The ontology engine
for enterprise AI
The missing layer between raw data and AI. Velum builds ontologies that power data contracts—whether your data lives in documents or databases.
BY RESEARCHERS FROM
Two paths to semantic infrastructure
From Documents
Unstructured data → Hypergraphs → Contracts
Extract entities from documents with zero-shot NER, discover relationships with LLMs, and build rich hypergraphs automatically.
Self-correcting pipeline ensures ontology conformance. Hypergraphs capture n-ary relationships that traditional graphs can't.
From Databases
Relational sources → Ontology mapping → Contracts
Map existing database schemas to domain ontologies. Align relational sources with your semantic model.
Generate data contracts from ontology definitions. Enforce consistency across federated data products in your data mesh.
Process visibility that scales.
Every time.
Accuracy measured on benchmark datasets with ground-truth process models.
"Velum discovered 340 process variants we didn't know existed. We reduced cycle time by 28% within the first quarter."
VP of Operations, Fortune 500 Manufacturer
Connect any enterprise data source
Velum integrates with your existing enterprise stack—extracting event logs, master data, and transactional records to construct comprehensive operational hypergraphs.
Data extraction capabilities
- Connect in minutes with pre-built extractors.
- Supports SAP, Oracle, Salesforce, ServiceNow, and 50+ enterprise systems.
- Real-time event streaming and batch processing modes.
- SOC 2 Type II certified with enterprise-grade security.
Why hypergraphs?
Traditional graphs model pairs. Hypergraphs model reality. Our hypergraph-native architecture captures n-ary relationships between entities, processes, and events—the way complex operational data actually works. Better for supply chains, financial flows, and any domain where relationships span multiple entities simultaneously.
Ingest from documents or databases. Unstructured sources are parsed and chunked; relational sources are schema-analyzed and mapped to ontologies.
Discovered patterns are mapped to domain ontologies, creating semantic anchors that enable cross-system reasoning and conformance checking.
Causal inference algorithms surface root causes, predict bottlenecks, and generate actionable recommendations for process optimization.
Events are organized into hyperedges representing n-ary relationships, capturing process variants, resource allocations, and temporal dependencies simultaneously.
Advanced clustering algorithms identify process variants, detecting deviations from expected behavior and surfacing hidden operational patterns.
Researchers building for enterprise
Alen Rubilar-Muñoz
Co-founder
Benjamin Muñoz-Cerro
Co-founder