Domain-specific AI, built for production
AI Studio delivers a full machine learning (ML) development and deployment environment, integrated with Mendix for governed, enterprise-scale execution.
Most AI models never leave the lab.
Models built in isolation cannot be explained, audited, or defended, legal blocks deployment, compliance stalls sign-off, and AI stays in the lab. AI Studio closes that gap: a unified environment for developing, governing, and deploying domain-specific models with end-to-end traceability from the first training run to live production. Integrated with Mendix, it connects model output directly to the governed workflows and enterprise context that make AI consequential.
Built for production. Governed from the start.
Traceable and auditable by design
Black-box models cannot be explained to auditors, peers, or regulators. That is the final gate most AI initiatives never clear.
- Trace every prediction back to the data and features that produced it
- Deploy models that legal can approve and compliance can audit, without custom documentation built after the fact
Develop faster without sacrificing control
Speed without structure produces models that work in the lab and fail at the deployment gate.
- Build with AutoML, auto-feature engineering, auto-forecasting, and auto-clustering. Code-free or code-friendly, depending on the team
- Access 400,000-plus models via generative AI extension without leaving the development environment
- Deploy to production via one-click REST API, with monitoring and drift detection active from day one
One foundation for every team, region, and agent
Individual model performance does not aggregate to business-level impact when AI output is disconnected from the processes that act on it.
- Run models and agents across cloud, on-premises, and hybrid infrastructure without re-architecting for each environment
- Connect AI output directly to Mendix governed workflows across operations, finance, risk, and supply chain
- Orchestrate human and agent work within defined roles and escalation paths
Frequently Asked Questions
How do we get AI models out of pilot and into production?
The pilot-to-production gap is almost never a data or model quality problem. It is a governance and explainability problem. Models that cannot be audited, traced, or defended to legal and compliance never clear the final gate. AI Studio builds explainability and end-to-end traceability into the development lifecycle, so models arrive at the deployment decision already audit-ready.
Do we need a dedicated data science team to use AI Studio?
No. AI Studio supports both code-free and code-friendly development. AutoML, auto-feature engineering, and visual workflow tools let domain experts contribute directly alongside data scientists, reducing the bottleneck on centralized teams without removing the technical depth those teams bring.
How does AI Studio handle governance as models move into production?
Governance is not a post-deployment layer. It is built into the lifecycle. Every model carries a traceable record of its training data, feature logic, and decision outputs. Drift detection runs continuously, and every action is auditable within the Mendix platform.
Can AI Studio be deployed across our existing cloud and on-premises infrastructure?
Yes. AI Studio supports cloud, on-premises, and hybrid deployment without requiring re-architecture of the existing environment. One-click REST API deployment and an open API for integration mean models reach production on the infrastructure already in place.
How does AI Studio connect to the Mendix platform?
AI Studio integrates with Mendix, connecting model output to the enterprise knowledge graph, governed workflows, and the broader unified workforce. Models do not operate in isolation. They feed directly into the processes and applications where the enterprise acts on them.