Problems VISEON solves
Find your problem. See how VISEON solves it.
These are the problems organisations bring to VISEON, from being left out of AI answers to AI agents that can’t be trusted with a number. Each one links to the solution behind it. AI agents read the full detail, with buyers, alternatives and evidence, from the VISEON knowledge graph.
Theme 1 of 10 · 10 problems
Being found and recommended by AI
Buyers ask AI first. These are the reasons AI leaves an organisation out, or gets it wrong.
AI assistants don’t mention us
VISEON for Web publishes the organisation, its offers and its evidence as one connected knowledge graph that AI systems can read and cite.
AI gets our facts wrong
VISEON gives AI one authoritative, machine-readable account of the business, so answers are built from stated facts rather than guesses.
We are confused with another company of the same name
VISEON gives the organisation a stable identifier and disambiguating facts (location, founders, registrations, sameAs links) so AI can tell the two apart.
AI confuses our offer with similar ones nearby
VISEON describes each offer as its own entity, with what it includes and where it is, linked to the organisation that provides it.
A new name loses years of reputation
VISEON links the new organisation to its former names and history (alternateName, sameAs, founding facts) so expertise carries forward.
Bigger competitors get recommended instead of us
VISEON makes a specialist’s expertise explicit and connected, so AI can match it to the questions it is best placed to answer.
AI cites other sites about us, not our own
VISEON makes the organisation’s own site the authoritative entity home, with facts AI can verify and cite directly.
Our expertise is scattered across disconnected pages
VISEON connects every page’s entities into one graph with the organisation at its centre.
We can’t see how AI understands us
The free Semantic Entity Assessment reads the site’s structured data and reports its entities, errors and gaps, with results in minutes.
We can measure our AI visibility but not improve it
VISEON changes the facts AI reads: it builds, governs and publishes the knowledge graph rather than only reporting on it.
Theme 2 of 10 · 10 problems
Structured data that AI can trust
AI reads structured data. These are the ways it gets mixed, missing or out of date.
Our plugins publish conflicting schema
VISEON defines each entity once and references it everywhere, so every page tells the same story.
We can’t see the errors in our structured data
VISEON consolidates every page’s structured data into one place and shows the errors that exist.
Important things about us are never described
VISEON’s data catalogue lists every entity described and exposes the gaps to fill.
Our entities aren’t connected to each other
VISEON visualises the graph so missing or wrong relationships are easy to see and fix.
Our schema says something different from our pages
VISEON governs the facts once and publishes them consistently, so markup and pages agree.
Our schema was built for search snippets, not for AI
VISEON moves from rich-result markup to Semantic Entity Optimisation: a full model of the business that AI can reason over.
Our entities change identity every time the site changes
VISEON keeps stable identifiers and records old names as alternate names when things are renamed.
Our structured data decays as the site changes
VISEON syncs the knowledge graph from the live site and flags what has changed.
Our brands describe the group differently
VISEON for Web Multi-Brand keeps one canonical graph across brands, with each brand linked to the group.
Our website meaning and our business data never meet
VISEON is canonical by design: the same entity model serves the website, enterprise AI and data quality.
Theme 3 of 10 · 10 problems
Reaching the right audience
Being found by the right people, in the right place, in their own language.
Donors and partners can’t find the right charity
VISEON describes the charity, its programmes and its regions so AI can recommend it to the people it serves.
Local customers can’t find us
VISEON states location, service area and identity precisely so AI can match a local business to local questions.
Guests don’t have enough detail to book
VISEON describes each property, what it includes and where it is, so AI gives clear, specific answers.
AI can’t place an organisation that works across regions
VISEON keeps one organisation entity with its regions, languages and programmes connected to it.
Our agency can report AI visibility but not fix it
VISEON gives agencies an assessment and a platform to build and govern each client’s knowledge graph.
AI doesn’t recognise our people’s expertise
VISEON describes people, their roles and what they know about, linked to the organisation and its work.
Our international audience asks in other languages
VISEON Ask answers questions about the organisation in the visitor’s language, grounded in the same graph.
AI can’t see our proof
VISEON publishes reviews, events and evidence as entities linked to what they prove.
AI knows what we sell but not how we deliver it
VISEON models each service with its offer, audience, area served and first step.
AI doesn’t know where we are speaking or exhibiting
VISEON publishes events with speakers, topics and organisers linked to the organisation.
Theme 4 of 10 · 13 problems
Conversations with people and agents
Answering people and agents directly, from your own domain, with answers you can stand behind.
Our website can’t answer visitors’ questions
VISEON Ask answers questions in plain language, grounded in the organisation’s own knowledge graph.
Our chatbot makes things up
VISEON Ask answers from the governed graph and says what it does not know.
AI agents can’t query us directly
VISEON exposes the knowledge graph through an MCP server, so any MCP client can query it with tools.
Agents can’t discover that we have tools
VISEON publishes discovery manifests and registry listings so agents can find the organisation’s tools.
Our document search can’t follow relationships
VISEON uses GraphRAG over a governed graph, so answers follow real relationships.
Building our own GraphRAG would take too long
VISEON deploys in one to three days on the client’s existing site and data, with the platform already built.
We can’t tell where an AI answer came from
VISEON answers are grounded in named entities in the graph, so each answer can be traced to its source.
We want to see it working before we commit
viseon.io runs VISEON Ask over its own MCP server and graph, so buyers can try it on a live site.
Our answers are scattered across FAQs, PDFs and pages
VISEON links each answer to the entity it is about, so one governed answer serves every channel.
Agents can’t compare us with alternatives
VISEON publishes offers, features, audiences and the problems each product solves, so an agent can compare like for like.
Our website isn’t ready for AI agents
VISEON for Web gives the site an MCP server, WebMCP tools and discovery manifests over its governed knowledge graph.
ChatGPT, Claude and Copilot can’t connect to us
VISEON gives the organisation a standard MCP endpoint over its own knowledge graph that any MCP client can use.
Conversational web tools are only as good as our Schema.org
VISEON builds and governs the Schema.org graph those endpoints read, and serves its own Ask and MCP over it.
Theme 5 of 10 · 9 problems
Agentic commerce
Letting AI agents act: compare, book and buy.
AI agents can find us but can’t buy from us
VISEON connects the graph to MCP and WebMCP tools, so agents can act as well as read.
Our product data isn’t ready for AI agents
VISEON models products, offers, prices and availability as linked entities agents can use.
We don’t know which commerce protocol to support
VISEON builds the protocol-neutral entity layer underneath, so supporting a protocol is a connection, not a rebuild.
Agentic commerce is built for products, and we sell services
VISEON describes services and their offers as entities with a clear first step an agent can take.
Agents can’t read our prices and terms
VISEON publishes offers with price, currency, region and terms as structured data.
Agents see stale prices and availability
VISEON keeps the graph in step with source systems through change data capture.
Agents don’t know how to start working with us
VISEON publishes actions (assess, book, contact) on each product so the next step is machine-readable.
Our brand looks different in every feed and marketplace
VISEON holds the canonical brand entity that every channel can reference.
AI agents fumble through our forms and pages
VISEON for Web exposes structured WebMCP tools, so agents act through defined actions rather than guesswork.
Theme 6 of 10 · 17 problems
Business meaning for enterprise AI
AI has access to enterprise data, but not to what it means.
Our AI has access to data but not to its meaning
VISEON establishes the semantic path first: intent, meaning and metric, before any data is queried.
The same word means different things in different teams
VISEON gives each meaning its own governed concept, with every implementation linked to it.
AI picks the wrong metric
VISEON resolves the question to the governed concept and its approved implementation before calculation.
Our definitions are scattered across tools
VISEON is the canonical ontology: one place for meaning, referenced by every tool and agent.
Each team’s AI agent has its own version of the truth
VISEON gives every agent the same canonical context, so answers agree across teams.
We have no canonical ontology
VISEON for Enterprise builds and governs the canonical ontology with the client, typically in one to three months.
Our semantic layer still lets AI get it wrong
VISEON adds the business meaning, governance and trust around each concept, above any one data stack.
Our AI projects stall before production
VISEON makes data AI-ready: governed meaning from VISEON for Enterprise and tested data from VISEON Data Assurance.
We don’t know how ready we are for AI
The AI Readiness Assessment reviews one business function’s architecture, tools and data, at a fixed fee credited against the pilot.
Locations and business functions are described differently in every system
VISEON links each real-world thing to one canonical entity, with every system’s identifier attached.
What we tell customers and what our systems say don’t match
VISEON uses one entity model for the public website and for enterprise AI.
Nobody owns what our business terms mean
VISEON records who approved each concept and when, and keeps a human steward in the loop.
An enterprise ontology sounds like a multi-million platform programme
VISEON for Enterprise builds the canonical ontology and MCP hub on open standards over the existing estate, at a published price from £24,000 a year.
Our business meaning would be locked inside one vendor’s platform
VISEON keeps meaning in open standards (Schema.org, JSON-LD, MCP) and federates platform ontologies as sources rather than depending on one.
We’d have to move our data into a new platform to get an ontology
VISEON for Enterprise builds over the estate already running, without moving data.
We want a digital twin of our organisation that AI can use
VISEON builds the semantic digital twin: the canonical graph of the organisation, served to people through Ask and to agents through MCP.
Our agents can act, but they don’t know how our business works
VISEON for Enterprise encodes business meaning as a canonical knowledge graph that every agent reads through MCP before it acts.
Theme 7 of 10 · 14 problems
MCP servers and agent routing
More MCP servers, more agents, more vendors, and no shared meaning across them.
We have more MCP servers than we can govern
VISEON for Enterprise is the hub of all your MCP servers: it knows what each server holds and routes questions to the right one.
Agents call the wrong MCP server
VISEON routes by meaning: the concept in the question decides which server and implementation to use.
Our MCP gateway controls access but not meaning
VISEON governs both: its own MCP gateway, on an OpenZiti Zero Trust network, decides which identities may reach which MCP server, and VISEON for Enterprise’s canonical ontology says what each server’s data means.
Every MCP server uses its own vocabulary
VISEON maps every server’s terms to one canonical ontology.
We don’t want to be locked into one AI vendor
VISEON serves context over MCP, so Claude, ChatGPT and any MCP client use the same governed meaning.
Agents compute before they understand
VISEON puts context first and implementation second: the vCat MCP resolves meaning before the Qlik MCP computes.
Our website has no MCP server
VISEON for Web includes an MCP server over the site’s knowledge graph.
Our agents can’t find the tools we already have
VISEON catalogues each server and tool as an entity with what it does and what it covers.
Qlik is one of many MCP servers our agents use
VISEON for Enterprise and vCat route by meaning, so the right server answers and every answer uses the same definitions.
Our AI agents went live before our governance did
VISEON for Enterprise gives every agent the same governed context over MCP: which server holds which definition, who approved it and how current it is, so agents act on approved meaning.
Our agents come from many vendors, and each governs only its own
VISEON is vendor-neutral: one canonical ontology and MCP hub on open standards, served to every vendor’s agents alike.
Our MCP servers are reachable from the network
VISEON puts MCP servers behind its own MCP gateway on an OpenZiti Zero Trust network, as it does with its own MCP hub: no inbound ports, no public endpoints, and every connection authorised by identity before a network path exists.
Reaching our MCP tools remotely means a VPN or open ports
VISEON’s MCP gateway runs on an OpenZiti Zero Trust overlay, so authorised agents and people reach MCP tools from anywhere with outbound-only connections, without a VPN or open ports.
Our MCP gateway would tie us to one cloud provider
VISEON’s MCP gateway runs on OpenZiti, open source Zero Trust networking that works across clouds and on-premises, in front of the VISEON for Enterprise hub.
Theme 8 of 10 · 20 problems
AI over Qlik
Qlik Answers, the Qlik MCP Server and outside AI tools all read the same apps: their meaning, their data and how they are built.
Qlik Answers falls back on general knowledge
vCat drafts the missing descriptions, a steward approves them, and they are written back to the apps Qlik Answers reads.
Our Qlik master items have no descriptions
vCat scans the estate and drafts descriptions for every master measure, dimension and field.
Writing Qlik descriptions by hand doesn’t scale
vCat automates the drafting and keeps descriptions current, with humans approving each one.
We can’t let AI publish definitions unchecked
In vCat nothing is published as governed until a data steward signs it off: AI recommends, humans certify.
The same measure exists in many versions across apps
vCat links every implementation to one governed concept, so one meaning can point to many measures.
Agents call the Qlik MCP Server without knowing what we mean
The vCat MCP resolves intent, meaning and metric first, then hands the right implementation to the Qlik MCP.
AI answers don’t say how far to trust them
vCat returns the governed definition, its trust level and its freshness with every answer.
We don’t know what’s in our Qlik estate
vCat keeps the canonical master map of the tenant: live reporting, master items and operating rules.
Our glossary isn’t reflected in our master items
vCat writes the approved meaning back into master item descriptions, where Qlik Answers looks first.
We don’t know if our Qlik data is right
VISEON Data Assurance for Qlik scores every row of the app data and QVD files behind the answer.
Qlik Answers and our other AI tools give different answers
vCat serves one governed meaning to every tool over MCP, so they answer alike.
Is our Qlik estate ready for AI?
AIRA, Differentia Consulting’s AI Readiness Assessment for Qlik, audits how every app is built and reports what to fix first, so AI starts from apps you can stand behind; vCat then governs their meaning and VISEON Data Assurance for Qlik tests their data.
AI will repeat our Qlik apps’ mistakes at scale
Assess the apps before AI reads them: AIRA finds the flaws, vCat governs what each measure means, and VISEON Data Assurance for Qlik tests the data underneath.
Our master measures silently break Qlik Answers filters
AIRA flags every master measure that embeds a set identifier and gives the rewrite that keeps Qlik Answers filters working.
Our Qlik data models have hidden structural problems
AIRA reviews each data model for structural problems and recommends the model changes, ranked by impact.
Our Qlik apps rely on deprecated objects and extensions
AIRA scans every app for deprecated objects and extensions, so replacements are planned rather than discovered.
Our Qlik apps carry fields and data nobody uses
AIRA identifies unused fields and data in each app and how much can safely be removed.
We inherited Qlik apps nobody fully understands
AIRA gives a consistent, app-by-app picture of how each one is built and what needs fixing, and vCat documents what its measures mean.
Moving to Qlik Cloud would carry our app problems with us
Assess first: AIRA shows what to fix, remove or rebuild before migration, so the estate arrives in Qlik Cloud ready for AI.
Theme 9 of 10 · 18 problems
Data AI can trust
A governed definition is no help if the number behind it is wrong.
The dashboard looks fine, but the number isn’t
VISEON Data Assurance tests the data behind every figure before anyone, or any agent, trusts it.
Sample checks miss the bad records
VISEON Data Assurance checks every row, not a sample.
Our data can’t leave our environment to be checked
Checks run as SQL inside the client’s own warehouse; only results come back, and no data is stored by VISEON.
Customers and auditors find our data errors before we do
VISEON Data Assurance keeps watching and alerts the owner when quality breaks, and when it is fixed.
We have no single, honest measure of data quality
VISEON Data Assurance scores data against nine quality dimensions and gives one honest score with a quality gate.
Sensitive data sits in clear text
VISEON Data Assurance finds sensitive values, masks them in results and never stores them.
Our data lives in too many places to check
VISEON Data Assurance plugs into 17 sources where the data already lives.
Writing data quality rules takes too long
VISEON Data Assurance suggests the rules you missed by reading the shape of the data, never the values.
Our data is behind a firewall
VISEON Data Assurance runs there, with outbound-only connections: nothing comes in.
We can’t evidence data quality for regulators
VISEON Data Assurance keeps the evidence behind every score, aligned to privacy and data standards.
Our AI agents trust data nobody has checked
VISEON Data Assurance is the touchstone: every figure tested before anyone, or any agent, trusts it.
We can’t simply ask what’s failing
VISEON Data Assurance answers in plain English through any MCP client, such as Claude or ChatGPT.
Data failures don’t reach the people who can fix them
VISEON Data Assurance opens an incident and alerts the owner in Slack, Teams or email.
We know quality dropped but not which record broke it
VISEON Data Assurance drills down to each failed record and the rule it broke.
Bad data flows straight into reports and AI
VISEON Data Assurance applies a quality gate, so failing data is flagged before it is used.
We know what a number means but not whether it’s right
vCat tells your AI which number you mean; VISEON Data Assurance tells you whether that number can be trusted.
Our risk reports must be provably accurate
VISEON Data Assurance scores every row behind risk figures and keeps the evidence for supervisors.
Inaccurate answers are stopping us trusting agents
VISEON tackles both causes: governed meaning, so agents use the right definition, and VISEON Data Assurance, so the data behind it is tested.
Theme 10 of 10 · 13 problems
Buying and delivery
What buyers need to know before they commit.
Our AI tools each solve one slice
VISEON is one semantic intelligence platform, canonical by design, from the public website to enterprise data.
We want a baseline before we invest
The Semantic Entity Assessment is free for one domain, with results in minutes; enterprises start with a fixed-fee AI Readiness Assessment.
We can’t wait months to see results
VISEON deploys in one to three days, included in paid plans; the ontology build is set by how fast the client agrees it.
We can’t get a price without a sales call
VISEON publishes from-prices for every solution, and the assessment for VISEON for Web is free.
We must buy services delivered to a recognised standard
VISEON delivery is aligned to ITIL 4 service management practice.
We don’t want another vendor holding our data
VISEON Data Assurance stores no client data: checks run in place and only results return.
We need a partner with enterprise delivery behind them
VISEON is part of Differentia Consulting, a Qlik partner, which delivers and supports it.
Our AI strategy has no data foundations
VISEON supplies the foundations: canonical meaning first, tested data underneath.
We can’t show what AI visibility is worth
VISEON starts from a measured assessment, so improvement can be shown against a baseline.
We don’t have knowledge graph specialists
VISEON delivers the platform with the service: deployment, build and run included in the plans.
AI search changes faster than we can keep up
VISEON builds on open standards (Schema.org, JSON-LD, MCP), so the same graph serves each new assistant and protocol.
Competitors may get there first
VISEON gets the organisation’s facts established as the authoritative source now.
Is MCP a safe standard to build on?
VISEON builds on MCP, now an open project of the Linux Foundation’s Agentic AI Foundation, and on Schema.org and JSON-LD.
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