We design and build ontology-driven knowledge graphs that give AI systems a verifiable model of the world they're operating in. It's how your products stop guessing and start knowing.
Language models are breathtaking pattern-completers. They are also, by architecture, unable to tell you when they're wrong. We rebuild that missing spine: a formal semantic model of your domain, machine-readable and human-auditable, that every prediction must reconcile against.
The result — measured across clinical, legal and financial pilots — is a step-change in factual reliability: fewer fabrications, faster retrieval, and a citation trail for every answer. The output isn't smarter prose. It's a trusted system of record.
Outputs must resolve to entities in the graph or flag uncertainty — no silent drift.
Every claim carries a citation chain back to the source document or data row.
Regulators, compliance officers, and clinicians can inspect every reasoning path.
Domain ontologies stack — enterprise, industry, jurisdiction — without duplication.
Unstructured documents, databases, APIs, taxonomies, glossaries — mapped into a staging layer.
Domain experts and our ontologists co-design the schema in OWL / RDF / SHACL.
SHACL shapes enforce integrity; axioms define what is and isn't a valid inference.
Graph-native RAG with embedding + symbolic joins — faster and more auditable than vector-only.
Every LLM output is checked against the graph. Mismatches flag, escalate, or abstain.
Clinical decision support, medication safety, dental and dermatology workflows.
Regulatory reporting, KYC/AML reasoning, investment research with auditability.
Contract intelligence, regulation mapping, policy-to-procedure chains.
Trial design, literature synthesis, adverse-event correlation.
Policy reasoning, citizen services, cross-departmental record reconciliation.
Supply-chain provenance, part-level traceability, defect-pattern knowledge graphs.