KNOWLEDGE GRAPHS & ONTOLOGIES
A connected view of your business.
Turn scattered information into a structured model of the people, processes, systems and relationships that matter.
We design ontologies and build knowledge graphs to help you explore business dependencies, connect data and make knowledge usable by people and AI.
Standalone or integrated knowledge engineering · From a defined domain to an evolving business representation.
ILLUSTRATIVE BUSINESS KNOWLEDGE GRAPH
Client
signs
Contract
covers
Team
delivers
Service
System
uses
follows
Procedure
Connect business entities through explicit relationships. The model and its data are defined around the business questions you need to answer.
QUESTIONS WORTH CONNECTING
Understand more than individual records.
A knowledge graph brings relationships into view, so you can explore information across all the sources involved.
TRACE DEPENDENCIES
Which customers rely on this system?
Connect applications, services and customers to identify potential exposure when something changes.
FIND THE RIGHT KNOWLEDGE
Who owns the process, and which documents govern it?
Link activities, responsibilities and approved documentation so the context is easier to find.
CONNECT BUSINESS INFORMATION
How do our projects, contracts and suppliers relate?
Bring agreed records and definitions together across your selected sources.
Illustrative questions. Coverage and answer quality depend on the information available and the relationships modeled.
WHAT WE CAN DELIVER
From shared definitions to connected knowledge.
01 / MODEL THE DOMAIN
Ontology design
Agree on the concepts, definitions and relationships that describe your business domain.
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Business questions and domain boundaries
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Concepts, relationships and agreed constraints
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Documented ontology, reviewed with domain experts
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Examples and guidance for future extension
A standalone deliverable, whether you implement a graph now or later.
02 / BUILD THE GRAPH
Knowledge graph development
Populate and validate a graph using selected business sources, then make it available for the agreed applications.
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Source mappings and entity identification rules
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Data ingestion and quality checks
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Queries, graph exploration or application interfaces
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Source traceability and updates
Build on a new ontology or assess and extend an existing model.
Start with a useful, bounded scope.
For example: service delivery connecting clients, contracts, teams and systems. Your proposal defines the sources, questions, deliverables and acceptance criteria.
BUSINESS DIGITAL TWINS
Build a representation that evolves with your operations.
A business knowledge graph can provide the foundation for a digital twin of a defined part of your organization.
Connect the model to maintained business data to reflect changing relationships and operational states. Define what is represented, how updates arrive and how current the information needs to be.
Model the business domain
Entities, relationships and agreed meaning
Connect and maintain source data
Defined update frequency and quality checks
Example: a connected view of service delivery
Link customer contracts, active services, responsible teams and supporting applications. As those records change, update the representation to help trace dependencies and investigate potential impacts.
Begin with one operational area and expand where the value is clear.
Explore the current business state
Dependencies, context and source traceability
Scope defines the capability.
A static knowledge map is a starting model. A digital twin needs a maintained connection to the operations it represents. Simulation and prediction require additional data and behavioral models, scoped separately.
KNOWLEDGE FOR AI
Give AI access to business relationships
Use structured knowledge alongside documents to support questions that cross systems, entities and relationships.
A graph can help an assistant retrieve related facts and their sources, or give an agent context for a defined task. The retrieval strategy, permissions and answer quality are validated against agreed questions.
GraphRAG & neurosymbolic approaches
GraphRAG uses graph-based retrieval to provide context to a language model. Neurosymbolic approaches combine neural models with explicit knowledge or reasoning.
We assess where these approaches add value. Ontologies and knowledge graphs can also be commissioned independently of an AI application.
A DEDICATED ENGAGEMENT
A project shaped around your knowledge.
Start with a free 40-minute consultation. Detailed discovery, modeling and implementation are scoped as paid work.
01
Define
Agree on the domain, users, sources and business questions the model should support.
02
Model
Design concepts and relationships, then validate the ontology with your domain experts.
03
Build & validate
Where commissioned, populate the graph and test it against agreed data checks and questions.
04
Hand over & evolve
Deliver documentation and guidance. Agree on update ownership and optional ongoing support.
The scope can end with a validated ontology. Graph implementation, integrations and ongoing maintenance are agreed separately.
BEFORE WE START