Trusted by world-leading enterprises
Build a shared understanding of your data
metaphactory helps organizations define, govern, and connect what their data means. Its enterprise knowledge graph platform brings business expertise into a shared semantic layer, giving people and AI consistent definitions, relationships, and context.
Teams can build and manage knowledge models, connect distributed data, and use that foundation for discovery, applications, and explainable AI.
Through fleet-wide analysis our business users can take data-driven decisions when optimizing outage-specific spare part packages. An optimized package recommendation means happy customers, as it allows us to give them more comprehensive and precise recommendations as to which spare parts to order for which outage.”
Features
Semantic modeling and governance
Bring business and technical experts together to define shared concepts with review workflows, versioning, and traceability.
Data mapping and virtualization
Connect source data to your semantic model and query it where it lives, without creating duplicate datasets.
AI-assisted modeling and discovery
Build and refine models with AI assistance. Enable natural-language discovery grounded in enterprise knowledge through metis.
Enterprise Information Architecture
Connect business concepts, processes, roles, and systems to give people and AI a shared understanding of your organization.
Capture expertise. Govern meaning.
- Enable domain experts to build knowledge models alongside technical teams using a visual interface and AI assistance
- Manage definitions and relationships with editorial workflows, version history, and clear ownership
- Build on open standards to reuse and extend models across tools and teams
Turn shared knowledge into answers and action.
- Explore connected information through search, visualizations, and natural-language conversations
- Support explainable AI answers with defined business concepts and traceable sources
- Reuse your semantic model to build applications for discovery and decision-making
<2 years
To break even on knowledge graph investment (BKW Energie)
1,500 hours
Of manual processing saved in year one (Siemens Energy)
10+ years
Of enterprise knowledge graph deployments
Build the foundation for trusted enterprise AI
You need to bring business to [the ontology modeling] activity because IT folks lack the domain expertise, of course, and they cannot create this data model for you. Here we started using metaphactory’s visual modeling interface where users can create ontologies and taxonomies in a collaborative way.”
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