Organisations deploying AI at institutional scale cannot afford ambiguity. Matta provides the semantic substrate, from formal ontologies and knowledge graphs to validation infrastructure, that makes machine-verifiable shared meaning possible across every system you govern.
A model can only be as reliable as the meaning it is grounded in.
Ontologies · Knowledge graph · Validation · Provenance
must be governed
AI without grounding is unreliable.
Language models hallucinate because they have no authoritative semantic substrate to verify against. Matta provides that substrate: a machine-readable, institutionally governed body of verified fact.
Ambiguity compounds at scale.
When "Customer" means something different in your CRM, ERP, and data warehouse, every integration is a source of error. Matta eliminates definitional ambiguity by design.
Compliance requires provenance.
Every triple in the Matta knowledge graph records its source, timestamp, and transformation lineage. Audit trails are not an add-on; they are the architecture.
one dependency
Import dependencies flow upward only. Domain ontologies extend Core, they never modify it. Application ontologies reference concepts, they never redefine them. Circular dependencies are rejected at authoring time.
Foundational
Universal concepts: Entity, Object, Person, Organisation, Event, Process, Location, Role, Artifact.
Stability: Very highCore Enterprise
Shared business concepts: Customer, Product, Order, Invoice, Contract, Asset, Employee, Department.
Stability: HighDomain
Specialised modules: Finance, HR, Procurement, Logistics, Manufacturing, Legal, Security.
Stability: MediumApplication
System-specific mappings from CRM, ERP, and database schemas to ontology concepts.
Stability: Lowservices
Ontology Service
Full lifecycle management: authoring, versioning, validation, documentation generation, and refactoring operations.
Validation Service
SHACL shape evaluation against RDF data graphs. Synchronous and batch validation with severity-graded reports.
Query Service
SPARQL 1.1, GraphQL, and natural-language query interfaces with federated query support across remote endpoints.
Reasoning Service
OWL 2 DL reasoning for inferred facts, inconsistency detection, and incremental inference with dependency tracking.
Ingestion Service
ETL and ELT pipelines with mapping-driven RDF generation, SHACL pre-validation, and exactly-once processing semantics.
LLM Grounding Service
Retrieve ontology context for language-model prompts, and validate generated facts against the knowledge graph before surfacing them.
Governance Service
Stewardship workflows for ontology changes, with impact analysis across dependent ontologies, mappings, shapes, and instances.
Analytics Service
Graph centrality, community detection, shortest paths, embeddings, and data-lineage visualisation.
surfaced
Matta retrieves relevant ontology context and verified facts before language-model inference. Every claim the model produces is validated against the knowledge graph before it is surfaced. Contradictions are flagged, and hallucinations are caught.
Semantic infrastructure is not optional at institutional scale.
Define meaning once, govern it centrally, and let every system and model draw on the same verified ground truth.