Business questions first
The model begins with the decisions, obligations and questions people and AI must answer.
Senior-led semantic engineering
Highly qualified ontology engineers for enterprise-scale change.
Semantic Data Services is a senior-led group of domain modelers, knowledge graph engineers, data architects and AI practitioners. We understand ontology as both a technical system and an organizational capability—and we transfer the methods so your team can own it.
Our point of view
AI transformation is a meaning transformation.
Organizations do not struggle because they lack data. They struggle because data carries different meanings across businesses, systems and moments in time.
Ontology creates a durable agreement about those meanings. Knowledge graphs make that agreement operational. AI uses it to interpret, retrieve, reason and explain.
We help leaders create all three—and establish the governance and skills required to sustain them.
Engineering principles
Rigor in the model.
Pragmatism in delivery.
Modular by design
Domain ontologies can evolve independently while aligning to a coherent enterprise backbone.
Standards with judgment
We reuse FIBO, SKOS, PROV-O, STIX and other standards where they add clarity—not complexity.
Executable semantics
Mappings, rules, constraints and APIs make ontology active inside data and AI workflows.
Evidence over assertion
Lineage, provenance, validation and explainability are part of the model from day one.
Ownership over dependency
We transfer patterns, governance and skills so client teams can sustain the capability.
One integrated team
The disciplines required to make semantics operational.
Enterprise and domain models, formal semantics, modularization and standards alignment.
Instruments, parties, agreements, markets, portfolios, risk, operations and regulation.
Extraction, entity resolution, RDF/LPG implementation, reasoning, APIs and performance.
Streaming, batch, metadata, data products, mappings, contracts and platform integration.
Retrieval, agents, tools, grounding, policy context, evaluation and explainability.
Stewardship, operating models, quality, change control, education and organizational rollout.
The organizational shift
We do not hand over an ontology file and leave.
We help establish a living semantic practice that connects business owners, data producers, platform teams, governance and AI delivery.
BeforeTerms live in documents
→ShiftMeaning becomes governed code
→AfterEvery data and AI product reuses it
Work with specialists
Bring us the domain your organization must understand better.
We will bring the modeling rigor, engineering depth and delivery leadership to make it usable.