Which graph databases and semantic tools do you support?

We select the store based on your workload and team skills—ranging from RDF/SPARQL stores with native SHACL to property graphs and PostgreSQL-backed hybrid architectures.

Can we start with an architecture and schema review?

Yes. We frequently run focused discovery sprints to audit existing ontologies, design SHACL/JSON Schema contracts, and benchmark GraphRAG against vector baselines.

How do you ensure our internal team can maintain the system?

Every engagement includes versioned schemas, automated validation test suites, operational runbooks, and hands-on pairing with your engineers.

Open-source knowledge graphs & semantic reasoning

Connecting ontology, validation, and structured retrieval

Enterprise knowledge systems fail when domain models, data validation, and retrieval pipelines are built in isolation. We combine lean ontology modelling, automated constraint checking (SHACL and JSON Schema), and hybrid GraphRAG architectures so your data stays consistent and your AI answers remain verifiable.

What we deliver

Ontology & knowledge-graph architecture

Define entity models, persistent identifiers, and ingestion write paths so your graph stays maintainable as sources and schemas evolve.

Validation contracts (SHACL & JSON Schema)

Enforce structural rules with JSON Schema at API boundaries and SHACL shapes across graph relationships before invalid states reach downstream systems.

GraphRAG & structured retrieval

Combine graph traversal and vector search when multi-hop reasoning, entity disambiguation, and strict citation governance are required.

Engineering standards in production

How we keep graph and semantic systems maintainable

Query-driven modelling

Model the entities, relations, and invariants your queries and compliance checks actually need—avoiding abstract ontology sprawl.

Automated constraint regression

Run SHACL and JSON Schema validation suites in CI/CD and ingestion pipelines before expanding the graph.

Explainable inference

Introduce OWL or rule-based reasoning only where derived facts are testable, auditable, and fast enough for production SLAs.

Choosing the right architectural scope

Document lookup vs multi-hop graph queries

When vector-only RAG is sufficient, keep the stack lean; add a knowledge graph when answers depend on typed relationships across systems.

End-to-end semantic data platform

When domain vocabulary, validation gates, and retrieval permissions must work together across multiple engineering teams.

Self-hosted AI & data infrastructure

When your semantic layer runs alongside on-premise LLM serving, PostgreSQL, and Kubernetes under strict data residency rules.

Related services & guides

FAQ

Which graph databases and semantic tools do you support?

We select the store based on your workload and team skills—ranging from RDF/SPARQL stores with native SHACL to property graphs and PostgreSQL-backed hybrid architectures.
Can we start with an architecture and schema review?

Yes. We frequently run focused discovery sprints to audit existing ontologies, design SHACL/JSON Schema contracts, and benchmark GraphRAG against vector baselines.
How do you ensure our internal team can maintain the system?

Every engagement includes versioned schemas, automated validation test suites, operational runbooks, and hands-on pairing with your engineers.

Discuss your knowledge-graph & semantic project

Scope ontology design, SHACL validation, GraphRAG, and reasoning with our engineering team.

Open-source knowledge graphs & semantic reasoning in production

Coherent architecture across ontology design, SHACL/JSON Schema validation, GraphRAG, and controlled inference—built for your team to own and operate.