Accelerate every stage
of product development
Give engineers and AI agents shared lifecycle context across PLM,
MES, and ERP, without disrupting your systems of record
Take a modern approach to product lifecycle management
Your engineering systems are not the problem. PLM governs product definitions. MES runs production. ERP owns planning and cost. Each does its job. None of them connect an engineering decision to what it triggers downstream.
That gap is why AI in engineering stalls. A model trained on your change history learns that a part was specified in titanium 60% of the time. It never learns why, or whether the conditions behind that call still hold. Reasoning takes the decision logic, not the decision record.
The longer that gap stays open, the more these costs compound.
AI without context
Demands models that converge on real process paramenters. LLMs cannot perform this function.
(Gartner)
Pilots that never reach production
More than 80% of AI projects fail, twice the rate of IT projects that do not involve AI.
(RAND Corporation)
Expertise you cannot replace
1.9 million manufacturing jobs could go unfilled by 2033, taking decades of engineering judgment with them. (Deloitte and The Manufacturing Institute)
Adapt and extend your PLM instead of replacing it
Core systems were built for stability and control. That is exactly what makes them hard to change, and why every new workflow turns into an IT project.
Mendix layers on top. Connect engineering data where it already lives, build the applications and agents that act on it, and leave your systems of record intact.
Shared lifecycle context
Connect PLM, MES, ERP, and OT data into one ontology. No migration. No data lake project.
Governed AI
Every model output, agent action, and human approval is traceable, auditable, and policy enforced.
Coordinated execution
Turn one engineering decision into action across engineering, procurement, and the shop floor.
“Combining Teamcenter with Mendix is really how we achieved what we wanted from both an engineering perspective and shop floor perspective.”
Steven Abbey
PLM Business Analyst, Team Penske
Read more about how Team Penske centralizes systems across the product lifecycle
How Mendix support engineering and product development
Build software that connects product data, engineering judgment, and AI into one governed workflow. Mendix gives your teams the context to decide and the execution layer to act on it.
Customer Stories
Resources
Engineering Agility without Compromise
Discover how combining Teamcenter's robust PLM foundation with Mendix's agentic low-code layer will allow you to achieve faster cycle times, improve collaboration, and higher…
Teamcenter and Mendix
Make the most of your Teamcenter Product Lifecycle Management (PLM) data with Mendix.
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Industrial agility is your organization's ability to respond to change without operational disruption. It means adopting AI, automating decisions, and delivering mobile experiences while…
Frequently Asked Questions
What is product lifecycle management?
Product lifecycle management (PLM) is the practice of managing a product’s data and processes from concept through design, manufacturing, service, and retirement. PLM systems govern product definitions: requirements, design history, bills of materials, and change records.
Can Mendix integrate with our existing PLM, ERP, MES systems ?
Yes. Mendix adapts and extends your systems of record rather than replacing them.
Mendix provides a unified Teamcenter connector, a native BOM component, and pre-built connectors and APIs for ERP, MES, and other engineering systems. You build applications on top of data where it already lives, with no migration and no rip-and-replace program.
How does Mendix govern AI in regulated engineering environments?
Governance is built into the platform, not added after deployment.
Every model inference, agent decision, human approval, and workflow execution is logged, traceable, and policy enforced. Role-based access, single sign-on, and audit trails are standard, and Mendix deploys to public cloud, private cloud, and air-gapped environments. For regulated manufacturers, that means AI outputs that hold up under audit.
Why isn’t a data lake enough for engineering AI?
A data lake aggregates data. It does not connect it.
Centralizing product, production, and supplier data in one place leaves it fragmented and unlabeled. The relationships between a part, a process, and a cost are still missing, and AI agents need that semantic context to reason. An ontology-based knowledge graph supplies it by mapping how your data actually relates, so agents work from your enterprise’s real structure instead of generic inference.