Enterprise context.
Enterprise scale.
Graph Studio connects every data source the enterprise runs, maps relationships across a unified ontology, and delivers the semantic context AI agents need to reason accurately, at any volume.
Add context to your data
Data isn’t what AI agents are missing. Context is: the ability to reason across domains, follow relationships, and return answers traceable to facts.
Without that layer, every agent query becomes an expensive guessing game: compounding reasoning iterations, mounting token costs, and answers no one can fully trust or act on. Graph Studio builds that layer on top of your existing data infrastructure, without replacing it.
Ingest structured and unstructured data from any enterprise source without schema constraints or data migration
Map relationships across the full enterprise ontology, not just within individual systems
Query billions of data points at enterprise scale with no performance degradation
Deliver semantically enriched, real-time context to every downstream AI model and agent
Enforce security and governance at the graph layer, before data reaches any application
Built for the problems that break
conventional data architecture
Any data, any scale, always ready
Structured and unstructured data rarely live in the same place or speak the same language. Graph Studio ingests both without forcing a schema, then relates them within a unified ontology.
- Ingest from data warehouses, lakes, documents, OT/IoT feeds, and enterprise applications in a single pipeline
- Handle billions of RDF triples in-memory, on disk, or virtualized
- Eliminate schema bottlenecks that slow conventional integration
Speed that holds at enterprise volume
Ad-hoc queries across cross-domain data sets are where conventional databases fail. The Graph Lakehouse MPP engine is built for that load.
- Run fully distributed, massively parallel queries across the full enterprise graph
- Scale horizontally with automated data sharding and query parallelization
- Deploy on Kubernetes in the cloud or on-premises, with no architectural trade-offs at scale
Context for defensible AI decisions
A model trained on siloed data produces answers no one can fully trust or trace. Graph Studio provides the ontology layer that gives every AI output a verifiable context.
- Enrich every data point with semantic relationships before it reaches a model or agent
- Refresh context in near real-time so models reason from the current state, not stale snapshots
- Trace every inference back to the source data and the relationships that produced it
Governance built into the graph
Access control and security enforced at the application layer can be bypassed. They cannot when enforced at the graph layer.
- Apply metadata management, data profiling, and access controls directly within the knowledge graph
- Manage ontology versions with full audit trails across every transformation and inference
- Connect to existing governance frameworks without rebuilding data pipelines
Frequently Asked Questions
Can Graph Studio connect to the systems we already have?
Yes, and it does not require data migration or consolidation before ingestion begins. Graph Studio connects to data warehouses, relational databases, data lakes, documents, OT/IoT feeds, and enterprise applications via direct connections. Your data stays where it lives. The graph connects it and makes it queryable across domains.
How does Graph Studio perform at the scale our enterprise runs at?
Lakehouse MPP engine handles hundreds of billions of RDF triples across a fully distributed architecture. Automated sharding and query parallelization maintain performance as data volume grows. It deploys on Kubernetes, cloud or on-premises, and scales horizontally by adding nodes.
How does this connect to AI development on the Mendix platform?
Graph Studio is the context infrastructure that AI Studio builds on. Models and agents deployed through AI Studio draw directly from the enterprise knowledge graph, reasoning from the full operational picture of the enterprise rather than a fragment of it. For AI agents specifically: without a knowledge graph, an agent resolves every entity relationship and naming inconsistency from scratch, compounding reasoning iterations and token costs. With the graph, the agent follows the ontology and reaches the answer in a fraction of the steps.
How does Graph Studio handle security and data governance?
With a layered model designed for enterprise-regulated environments. Role-based access control operates at the graphmart and layer level. Attribute and policy-based controls apply fine-grained rules based on data classification, region, or sensitivity. Every query, data access, and transformation is logged for compliance. Enterprise identity integration supports single sign-on and federated authentication. Security is built into the architecture, not added after deployment.