Understand where ontology can help your organisation—and what evidence to demand before you invest. Explore practical guidance, reference architectures and the delivery methods behind our services.
Read this first if you are deciding whether to fund an ontology or knowledge graph initiative.
How shared definitions, connected relationships and source evidence can support decisions about suppliers, financial exposure and internal operations.
Ontology delivery combines domain understanding, formal modelling, engineering and evaluation. The engagement scope establishes responsibilities and deliverables.
Ontology specialists work with your domain reviewers to clarify terminology, decisions, identities and relationships.
What you inspectGraph and data engineering turns agreed concepts into mappings, linked entities and queryable relationships.
What you inspectAI and evaluation work connects retrieval to the business question and compares the proposed approach with a baseline.
What you inspectGovernance and enablement define who changes the model, how it is released and what your team needs to maintain it.
What you inspectService engagements include SWARMCARE, PARA GROUP LLC and Bessemer Trust. Discuss the experience relevant to your requirements with SDS.
Organisation names identify service experience and do not imply endorsement. Project scope and any shareable references can be discussed during discovery.
Discuss relevant experience →Explore the public work behind SDS’s service approach. Demonstrations and reference patterns are distinct from customer results.
Inspect a synthetic example of how business concepts connect source information to an AI question.
Explore the demonstration →Explore reference patterns for entity identity, research retrieval and cyber-to-business dependencies.
Review implementation patterns →Inspect how open standards can inform modelling, validation and provenance. Referencing a standard does not imply certification or endorsement.
Explore primary references →Each research track connects a difficult enterprise question to an implementable ontology, graph or validation pattern.
Point-in-time identity, event time, observation time, lineage and versioned relationships for stocks, ETFs and portfolios.
Hybrid graph and vector retrieval in which governed concepts guide extraction, traversal, ranking and explanation.
Connecting threat intelligence and technology dependencies to instruments, data products, decisions and regulatory impact.
Converting data-quality, policy, access and model-risk requirements into portable graph constraints and rules.
A governed context architecture for models, GraphRAG and enterprise agents.
Read paper →Preserving point-in-time truth and evidence for explainable signals.
Read research →What emerging SHACL 1.2 work could mean for governed AI platforms.
Read advancement →Our radar separates production-ready foundations from promising capabilities that require controlled evaluation.
We turn the question into competency questions, a semantic model, a graph experiment and measurable evaluation criteria.
Practical guides to choosing the architecture, defining deliverables and evaluating a pilot.
A practical decision guide for enterprise teams evaluating graph retrieval, shared business definitions and evidence-linked AI.
Read the guide →Define a baseline, question set and acceptance criteria for an evidence-linked enterprise AI pilot.
Read the guide →Understand ontology pilot deliverables, client inputs, boundaries and production handover considerations.
Read the guide →