the run completes. Only the results come back: scores, counts, reasons and the IDs of failing rows, never the failing values. - id: https://viseon.io/faq/how-does-data-assurance-handle-personal-data/ url: https://viseon.io/faq/how-does-data-assurance-handle-personal-data/ question: How does VISEON Data Assurance handle personal data? answer: Personal data is detected and reported as present, rather than returned as failing data values. Assessment findings identify failing records by row ID so you can locate and fix them at source. The location and handling of source data depend on the assessment mode and agreed deployment; confirm those arrangements for warehouse, file and cloud-storage assessments. - id: https://viseon.io/faq/is-data-assurance-a-compliance-certification/ url: https://viseon.io/faq/is-data-assurance-a-compliance-certification/ question: Is VISEON Data Assurance a compliance certification? answer: No. It is evidence toward compliance. Findings cite the clause they relate to, for example ISO/IEC 29100 or GDPR Article 5, and anything that cannot be assessed from the data is marked not assessed rather than guessed. - id: https://viseon.io/faq/which-sources-can-data-assurance-assess/ url: https://viseon.io/faq/which-sources-can-data-assurance-assess/ question: Which data sources can VISEON Data Assurance assess? answer: Snowflake, Databricks, Google BigQuery, Amazon Redshift, Azure Synapse, Microsoft Fabric, Qlik Open Lakehouse, Apache Iceberg tables, Oracle, SQL Server, PostgreSQL, MySQL, Amazon S3, Azure Blob and ADLS, Google Cloud Storage, SharePoint and OneDrive, plus Parquet, CSV, Excel, JSON and Qlik QVD files, on-premises file shares, and the data inside a Qlik app. - id: https://viseon.io/faq/what-can-the-data-assurance-ai-assistant-do/ url: https://viseon.io/faq/what-can-the-data-assurance-ai-assistant-do/ question: What can the VISEON Data Assurance AI assistant do, and not do? answer: It can answer from everything you are allowed to see, re-run an assessment, and, for admins, record approval decisions. It cannot create a dataset or edit a rule, compliance policy or attestation, and it never receives your data values. - id: https://viseon.io/faq/can-data-assurance-reach-on-premises-data/ url: https://viseon.io/faq/can-data-assurance-reach-on-premises-data/ question: Can VISEON Data Assurance reach on-premises data? answer: Yes. The VISEON agent runs as a Windows service inside your network, keeps database passwords on your machine and reads only the folders you allow. No inbound connections or open ports: the agent only makes outbound HTTPS calls on port 443. It does not require a VPN. It contacts the VISEON API to collect jobs and report progress, and uploads results to VISEON S3 so they can be surfaced in the tool. The outbound-only connection model applies to all modes. - id: https://viseon.io/faq/how-is-an-enterprise-knowledge-graph-different-from-a-data-catalogue/ url: https://viseon.io/faq/how-is-an-enterprise-knowledge-graph-different-from-a-data-catalogue/ question: How is VISEON for Enterprise different from a data catalogue or a semantic layer? answer: A catalogue inventories data assets; a semantic layer defines metrics for analytics. VISEON: Enterprise models the business itself: its entities, their relationships, the rules that govern them and the documents that explain them. Catalogues and semantic layers become sources for that model, not replacements for it. - id: https://viseon.io/faq/why-add-viseon-to-a-data-platform-ontology/ url: https://viseon.io/faq/why-add-viseon-to-a-data-platform-ontology/ question: We already have an ontology in our data platform. Why add VISEON for Enterprise? answer: You would not be adding a competing one. A platform’s ontology describes the data inside that platform. VISEON federates it with the definitions in your other systems and documents, so there is one canonical identifier per entity across all of them. The platform ontology keeps working and is linked, not replaced. - id: https://viseon.io/faq/how-does-viseon-for-enterprise-compare-with-palantir/ url: https://viseon.io/faq/how-does-viseon-for-enterprise-compare-with-palantir/ question: How does VISEON for Enterprise compare with Palantir? answer: Both model the enterprise as an ontology across structured and unstructured data, and both can work with data where it lives. The difference is where the ontology itself lives. Palantir’s is defined and run inside Palantir’s platform. VISEON’s is written in open standards (Schema.org, JSON-LD and RDF-compatible identifiers), so the model is portable, and selected parts of it can be published externally for AI search and agentic commerce. - id: https://viseon.io/faq/does-viseon-for-enterprise-move-our-data/ url: https://viseon.io/faq/does-viseon-for-enterprise-move-our-data/ question: Does VISEON for Enterprise move our data? answer: No. The graph holds definitions, relationships, rules and references to your sources. Queries are answered through MCP behind your existing access controls. - id: https://viseon.io/faq/viseon-ask-vs-mcp-endpoint/ url: https://viseon.io/faq/viseon-ask-vs-mcp-endpoint/ question: What is the difference between VISEON Ask and the VISEON MCP endpoint? answer: VISEON Ask is MCP for people. VISEON Ask is the common interface: plain English, links to the sources behind each answer, and context kept for follow-up questions, whichever product it sits in front of. Behind it, a worker tailored to the need, such as the VISEON for Web MCP or the Enterprise Ask MCP, reads the governed knowledge graph, and that worker’s MCP endpoint lets compatible AI assistants and agents call the tools directly. A person and an AI agent therefore work from the same facts and sources. Context shapes how an answer is put, and access permissions decide what is shown; the facts and their sources stay the same. - id: https://viseon.io/faq/how-is-viseon-different-from-ai-visibility-monitoring-tools/ url: https://viseon.io/faq/how-is-viseon-different-from-ai-visibility-monitoring-tools/ question: How is VISEON for Web different from AI-visibility monitoring tools? answer: Monitoring tools report what AI assistants say about you today. Monitoring tools do not change what those assistants can read. VISEON for Web publishes the governed, canonical catalogue that assistants read from, on your own domain, and the free assessment shows where your catalogue is missing or in conflict. The two work together: monitor the output, govern the source. - id: https://viseon.io/faq/can-we-build-this-ourselves-with-nlweb-or-other-open-standards/ url: https://viseon.io/faq/can-we-build-this-ourselves-with-nlweb-or-other-open-standards/ question: Can we build VISEON for Web ourselves with NLWeb or other open standards? answer: NLWeb, Schema.org, JSON-LD and MCP are the building blocks, and VISEON is built on them, which is why the result is portable and yours. What VISEON for Web adds is the work around them: auditing and reconciling the schema you already have, one canonical entity for each organisation, product, service and person, answers that cite their source and say when the graph does not know, discovery listings (ARD and the MCP Registry), and governance as your site and offer change. Standing up an endpoint is the easy part; keeping the catalogue correct is the ongoing job. - id: https://viseon.io/faq/which-standards-is-viseon-for-enterprise-built-on/ url: https://viseon.io/faq/which-standards-is-viseon-for-enterprise-built-on/ question: Which standards is VISEON for Enterprise built on? answer: Schema.org and JSON-LD for the entities and relationships, RDF-compatible identifiers, the Model Context Protocol for agent access, and NLWeb for natural-language access to published content. Metric definitions can align with the Open Semantic Interchange specification. - id: https://viseon.io/faq/what-is-seo-3/ url: https://viseon.io/faq/what-is-seo-3/ question: What is SEO 3.0? answer: SEO 3.0 is Semantic Entity Optimisation: optimising the entities your organisation is and offers, rather than individual pages or keywords. Each entity gets one canonical definition, a stable identifier, verified links and provenance, so AI search and AI agents can identify, understand and cite it directly. - id: https://viseon.io/faq/how-is-seo-3-different-from-geo/ url: https://viseon.io/faq/how-is-seo-3-different-from-geo/ question: How is SEO 3.0 different from GEO? answer: Generative Engine Optimisation (GEO) focuses on how an organisation is represented in generated answers. VISEON uses SEO 3.0, Semantic Entity Optimisation, to govern the underlying entities, their definitions, relationships and evidence, and publish them through MCP. This gives connected agents an authoritative source to query directly. Clear content and a governed graph can work together; neither guarantees how every external engine will phrase an answer. - id: https://viseon.io/faq/what-does-an-mcp-endpoint-on-our-website-do/ url: https://viseon.io/faq/what-does-an-mcp-endpoint-on-our-website-do/ question: What does an MCP endpoint on our website do? answer: An MCP endpoint on your website lets AI assistants and agents query your organisation’s knowledge graph directly, over the Model Context Protocol, instead of scraping and guessing. The MCP endpoint is the direct agent connection to Interact, the hub on your domain; VISEON Ask is the same MCP interface for people, answering your visitors from the same graph. Where agents need to act in the browser, WebMCP extends the MCP endpoint. - id: https://viseon.io/faq/what-are-ard-and-the-mcp-registry/ url: https://viseon.io/faq/what-are-ard-and-the-mcp-registry/ question: What are ARD and the MCP Registry, and why publish to them? answer: Agentic Resource Discovery (ARD) describes a manifest on your domain that identifies the resources you offer. The MCP Registry is a directory of MCP servers used by compatible discovery services. Publishing these resources helps agents and directories find your endpoint. An agent must still use an applicable discovery route and be configured or permitted to connect; listing an endpoint does not automatically connect every AI assistant. - id: https://viseon.io/faq/transact-vs-acp-ucp/ url: https://viseon.io/faq/transact-vs-acp-ucp/ question: What is the difference between VISEON:Transact and simply implementing ACP or UCP? answer: ACP and UCP define interfaces and data models for commerce interactions. VISEON:Transact prepares the knowledge and operational foundation those interactions depend on: governed product and offer definitions, consistent business meaning, current data and endpoint readiness. The scope includes the relevant commerce integrations and validation. Transaction reliability also depends on those connected systems, permissions and operational conditions; implementing a graph or protocol alone does not guarantee completion. - id: https://viseon.io/faq/what-is-osi-agentic-commerce/ url: https://viseon.io/faq/what-is-osi-agentic-commerce/ question: What is OSI and why does it matter for agentic commerce? answer: Open Semantic Interchange (OSI) is an initiative for sharing semantic metadata across participating data and analytics platforms. It provides a common specification for business definitions such as metrics and dimensions. For agentic commerce, consistent definitions help reduce mismatches between the information an agent retrieves and the meaning used by operational systems. Governance and current data remain necessary to maintain that alignment. - id: https://viseon.io/faq/transact-where-to-start/ url: https://viseon.io/faq/transact-where-to-start/ question: Where do we start with agentic commerce if we are not yet discoverable by AI agents? answer: Start with a VISEON Semantic Entity Assessment to identify gaps in your current entity definitions, relationships and supporting evidence. Then agree the product and offer information, business rules, live data requirements and commerce integrations needed for the intended workflow. VISEON:Transact readiness builds on this governed foundation, with validation of both the knowledge graph and the connected transaction capabilities. - id: https://viseon.io/faq/ url: https://viseon.io/faq/ question: answer: - id: https://viseon.io/transact/#faq-needs-qlik url: https://viseon.io/transact/#faq-needs-qlik question: Do we need Qlik to implement VISEON:Transact? answer: No. VISEON:Transact readiness is platform-agnostic at the data pipeline layer. For organisations already using Qlik for data integration and ERP connectivity, the Differentia Consulting and VISEON combination offers a natural path: Qlik supplies the governed, real-time data products; VISEON projects them into the Schema.org knowledge graph and OCP-ready endpoints. - id: https://viseon.io/transact/#faq-erp-real-time url: https://viseon.io/transact/#faq-erp-real-time question: Is our ERP system compatible with real-time agent transactions? answer: This depends on your current ERP architecture and data pipeline design. Many enterprise ERP systems — including SAP and JD Edwards — support real-time data access but are often configured for batch processing in practice. A VISEON:Transact readiness assessment evaluates your current pipeline architecture and identifies the specific changes required to support live agent queries. - id: https://viseon.io/transact/#faq url: https://viseon.io/transact/#faq question: answer: - id: https://viseon.io/viseon-data-assurance/#faq url: https://viseon.io/viseon-data-assurance/#faq question: answer: - id: https://viseon.io/viseon-for-enterprise/#faq url: https://viseon.io/viseon-for-enterprise/#faq question: answer: - id: https://viseon.io/viseon-for-web/#faq url: https://viseon.io/viseon-for-web/#faq question: answer: alternate: https://data-lake-seo.s3.eu-north-1.amazonaws.com/viseon-v2/https---viseon-io/schema-txt-json-files/faqpages.json # Service Data @type: Service @id: - id: https://viseon.io/services/#knowledge-graph-solution url: https://viseon.io/services/ name: Knowledge Graph Solution description: End-to-end knowledge graph implementation, validation, and ongoing maintenance. Transforms unstructured website content into a complete, AI-traversable entity graph using Schema.org and JSON-LD. - id: https://viseon.io/services/#knowledge-graph-assessment url: https://viseon.io/semantic-entity-assessment/ name: Semantic Entity Assessment description: The free first step of VISEON for Web: an audit of one domain's Schema.org knowledge graph. Identifies missing entities, broken @id references, invalid property usage and AI discoverability gaps, with recommendations for SEO 3.0, Semantic Entity Optimisation. Fully automated, with results in minutes; free, once per domain. - id: https://viseon.io/services/#ai-readiness-assessment url: https://viseon.io/viseon-for-enterprise/ name: Enterprise AI Readiness Assessment description: The fixed-fee first step of VISEON for Enterprise, scoped to one business function (and, for a multi-location company, one location). We inventory your architecture: data platforms, warehouse or lakehouse, BI, semantic and metrics layers, data catalogue, identity and permissions, and the AI agents you run or plan. We map where the same customers, products and metrics are defined differently across those tools, and measure one source that function relies on with VISEON Data Assurance. You receive a readiness report, an architecture map and a costed proposal for the eight-week pilot, and the fee is credited against the pilot. - id: https://viseon.io/services/#schema-implementation url: https://viseon.io/services/ name: Schema.org Implementation description: Professional Schema.org structured data implementation for AI discoverability. Includes entity definition, JSON-LD authoring, WordPress integration, and validation against Schema.org specifications. Delivered through VISEON for Web (automated) or VISEON for Enterprise (managed), not sold as a standalone line. - id: https://viseon.io/services/#enterprise-data-assurance url: https://viseon.io/viseon-data-assurance/ name: VISEON Data Assurance for Enterprise description: Deployment of VISEON Data Assurance across an organisation's own data estate: warehouses, lakehouses, databases, cloud storage and on-premises data through the VISEON agent. Establishes where the data stands on AI readiness, with MCP-ready governance over the private data estate in as little as 3 days, then monitoring, alerting and compliance evidence along the data pipeline's lineage. - id: https://viseon.io/services/#data-assurance-for-qlik url: https://viseon.io/viseon-data-assurance/ name: VISEON Data Assurance for Qlik description: Deployment of VISEON Data Assurance for Qlik customers: assesses Qlik app data, QVD files and Qlik Open Lakehouse tables, inside Qlik Cloud through the VISEON Data Assurance Qlik Cloud extension, beside the analytics built on them. - id: https://viseon.io/services/#aira url: https://www.differentia.consulting/smarter.bi/solutions/smarter-ai-powered-by-qlik/ name: AIRA: AI Readiness Assessment description: AIRA, the AI Readiness Assessment from Differentia Consulting, prepares a Qlik Cloud estate for AI. It is a structured, scored assessment of a Qlik application's readiness for AI consumption across five dimensions, giving a composite AI Readiness Index with a pillar-by-pillar breakdown. Before Qlik Answers, the Qlik MCP Server or outside AI assistants answer from your apps, it audits how they are built and described (load scripts, data models, expressions, master items and objects) with VISEON tooling and Differentia's Qlik expertise, and finds the mistakes that give wrong numbers that look right, such as master measures that break Qlik Answers filters, data model problems, deprecated objects and unused data. Every assessment ends with a prioritised action plan, which Differentia Consulting's managed services can implement. - id: https://viseon.io/services/#zero-trust-mcp-gateway url: https://viseon.io/viseon-for-enterprise/ name: VISEON Zero Trust MCP Gateway description: VISEON's own MCP gateway, built in-house and running on an OpenZiti Zero Trust network. Every agent and client connection is authorised by identity before any network path exists, and MCP servers have no inbound ports and no public endpoints. The VISEON for Enterprise MCP hub sits behind it, and VISEON helps organisations put their own MCP servers behind it, across clouds and on-premises. With VISEON for Enterprise it governs both which identities may reach which MCP server, and what each server's data means. - id: https://viseon.io/services/#discuss-deployment url: https://viseon.io/services/ name: VISEON:Discuss Deployment description: Deployment of the VISEON:Discuss conversational interface on a client website. Requires a validated VISEON knowledge graph as foundation. Includes embedded JavaScript component, knowledge graph endpoint connection, content index configuration, and query intent analytics. - id: https://viseon.io/services/#transact-readiness url: https://viseon.io/services/ name: VISEON:Transact Readiness Assessment description: Assessment and preparation of a commerce organisation's Schema.org knowledge graph, semantic consistency (OSI), real-time data pipeline, and OCP (Open Commerce Protocol: ACP, UCP, AP2) endpoint readiness — the five-layer stack required for AI agents to complete purchases reliably. Delivered in combination with Differentia Consulting ERP expertise and Qlik data integration where relevant. alternate: https://data-lake-seo.s3.eu-north-1.amazonaws.com/viseon-v2/https---viseon-io/schema-txt-json-files/services.json # NewsArticle Data @type: NewsArticle @id: - id: https://www.differentia.consulting/news/qlik-luminary-2017-adrian-parker/#article url: https://www.differentia.consulting/news/qlik-luminary-2017-adrian-parker/ name: Adrian Parker Qlik Luminary 2017 description: - id: https://www.differentia.consulting/news/adrian-parker-qlik-luminary-2018/#article url: https://www.differentia.consulting/news/adrian-parker-qlik-luminary-2018/ name: Adrian Parker Qlik Luminary 2018 description: - id: https://www.differentia.consulting/news/qlik-luminaries-2020/#article url: https://www.differentia.consulting/news/qlik-luminaries-2020/ name: Qlik Luminaries 2020 - Adrian Parker description: - id: https://www.differentia.consulting/article/ai-search-channel-strategic-imperative-2026/#news url: https://www.differentia.consulting/article/ai-search-channel-strategic-imperative-2026/ name: The AI Search Channel: Why It Must Be Your #1 Strategic Priority in 2026 description: A fundamental analysis of the shift from human-centric SEO to Agentic Discovery, situating VISEON at the centre of the semantic frontier. alternate: https://data-lake-seo.s3.eu-north-1.amazonaws.com/viseon-v2/https---viseon-io/schema-txt-json-files/newsarticles.json