Identities and entities
What each organisation, product, service, concept, person, policy or asset is, and how it retains a stable identity across systems.
ENTERPRISE · KNOWLEDGE INFRASTRUCTURE
Knowledge infrastructure for organisations and AI systems
When an organisation’s knowledge is spread across documents, teams, websites, applications and systems, having more information does not necessarily solve the problem.
You need to know which information is valid, what it means, where it comes from, how it relates to other information, who has authority over it and under what conditions it may be used.
Shymow designs knowledge infrastructure to turn scattered information into organised, governable knowledge that can be reused by people, applications, agents and artificial intelligence systems.
AI exposes a problem that already existed
An organisation may have thousands of documents, well-maintained databases and expert teams, yet still lack a shared, operational version of its knowledge.
The problem appears when:
- different departments maintain different definitions of the same entity;
- a current policy coexists with versions that should already have been withdrawn;
- the correct information exists, but it is unclear which source has authority;
- the context needed to interpret a piece of data lives in another system;
- an application reuses information without preserving its limits or conditions;
- no one can reconstruct why an answer is considered valid;
- an AI system finds information but cannot distinguish what is current, historical, provisional or incorrect.
Artificial intelligence does not create these inconsistencies.
It scales them.
The more systems consume and recombine knowledge, the more important it becomes to determine what may be used, how it should be interpreted and when it ceases to be valid.
A knowledge infrastructure establishes those rules
It is not simply a matter of gathering information in another repository.
We design a structure that preserves identity, meaning, provenance, evidence, context and responsibility as knowledge moves between people and systems.
Depending on the problem, an infrastructure can organise:
Relationships
How entities, concepts, processes, decisions, documents and sources connect without requiring each application to reconstruct those relationships independently.
Sources and provenance
Where a claim comes from, which source supports it and which system or document should be treated as the reference.
Evidence
What supports a claim, decision, definition or state, and how that relationship is preserved when knowledge is reused.
Authority and responsibility
Who may create, review, validate, replace or withdraw specific information.
Currency and versions
What is active, what has changed, what has been replaced and what should be retained only as historical information.
Context and limits
In which situations information is correct and in which it would cease to be so.
Access and exposure conditions
Which knowledge a team, application, agent or external system may use, and under what conditions.
The objective is not to give AI access to more information
It is to enable it to work with better knowledge.
A knowledge infrastructure separates one important question:
Can we retrieve this information?
from others that matter much more:
Is the information correct?
Is it still current?
Who is responsible for it?
In what context may it be used?
What evidence supports it?
May it be exposed outside the organisation?
Retrieval is one part of the system.
Trust requires an earlier layer.
Before a RAG, an agent or an assistant, there is a knowledge question
Connecting a model to documents does not automatically turn those documents into a reliable source of knowledge.
A system can accurately retrieve information that is already obsolete.
It can find two contradictory sources and not know which one takes precedence.
It can produce an answer that is correct in one context and incorrect in another.
It can retain the content while losing the conditions that limited its use.
That is why we work on the layer that defines what knowledge exists and which rules must accompany it before other systems consume it.
The infrastructure can support or coexist with:
- internal assistants;
- RAG architectures;
- agents and multi-agent systems;
- corporate applications;
- search engines;
- service systems;
- public interfaces;
- websites and digital environments;
- analysis tools;
- external generative systems.
It does not necessarily replace these technologies.
It gives them a more governable knowledge base.
When it begins to make sense
Not every organisation needs a formal knowledge infrastructure.
It generally becomes relevant when the complexity can no longer be resolved simply by publishing another document, adding a database or defining a new manual process.
Several sources describe the same reality
But there is no clear rule about which one takes precedence.
Knowledge crosses different teams
And each retains a different part of the context.
The organisation introduces AI
And discovers that the problem lies not only in the model, but also in what the model receives.
Traceability requirements exist
And an answer must be linkable to its source, responsible party or evidence.
There are multiple markets, languages, brands or units
And the same entity needs to retain its meaning across different expressions.
Information changes frequently
And it must be clear what is current, what has been replaced and when.
Different systems need different levels of access
Without maintaining independent, inconsistent versions of the knowledge.
Knowledge infrastructure
One common layer for different uses
The same knowledge may need to be expressed in different ways depending on who or what uses it.
A person may need an explanation.
An application, a defined structure.
An agent, context and permissions.
An AI system, enough relationships and evidence to avoid reconstructing meaning solely from fragments.
The infrastructure separates knowledge from each of its presentations.
This allows an organisation to reuse it in new channels, interfaces and systems without redefining its identity and rules from scratch every time the technology changes.
Identity · Relationships · Sources · Evidence · Authority · Context
It also affects how an organisation is interpreted from the outside
Visibility in generative systems is one specific application of this same problem.
When ChatGPT, Gemini or other systems reconstruct an organisation from public information, they may reduce its services, merge entities, lose distinctions or use sources that no longer represent the company correctly.
In small organisations, that problem can be addressed through a specific AI visibility and GEO intervention.
In Enterprise environments, external representation may be just one surface of a broader problem: how the organisation’s knowledge is structured, governed and exposed.
Two steps, one informed decision
From a diagnostic to an infrastructure worth building
We do not start by building
Before implementing an infrastructure, we need to determine whether it is needed.
The usual path begins with an Enterprise Diagnostic.
We analyse the problem and, depending on the scope, define:
- relevant knowledge domains;
- the sources and systems involved;
- principal entities and relationships;
- responsible parties and authority criteria;
- contradictions and overlaps;
- states of currency;
- access conditions;
- integration needs;
- reuse risks;
- validation criteria.
The purpose of the diagnostic is not to justify an implementation.
It is to decide with sufficient precision:
what problem exists, what is worth structuring, what should be preserved and what should not be built.
Enterprise Diagnostic
From€4,800excl. VAT
View the Enterprise DiagnosticWhen the diagnostic justifies an infrastructure
We design the architecture around the knowledge, systems and constraints that already exist in the organisation.
We do not start from a predetermined platform.
The scope may include:
- knowledge model;
- identities and entities;
- relationship architecture;
- sources and provenance;
- evidence;
- governance rules;
- responsible parties and validators;
- versioning and currency;
- access and exposure conditions;
- integrations;
- machine-readable representations;
- quality and validation criteria;
- deployment and continuity.
Enterprise knowledge infrastructure
From€30,000excl. VAT
The minimum investment applies to a defined implementation. The final price depends on the domain, sources, teams, systems, integrations and governance conditions involved.
View Enterprise implementationWe work with the system that exists, not an ideal architecture
Organisations already have technology, processes, documentation, constraints, debt and accumulated knowledge.
That is why a useful infrastructure cannot be designed as though starting from scratch.
First we determine:
what must be retained, what needs to be connected, what must be corrected and where a new layer is required.
The architecture adapts to the problem.
Not the problem to a tool.
Technology changes.
Knowledge needs continuity.
Models, interfaces, search engines, agents and applications will continue to change.
An organisation should not have to reconstruct what it knows every time a new interface appears.
We work so that identity, relationships, sources, evidence, context and authority can be preserved beyond the particular tool that uses them.
End of page
The first step is not to build an infrastructure
It is to find out whether you need one.
If the problem concerns how your organisation’s knowledge is organised, validated, connected or reused, we can assess the fit before defining an intervention.
Assess fit with ShymowKnowledge infrastructure · Barcelona · International
Frequently asked questions
What kinds of organisations need knowledge infrastructure?
Primarily organisations where relevant knowledge is distributed across multiple sources, teams or systems and that fragmentation is beginning to cause contradictions, loss of context, traceability problems or difficulties using AI reliably.
Size alone does not determine the need. Knowledge complexity does.
Does Shymow implement its own platform?
The architecture is defined around the problem and the existing ecosystem. An intervention may require technology, integrations or specific components, but we do not begin with an obligation to replace current tools with a predetermined platform.
Does all the information need to be centralised?
Not necessarily.
Physically centralising all knowledge may be unnecessary or even counterproductive.
What matters is establishing which sources exist, what authority they have, how they relate to one another and which rules allow them to be used correctly.
How does this relate to artificial intelligence?
AI systems increase the need for structured, contextualised and governed knowledge because they can retrieve, combine and produce information at scale.
The infrastructure establishes an earlier layer that helps determine what those systems may use and how it should be interpreted.
Is it the same as RAG?
No.
A RAG architecture may form part of a solution, but it primarily addresses retrieval and generation over specific sources.
A knowledge infrastructure also addresses identity, relationships, provenance, authority, currency, evidence, context and conditions of use.
Can it be used for AI agents?
Yes.
Knowledge can be prepared for people, applications, assistants, agents and other systems by defining appropriate access and exposure conditions for each use.
How does it relate to GEO and AI visibility?
GEO addresses how generative systems find, interpret and externally represent an organisation.
It may be an independent intervention when the problem is limited to that surface, or part of a broader infrastructure problem.
What does the Enterprise Diagnostic help you decide?
It helps determine whether there is a problem that justifies structural intervention, which domain should be addressed, which sources and responsible parties are involved and what is not worth building.
Do I have to commission an implementation afterwards?
No.
The diagnostic should be useful precisely for making that decision.
How much does an implementation cost?
Enterprise implementations start at €30,000 excl. VAT for a defined scope.
The final cost depends on the sources, systems, domains, teams, governance rules and integrations involved.