Platform · Altair Suite

Graph StudioEnterprise Context, Built and Governed for Global Enterprises

Global enterprises run on data spread across ERP, MES, PLM, CRM and data lake systems that were never built to talk to each other — or to an AI agent. MXTechies implements Graph Studio to connect that data into one governed knowledge graph, giving every application, analyst and AI agent a single, traceable context layer, without migrating or duplicating the systems you already run.

Scope a Graph Studio implementation → See how AI Studio builds on it →

A knowledge graph with Graph Studio at the centre. On one side, the enterprise systems it connects: ERP, MES, PLM, CRM and data lake. On the other, what queries it: AI agents, Mendix applications and BI tools.

The context layer
Graph Studio

Relationships modelled once, queried by everything, logged for audit.

Connected, not migratedYour systems stay where they are
W3C open standardsRDF, SPARQL, OWL, SHACL
Governed at the graphEvery query logged for audit

What it is

One graph over the systems you already run.

Quick answer

Graph Studio is an enterprise knowledge graph platform that connects structured and unstructured data from across the business into one queryable graph, built on open W3C standards (RDF, SPARQL, OWL). It uses an in-memory, massively parallel processing engine to query billions of connected data points at speed, and enforces governance and access control directly at the graph layer — which matters for global enterprises operating under regulations such as GDPR, CCPA and sector-specific frameworks, where access control at the application layer alone isn’t enough.

Rather than requiring every system to be migrated into one warehouse, Graph Studio connects to your existing ERP, MES, PLM, CRM and data platforms directly, adding a semantic layer on top that maps how everything relates without moving or duplicating the underlying data. Ontology development follows a Graphmart approach: the graph is built and refined incrementally, in composable layers, with each addition tested before the next is built on top of it — rather than attempting to model the entire enterprise ontology in one pass.

Scale, standards & governance

Why it holds up at enterprise scale.

A semantic layer is only worth building if it survives the volume, outlives the vendor, and can be audited. Three architectural decisions carry that weight.

01 — The engine

Built for enterprise scale: the in-memory MPP engine

Conventional databases slow down as cross-domain queries touch more joins and entity types. Graph Studio’s in-memory massively parallel processing engine automatically shards data and parallelizes both loading and querying, so performance holds as the graph scales into the billions of connected data points — deployed in-memory, virtualized, or on disk, on Kubernetes in the cloud or on-premises.

01 — The engine

Scales to
Billions of connected data points
Deployment
In-memory, virtualized, or on disk
Runs on
Kubernetes, cloud or on-premises
How we deliver →

02 — The standards

Open standards, no lock-in

Graph Studio is built on W3C open standards — RDF, SPARQL, OWL and SHACL — rather than a proprietary graph format. That matters for global enterprises weighing long-term platform risk: the ontology and query logic aren’t locked to one vendor’s runtime, and the same standards-based graph can be queried through Jupyter notebooks, REST APIs, BI tools, or Apache Arrow Flight.

02 — The standards

Built on
RDF · SPARQL · OWL · SHACL
Extends with
RDF-star for graph algorithms
Query from
Jupyter, REST, BI tools, Arrow Flight
See the key facts →

03 — The controls

Governance at the graph layer, not just the application layer

Access controls enforced only at the application layer can be bypassed by anything that queries the graph directly. Graph Studio applies role-based access control at the graph and layer level, with attribute- and policy-based rules for finer-grained control by data classification, region, or sensitivity — and logs every query, access and transformation for audit.

03 — The controls

Enforced at
The graph and layer level
Scoped by
Classification, region, sensitivity
Logged
Every query, access, transformation
Read the compliance FAQ →

Why that matters for a regulator

For global enterprises, that architecture lines up directly with what regulations such as GDPR, CCPA and sector-specific frameworks expect: a traceable record of what data was accessed, by what process, and under what authorization — enforced structurally, not left to individual application teams to implement consistently across every market.

What it delivers

Grounding AI agents in verified enterprise context.

An AI agent without a knowledge graph has to resolve every entity relationship and naming inconsistency from scratch on every query — compounding reasoning steps and token cost, and producing answers no one can fully trust. With Graph Studio’s ontology in place, an agent follows pre-modeled relationships directly, reaching an answer in a fraction of the steps, with every inference traceable back to the source data and relationships that produced it.

Connects

To the systems you already run. ERP, MES, PLM, CRM, data warehouses, lakes, documents and OT/IoT feeds connect directly, so the graph maps how everything relates without migration or duplication.

Governs

At the layer that matters. Role-, attribute- and policy-based access control sit on the graph itself, with every query, access and transformation logged for audit — not reimplemented per application.

Grounds

The AI on top of it. Graph RAG and agentic workflows query pre-modeled relationships instead of inferring them, so answers arrive in fewer steps and every inference traces back to source.

How we deliver

Our approach for global deployments.

MXTechies scopes, builds and governs Graph Studio implementations for enterprises operating across multiple markets, connecting to your existing systems rather than replacing them — with delivery teams spanning the United States, UAE and India, covering business hours across regions.

start with the question

Scope the use case

Identify the highest-value cross-domain question your teams can’t answer today — the one that needs two or three systems to agree before anyone can act on it.

with your domain experts

Design the ontology

Model entity classes, attributes and relationships with the people who know the domain, using the Graphmart approach to build incrementally rather than modelling the enterprise in one pass.

in place, not in transit

Connect the data

Link ERP, MES, PLM, CRM and data lake systems directly, without migration or duplication, so the graph reads governed source data rather than a copy of it.

before anyone queries it

Apply governance

Implement role-based and attribute-based access control at the graph layer, mapped to GDPR, CCPA and applicable sector regulation in each market you operate in.

traceable by construction

Ground AI on it

Connect Graph RAG and agentic AI workflows to the ontology so every answer is traceable to the source data and relationships that produced it.

domain by domain

Scale and extend

Expand ontology coverage domain by domain and market by market as value is demonstrated, rather than committing the whole enterprise up front.

Where it matters

Where this matters most globally.

Why MXTechies

Mendix partner. Ontology delivery team.

Mendix Certified Partner and Siemens Xcelerator partner, building governed data and AI foundations and the production Mendix applications that sit on top of them.

Team 0+ engineers

Ontology, data and Mendix engineers, working on the graph and the applications that query it.

Track record 0+ delivered projects

Across manufacturing, financial services, pharma, and other regulated industries.

Cadence 0 wks scope to first governed graph

Typical delivery cadence, from a scoped cross-domain question to a graph your teams can query.

Delivery teams span the United States, UAE and India, covering business hours across major global regions.

Start here

Start your Graph Studio implementation.

Tell us which cross-domain question your teams can’t answer today. Our team will scope a Graph Studio implementation on your data — governed for the markets you operate in, in weeks, not quarters.

What to bring

  • One question that needs two or three systems to agree before anyone can answer it
  • A rough list of the systems that hold the data — ERP, MES, PLM, CRM, the lake
  • Whoever owns that question. No deck, no pre-work

The conversation

  • We name the entities and relationships the question actually depends on
  • We check which systems can be connected in place, and which can’t yet
  • We map the governance the markets you operate in will expect

What you leave with

  • 01The first ontology slice, scoped and named
  • 02The gap in your data foundation, named
  • 03What a governed graph in production takes

“If a warehouse view would answer your question, we’ll tell you that — a graph earns its place on the questions a join can’t reach.”

Scope a Graph Studio implementation → See how AI Studio builds on this context →

Or read the concept behind it on our knowledge graph page, and our work across the Siemens ecosystem.

FAQ

Questions, answered.

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