Same business.
Different definitions.
Customers, products and assets appear differently across systems. Reconcile their identities and meaning before reusing them.
Enterprise ontology · Knowledge graphs · GraphRAG
Your business knows.
Does your AI?
Connect what your company knows.
Your knowledge lives across documents, systems and people. SDS connects its meaning and relationships so AI workflows can use business context and trace answers to evidence.
Start with one workflow. Define success. Evaluate before scaling.
THE ENTERPRISE CONTEXT LAYERIllustrative architecture
DocumentsEnterprise dataBusiness systems
YOUR BUSINESS, CONNECTEDEnterprise ontology
& knowledge graph
& knowledge graph
DefinitionsRelationshipsRulesProvenance
AI + BUSINESS CONTEXTAnswers you can investigate.
Source evidenceRelationship pathsEvaluation
Designed around your workflow, sources and access requirements.
Business questions firstStandards-aware engineeringVendor-neutral architectureKnowledge transfer built in
Start with the question that matters to you
What does your company know
that your AI cannot connect?
Choose a familiar challenge. See where connected business context could help.
Financial services
Which portfolios are affected by this issuer event?
Connect events to exposure →Manufacturing & supply chainWhich products depend on this supplier?
Connect parts to dependencies →Healthcare & life sciencesWhich research sources support this finding?
Connect concepts to evidence →Enterprise operationsWho owns this service—and which policy applies?
Connect knowledge to ownership →Illustrative questions to scope together. Suitability depends on your sources, domain and access requirements.
The challenge behind enterprise AI
Connected systems.
Disconnected meaning.
Moving data is only part of the work. Teams also need shared definitions, usable relationships and a path back to evidence.
Same business.
An answer.
But where is the evidence?
Connect retrieved information to sources and business relationships, then evaluate whether the answer is supported.
Rules on paper.
Context missing in practice.
Bring ownership, constraints, provenance and access requirements into the design of data and AI workflows.
What SDS delivers
A shared model
of how your
business works.
Ontology defines the concepts. Knowledge graphs connect the facts. Retrieval and APIs put that context to work.
Explore our services0102030405
Enterprise ontology consulting
Business concepts, relationships, definitions and validation rules.
Knowledge graph engineering
Entity resolution, connected data, provenance and semantic APIs.
AI-ready data foundations
Semantic integration, metadata, data quality and reusable context.
GraphRAG and AI agents
Graph and vector retrieval, source evidence and evaluation.
Semantic governance
Ownership, versioning, validation and change management.
A practical place to start
One problem.
A defined next step.
Start with a focused engagement. Agree scope, deliverables and success criteria before delivery begins.
Readiness
assessment
Decide where semantics can make a useful difference to your data or AI workflow.
- Business-question and source review
- Architecture and governance findings
- Prioritised delivery recommendation
Bring: one problem and a system overview.Discuss an assessment
Focused
pilot
Test one bounded workflow against a baseline and an agreed evaluation set.
- Representative data and semantic model
- Working graph or retrieval demonstration
- Results, limitations and scale plan
Bring: a workflow owner and representative sources.Explore the pilot
Production
implementation
Turn a validated approach into a maintained enterprise capability.
- Integrations and access-control design
- Deployment, monitoring and documentation
- Team handover and support planning
Bring: an agreed use case and deployment constraints.Plan implementation
Timing and pricing follow scoping. Ongoing OntologyOps support can cover model changes, mappings, validation and releases.
A common foundation. Domain-specific meaning.
Your industry.
Your business context.
The modelling approach is reusable. The vocabulary, rules and evaluation must reflect the domain.
01
Financial services
Instruments, issuers, events, exposure and evidence.
Explore financial solutions ↗02Healthcare & life sciences
Research knowledge, terminology and data lineage.
Explore potential applications ↗03Manufacturing & supply chain
Assets, suppliers, components and dependencies.
Explore potential applications ↗04Enterprise operations
Services, policies, ownership and internal knowledge.
Explore potential applications ↗SYNTHETIC DEMONSTRATION
“Which portfolios are affected
by this issuer event?”
by this issuer event?”
Source document→Issuer event→Portfolio
A visible path from a business question to connected evidence.
See the approach in action
Follow the evidence.
Understand the answer.
Explore the interactive financial example: ingestion, semantic mapping, knowledge graphs and evidence-grounded retrieval.
An illustrative, deterministic demonstration. It does not establish customer results or production performance.
Open the interactive demo ↗Review implementation patterns →How we work
Start with a question.
Finish with evidence.
A practical delivery path, with scope and acceptance criteria agreed at the start.
- 01
Define
Choose a workflow, owner, sources and measurable evaluation criteria.
- 02
Connect
Model business concepts, link entities and retain source provenance.
- 03
Evaluate
Compare against a baseline. Review quality, cost, latency and access behaviour.
- 04
Operationalise
Plan deployment, governance, maintenance and team ownership.
Make an informed decision
Before you invest,
ask better questions.
Practical guides to choosing the architecture, defining deliverables and evaluating a pilot.
When Does Your AI Need a Knowledge Graph?
A practical decision guide for enterprise teams evaluating graph retrieval, shared business definitions and evidence-linked AI.
Read the guide →How to Evaluate an Enterprise GraphRAG Pilot
Define a baseline, question set and acceptance criteria for an evidence-linked enterprise AI pilot.
Read the guide →What Should an Enterprise Ontology Pilot Deliver?
Understand ontology pilot deliverables, client inputs, boundaries and production handover considerations.
Read the guide →Before we talk
Good questions.
Clear answers.
Architecture decisions should follow the problem and the evidence.
Do we need to replace our data platform?
A semantic layer can work alongside existing warehouses, applications and document systems. Integration and deployment depend on the sources and constraints reviewed during discovery.
Does every AI workflow need a knowledge graph?
No. A simpler search or retrieval approach may be sufficient. An assessment should test whether explicit relationships, shared definitions or provenance justify the additional complexity.
How do you measure a pilot?
Agree a baseline and evaluation set before building. Relevant measures may include answer correctness, source support, retrieval quality, latency, operating cost and permission enforcement.
Can we start without sharing confidential data?
Yes. Begin with a workflow description and system overview. Any later data access, security arrangements and permitted use should be agreed before delivery.
Your next step
Bring us one
data or AI problem.
Tell us what your team needs to achieve. We’ll discuss the workflow and whether an assessment or focused pilot is a sensible starting point.
01 Share your challenge02 Discuss scope and fit03 Agree a practical next step
info@semanticdataservices.comA workflow description is enough to start. Please do not include confidential or sensitive data.
Let’s find the right starting point.
Start with a real question
What should your AI
know about your business?
Bring one workflow. We’ll discuss the evidence, scope and a practical first engagement.