The missing layer between raw data and AI
Data teams struggle to make sense of unstructured documents and fragmented databases. The gap between raw data and useful intelligence keeps growing.
Velum builds the semantic layer—domain ontologies—that lets you understand what your data means, enforce consistency through data contracts, and power intelligent applications.
Three ideas that change how you work with data
Ontologies
A formal definition of your domain—the entities, relationships, and constraints that give meaning to raw data.
Example
In financial services: Transaction, Account, Counterparty, and the rules governing how they relate.
Data Contracts
Enforceable rules derived from your ontology. They ensure data quality and consistency across systems.
Example
Every transaction must reference a valid account. Counterparty names must resolve to known entities.
Semantic Extraction
The process of turning unstructured text into structured knowledge that conforms to your ontology.
Example
A 50-page regulatory filing becomes a set of typed entities and relationships you can query.
Start from documents or databases
Your data lives in different places. Velum meets you where you are and builds toward a unified semantic layer.
From Documents
Unstructured → Structured
You have PDFs, reports, contracts, emails. Velum extracts entities and relationships, resolves references across documents, and produces structured data that conforms to your domain ontology.
What happens
1. Documents are parsed and chunked
2. Entities detected
3. Relationships extracted via LLM
4. References resolved across documents
5. Output conforms to your ontology
From Databases
Schema → Ontology → Contracts
You have PostgreSQL, data warehouses, APIs. Velum maps your existing schemas to a unified ontology and generates data contracts that enforce consistency across your data mesh.
What happens
1. Connect to your data sources
2. Schemas discovered and analyzed
3. Mapped to domain ontology
4. Data contracts generated
5. Governance enforced automatically
Both paths converge on a semantic layer you can query, validate, and build applications on.
What you can do with a semantic layer
Query across sources
Ask questions that span documents and databases. Get answers grounded in structured knowledge, not keyword matches.
Enforce data quality
Contracts catch schema drift, missing relationships, and constraint violations before they propagate downstream.
Power AI applications
Give your RAG pipelines and agents access to structured knowledge graphs instead of raw text chunks.
Understand your domain
Visualize how entities relate across your organization. Surface implicit knowledge buried in documents.