Enterprise Knowledge Graph
The context layer your AI stands on
Most enterprises don’t have a data problem. They have a relationship problem. Data sits in dozens of systems (warehouses, lakes, enterprise applications) and no single layer connects what each point means in relation to every other.
Graph Studio builds that layer: an enterprise knowledge graph that ingests data from
every source, maps relationships across the full ontology, and makes that context available to every
model, agent, and application running on the platform.
Ingest structured and unstructured data from any enterprise source without schema constraints
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, near real-time context to every downstream AI model and agent
Enforce security and governance at the graph layer, before data reaches any application
The shortcut your AI has been missing
Shadow AI isn’t a future risk. 69% of cybersecurity leaders already report employees using prohibited AI tools. A governance model that works per team or per project doesn’t scale. Mendix enforces policies centrally — across every app, agent, and workflow — so the rules that apply in one business unit apply everywhere.
Enforce consistent security, compliance, and risk policies across the full portfolio
Encode business rules, configuration logic, and domain semantics that LLMs do not carry in their training data
Reduce agent reasoning cost as AI pricing shifts to consumption-based models: fewer tokens, faster answers, lower cost per query
A knowledge graph that builds itself and gets smarter as it grows
Enterprise knowledge graph projects fail not because of their technology, but because of the approach: months of upfront ontology design before a single record loads. Graph Studio inverts this.
Auto-generate ontologies from existing data sources, with no months of upfront modeling by scarce experts
Build incrementally with composable graphmarts: add domains without rebuilding what already works
Deploy AI agents via MCP to accelerate construction: what previously took weeks now takes days
Enterprise memory that doesn’t walk out the door
The most valuable enterprise knowledge isn’t in any system: it’s held by specific people who understand what the data means in context. When they leave, it leaves with them.
Encode domain expertise as explicit ontology relationships, not locked in documents or individuals
Make cross-domain knowledge available to every agent and application without requiring expert involvement at query time
Preserve enterprise context as teams change: the graph retains what your people know, even when they move on
Built to scale across the full enterprise estate
Most knowledge graph deployments hit a ceiling at production scale. Graph Studio is architected specifically for where that ceiling appears.
Add new domains and data sources without disrupting existing graphmarts or rebuilding the ontology
Accelerate every subsequent AI initiative by building on enterprise context already in place
Scale from a single use case to enterprise-wide deployment on the same architecture, with no re-platforming required
Frequently Asked Questions
How is an enterprise knowledge graph different from a data warehouse or data lake?
A data warehouse stores structured records. A data lake stores raw data at volume. Neither maps the relationships between data points across systems, which is what AI models and agents need to reason accurately. When an agent asks a cross-domain question spanning suppliers, components, orders, and customers, it needs a semantic layer that knows how your data connects. Graph Studio builds that layer on top of your existing data infrastructure, without replacing it.
How long does it take to build a knowledge graph?
Less time than traditional approaches require. Graph Studio’s data-driven ontology extraction auto-generates ontologies from your existing data sources. You start with automated extraction, then refine iteratively. The graph evolves as your business does, without rebuilding from scratch. Most deployments begin with two or three data domains for a single high-value use case and expand from there.
Do we need to move our data into a new system?
No. Your data stays in the source systems where it lives today. Graph Studio creates a semantic overlay — connecting and contextualizing data from source systems according to the ontology — without copying or migrating it. Your source systems remain the authoritative source of truth. All configuration, ontologies, security rules, and transformation logic persist independently of the graph state.