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Knowledge infrastructure

ENTERPRISE · IMPLEMENTATION

Enterprise knowledge infrastructure

We design and implement the layer that enables an organisation to identify, connect, govern and consistently reuse knowledge across people, applications and AI systems.

The architecture is defined around the problem, the sources and the systems already in place.

We do not start from a predetermined platform.

Implementations from €30,000 excl. VAT

Assess fit with Shymow How it works

The common layer

From scattered knowledge to an operational structure

The purpose of a knowledge infrastructure is not to concentrate all information in a single place.

Its role is to establish the rules needed to know:

  • what each entity is;
  • which relationships it has;
  • which source supports a claim;
  • who has authority over it;
  • how long it remains valid;
  • which evidence supports it;
  • who may use it;
  • and under what conditions it may be exposed to other systems.

These rules allow different applications to work with the same knowledge without independently reconstructing its meaning.

Existing sources and systems
Identity · Relationships · Provenance · Evidence

KNOWLEDGE INFRASTRUCTURE

EntitiesSources
Governance and currency
AccessValidation
Context · Permissions · Representations
People, applications and AI systems

Modular scope

What the implementation may include

Every project has a different scope.

Depending on the problem, we can design and implement:

Knowledge model
The conceptual structure of the domain: entities, attributes, relationships, states and dependencies.
Stable identities
Rules for recognising the same entity across documents, applications, units, languages or systems.
Sources and provenance
Relationships between knowledge, documents, source systems and sources considered authoritative.
Evidence
Mechanisms for preserving what supports a claim, definition or state.
Governance
Responsible parties, validators, change, review and withdrawal rules.
Currency and versioning
Conditions for distinguishing active, superseded, provisional or historical knowledge.
Access and exposure
Which information each person, application, agent or external system may use.
Machine-readable representations
Structures that allow other applications to consume knowledge without relying exclusively on unstructured text.
Integrations
Connections to existing systems when knowledge needs to be synchronised, queried or reused.
Validation
Criteria for confirming that the infrastructure preserves identity, context, provenance and rules of use.

Identity · Relationships · Sources · Evidence · Authority · Context

We work with what already exists

We do not assume that the organisation must replace its CMS, document management system, ERP, CRM, database or AI platform.

First we determine the role each system performs.

Then we design the layer needed to connect them.

Some projects will require new technology.

In others, the main change will be to establish:

  • models;
  • rules;
  • responsibilities;
  • relationships;
  • exchange structures;
  • or a common authority over the knowledge.

One foundation, multiple surfaces

An infrastructure can serve many consumers

The same knowledge can feed:

01

People

Teams that need to locate and validate information without relying on informal knowledge.

02

Applications

Systems that need to consume entities, relationships or states consistently.

03

Assistants and RAG

Systems that retrieve information and need to distinguish sources, currency and context.

04

AI agents

Systems that need to act on knowledge under defined rules and permissions.

05

External channels

Websites, search engines and generative systems that reconstruct public information about the organisation.

The infrastructure allows knowledge to be reused without forcing each consumer to redefine what it means.

Process

How we approach an implementation

  1. 1. Definition

    We usually begin with a previously completed Enterprise Diagnostic.

    We confirm the domain, sources, systems, responsible parties and success criteria.

  2. 2. Design

    We define:

    • knowledge model;
    • entities and relationships;
    • authority;
    • governance rules;
    • provenance;
    • evidence;
    • access;
    • integrations.

  3. 3. Implementation

    We build or configure the necessary layers and connect them to the systems involved.

  4. 4. Validation

    We verify that the knowledge retains:

    • identity;
    • relationships;
    • context;
    • provenance;
    • currency;
    • permissions;
    • traceability.

  5. 5. Handover and continuity

    We document the rules, responsibilities and criteria needed to maintain the infrastructure once implemented.

Boundaries

What we do not do

We do not migrate information by default

A migration only makes sense when it forms part of the solution.

We do not impose a platform

The architecture is defined from the problem.

We do not promise that an AI model will be infallible

We can improve the conditions under which it accesses and uses knowledge.

We do not control every behaviour of external models.

We do not build more complexity than necessary

If a rule, integration or source correction solves the problem, we do not design a larger infrastructure to justify the project.

Cases where it may make sense

  • internal assistants working with complex documentation;
  • RAG architectures;
  • AI agents;
  • groups with multiple brands or units;
  • complex catalogues or services;
  • operations across several countries or languages;
  • organisations with regulated information;
  • systems where the origin of an answer must be reconstructable;
  • multiple sources describing the same entities;
  • ecosystems where internal and public information require different rules.

The first step is usually an Enterprise Diagnostic

We do not price an infrastructure based solely on a commercial meeting.

First, we need to understand which knowledge is involved, where it lives and which conditions it must retain.

The Enterprise Diagnostic defines that scope before implementation begins.

View the Enterprise Diagnostic

Frequently asked questions

Does this replace our current systems?

Not necessarily. The infrastructure is designed around the problem and the systems that already exist.

Can it connect to agents or RAG?

It can provide governed knowledge to different consumers if the project requires it. That does not make the infrastructure a RAG system or an agent.

Do you publish all the knowledge?

No. Part of the work is deciding what may be exposed, where and under what conditions.

Why does pricing start at €30,000?

Because this is not a page or an isolated intervention. The work involves modelling, governance, implementation, validation and, where required, integration with multiple sources or systems.

Investment

From €30,000 excl. VAT

This is an indicative minimum investment for a defined implementation.

The final price depends on:

  • the domain;
  • the number and diversity of sources;
  • the systems involved;
  • integrations;
  • teams;
  • governance rules;
  • access requirements;
  • validation complexity.

We define the scope before starting.

Assess fit with Shymow

End of page

The objective is not to build another technology layer

It is to ensure that knowledge retains its meaning as it moves across systems.

If your organisation needs people, applications and AI systems to work from a common foundation of identity, authority, evidence and context, we can assess whether there is a case for implementation.

Assess fit with Shymow