3DEXPERIENCE + private AI
AI for 3DEXPERIENCE and ENOVIA That Works With Your Business Objects, Not Around Them
Short answer
AI for Dassault 3DEXPERIENCE and ENOVIA places a private language model beside 3DSpace, reading parts, engineering changes and documents through supported web services under the asking user's identity. Engineers and change coordinators ask in plain English and receive answers citing the underlying business objects, while IP and controlled data stay in your environment.
- ERP
- Dassault Systemes 3DEXPERIENCE, ENOVIA
- Industries
- Aerospace, Defense, Automotive, Industrial Equipment, High-Tech
- Written for
- PLM Architect
ENOVIA grew out of the MatrixOne data model, where everything is a business object identified by type, name and revision, connected by relationships and governed by policies with states. That model is flexible enough that two ENOVIA installations can look very different, with custom types, attributes and Java Program Objects layered over the standard engineering and change applications.
On the 3DEXPERIENCE platform the same data is surfaced through 3DSpace, 3DDashboard and role-based apps. Users move between these apps all day, but cross-cutting questions still fall into the gaps: which change actions are open against this product, which of its parts are in a pending state, and what did a similar previous change decide.
Answering them means knowing the type and relationship names, writing the right query, and respecting the project and collaborative-space security that governs visibility. That knowledge sits with a few architects, so engineers wait, or they export and analyze data in spreadsheets, which defeats the access model.
Below is how a private AI layer fits next to 3DEXPERIENCE: what it reads, how it preserves the platform's security, where it helps change and release teams, and where Dassault's own capabilities are the simpler answer.
What usually gets in the way
The problems we hear most from plm architect teams running Dassault Systemes 3DEXPERIENCE.
Custom schema, scarce expertise
Years of customization add types, attributes and relationships that only a handful of architects understand. Ad hoc questions depend on them.
Change navigation is slow
Tracing a change request through change actions to affected items, then to downstream ERP and manufacturing, crosses apps and often multiple exports.
Collaborative-space security is hard to replicate
Exports used for analysis bypass project and collaborative-space controls. Teams then manage sensitive data in uncontrolled copies.
Documentation is scattered
Requirements, test reports and supplier documents live as document objects, shared drives and email. Finding the governing statement takes time.
ERP handoff is a recurring audit finding
Released structures reach the ERP through integrations with their own mapping rules. Differences in effectivity and variants are discovered late.
Where AI earns its place in Dassault Systemes 3DEXPERIENCE
Each use case names the ERP objects it reads or writes, so your ERP team can judge the integration effort before anyone commits budget.
Change action impact analysis
From a change request or change action, list affected items, parent assemblies and linked documents, then draft an impact summary for the board with object citations.
Touches: Change Request, Change Action, affected items, Part, Document, relationships
Outcome: Faster board preparation, with analysts reviewing and correcting drafts instead of assembling them.
Natural-language product structure queries
Ask where a part is used, which revision is released in a product, or what changed between two revisions of an assembly.
Touches: Part, EBOM relationships, revisions, policy states
Outcome: Routine structure questions move from architects to the engineers who ask them.
Requirement and spec retrieval
Answer from controlled specifications and test reports, citing the document object, revision and section.
Touches: Document objects, file versions, requirement objects where used
Outcome: Less time hunting for the governing requirement before design reviews.
Release gate checks
Check pending items for missing approvals, unreleased children, absent attributes or unmapped ERP fields against your own release rules.
Touches: Policy states, route and task objects, required attributes
Outcome: Fewer releases bounced late in the process.
PLM to ERP structure comparison
Compare released engineering structures with ERP manufacturing BOMs and list quantity, unit and effectivity differences.
Touches: EBOM, ERP BOM and item master, integration mapping tables
Outcome: Differences caught at handoff rather than during the first build.
Supplier and part reuse suggestions
Suggest existing parts and approved sources when a new part is being defined, using attributes and past usage.
Touches: Part attributes, classification, manufacturer equivalents, ERP vendor records
Outcome: Reduced duplicate part creation where classification is reasonably clean.
Dashboard Q&A
Answer questions about program status, open changes and release backlog that feed 3DDashboard views, with the query shown.
Touches: Change objects, state counts, program or project attributes
Outcome: Managers can ask follow-up questions instead of requesting new dashboard widgets.
Reference architecture
The AI service sits beside 3DSpace and uses the platform's authentication and web services. It retrieves evidence under the user's identity, hands it to a private model, and returns cited answers. Write actions are limited to drafts the user submits in the platform.
- 1
Identity and access
3DPassport authentication with delegated tokens or a scoped service account for indexing, so collaborative-space and project security is evaluated by the platform.
- 2
Data access
3DEXPERIENCE web services for object search and expansion, plus read-only database views or MQL-based extraction for on-premises ENOVIA where web services do not cover a custom type.
- 3
Retrieval layer
Structured object lookups with relationship expansion, and a vector index over document content that retains type, name, revision and state for citation.
- 4
Model layer
Open-weight models served locally with vLLM or Ollama, sized to concurrency, with no outbound inference calls.
- 5
Governance layer
Per-user audit of prompts and retrieved objects, evaluation sets drawn from real change records, and prompt rules that forbid answering outside retrieved evidence.
Integration notes for your ERP team
- Authenticate through 3DPassport; keep tokens short-lived and scoped, and avoid shared service accounts for question answering.
- Map your custom types and attributes in a configuration file so the AI layer queries them by real names rather than guessing.
- Expand relationships selectively, such as EBOM and change affected-item links, to keep requests light on the platform.
- On-premises ENOVIA databases are Oracle or SQL Server; read-only views are acceptable for indexing only if they do not bypass security for question answering.
- Retain state and revision as metadata so answers can say whether an item is in work or released.
- Index incrementally by modification date and schedule bulk loads outside engineering hours.
Deployment options
On-premises ENOVIA
Programs with classified or controlled data and self-managed platform
All components run on customer GPUs inside the existing network zone, with retrieval through internal services. Suitable for air-gapped environments.
Private cloud tenancy
Customers running the platform on dedicated infrastructure in their own cloud
Model hosting in the same tenancy and region with private connectivity and customer-managed keys.
Hybrid with Dassault-hosted platform
Customers on a vendor-managed deployment who want inference under their control
The AI service runs in the customer's environment and calls platform APIs for entitled objects only. Review contract terms on API use and data movement first.
Compliance and data control
How the architecture supports your obligations. Certification and accountability stay with your organisation; the design keeps the evidence straightforward.
ITAR and EAR
Controlled technical data remains in a boundary you manage, access is user-level and logged, and nothing is sent to public inference endpoints. Your export-control program continues to govern who may be a user.
CMMC Level 2 and NIST SP 800-171
Where CUI is processed, the AI stack follows your system security plan: access control, audit, encryption and media handling are documented with the deployment.
AS9100 and configuration control
Answers cite released revisions and states; drafts never alter baselines. Formal change records stay in the platform.
IATF 16949 change and PPAP linkage
For automotive suppliers, change summaries can reference affected control plans and PPAP documents, with approvals kept in the formal process.
How an engagement runs
Phase 1 . 2 to 3 weeks
Discovery
- -Schema walk-through of types and relationships
- -Question inventory from change and release teams
- -Security and export-control review
- -Interface access plan
Phase 2 . 5 to 6 weeks
Pilot build
- -Authenticated connector
- -Structure and change retrieval
- -Cited answer interface
- -Evaluation set
Phase 3 . 4 weeks
Workflow agents
- -Change impact draft
- -Release gate checks
- -ERP structure comparison
- -Admin dashboard of usage
Phase 4 . 2 to 3 weeks
Hardening
- -Access regression tests
- -Upgrade runbook
- -Model update process
- -Training
Questions to ask any vendor, including us
A short list that separates real Dassault Systemes 3DEXPERIENCE AI work from a chatbot demo.
- Are queries evaluated with the user's collaborative-space and project access?
- How are custom types and attributes handled, and who maintains the mapping?
- Which platform versions and APIs are supported?
- Can every answer cite object type, name and revision?
- What data leaves the platform and where is it stored?
- How are write actions limited and approved?
- What happens at a platform upgrade?
- Which questions would built-in dashboards or search already solve?
Frequently asked questions
Does this work with on-premises ENOVIA as well as the 3DEXPERIENCE platform?
Yes, with different access paths. The platform exposes web services, while older on-premises installations may need supported extraction methods for custom types. In both cases, the aim is to keep access control in force and avoid bulk exports that bypass it.
Can the model learn our product data permanently?
Normally it should not. Retrieval fetches current objects at question time, so changes appear immediately and access rules apply. Fine-tuning on product data is optional and raises IP and export-control questions that should be decided separately.
Do you need to touch CATIA models?
No. Questions are answered from business-object metadata and documents. Geometry analysis is a separate scope and is not needed for impact, BOM or release questions.
How is this different from Dassault's built-in assistants?
Built-in features are the easier route for tasks inside the apps. A separate layer fits when you need your own hosting, cross-system answers with ERP and quality data, or rules specific to your change process.
Can it approve changes?
No. It prepares impact summaries and checks. Routes, approvals and signatures stay with named users in the platform.
How long before value shows?
A read-only pilot over structure and change questions is usually meaningful within one to two months of access being granted. The schedule depends mostly on access approvals and how much customization needs mapping.
What does it cost to run?
Main costs are GPU hardware or cloud instances, engineering time for mapping and evaluation, and ongoing maintenance. Open-weight models avoid per-token fees, though total cost depends on concurrency and document volume.
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Talk it through with an engineer who knows Dassault Systemes 3DEXPERIENCE
Bring one real question your team cannot answer from the ERP today. We will map the data path, the model, and where it runs, and tell you honestly if AI is the wrong tool for it.