AI Integration for Dassault Systèmes 3DEXPERIENCE

The 3DEXPERIENCE platform's unified data model—spanning 3D CAD, Simulation, Program Management, and Manufacturing—provides a structured foundation for AI integration. AI agents can be embedded to act on specific objects and workflows: analyzing CATPart and CATProduct files for design reuse; parsing SIMULIA simulation results to correlate parameters with outcomes; monitoring ENOVIA project tasks to predict delays; and extracting insights from the Product, Process, Resource (PPR) structure to automate data propagation. The integration connects via the 3DEXPERIENCE REST API and webhooks to listen for events like a new simulation run completion or an Engineering Change Request (ECR) submission, triggering AI processing without disrupting user workflows.

Implementation follows a phased, governed rollout. Start with a single, high-value surface area, such as adding a natural-language search copilot to the Collaborative Industry Innovator role's dashboard for finding parts and specifications. Next, automate a workflow like using AI to draft initial impact analysis for an Engineering Change Order (ECO) within the Change Action app, suggesting affected items and required approvals. Each agent is deployed as a containerized service that calls the platform's APIs, with prompts and tools scoped to specific ENOVIA business objects and user collaborative spaces. All AI-generated outputs are logged as platform Change Items with clear attribution, maintaining the audit trail and enabling human-in-the-loop review before any system-of-record update.

Governance is critical. AI access must respect the platform's native Role-Based Access Control (RBAC) and data security policies. An agent assisting with Formula Management in the Process Engineer role, for example, should only have access to relevant formula revisions and substance databases. We architect integrations to use the 3DEXPERIENCE platform as the orchestration layer, where business logic and approvals remain in native apps, and AI serves as an assistive layer. This approach ensures compliance, minimizes risk, and allows teams to scale from a pilot—like an AI agent summarizing test reports in the Quality Management app—to enterprise-wide augmentation of the digital thread.

The core integration pattern connects AI agents to the 3DEXPERIENCE platform via its RESTful APIs (ENOVIA V6 REST Services) and webhooks. Key data objects for AI enrichment include Products, Parts, Documents, Change Items (ECOs), Simulation Results, and Project Tasks. An initial sync or delta-based ingestion pulls relevant records into a vector database (e.g., Pinecone, Weaviate) for semantic search, while a message queue (e.g., RabbitMQ, AWS SQS) handles real-time events like a new simulation run completion or an ECO submission, triggering an AI agent workflow.

A typical agent workflow for design exploration might be: 1) A designer queries the 3DEXPERIENCE platform via a natural language interface embedded in the 3DDashboard. 2) The query is routed to an AI orchestration layer, which uses RAG to retrieve similar past designs, simulation reports, and material specs from the vector store. 3) An agent calls the 3DEXPERIENCE Simulation API to fetch relevant parameters and results. 4) A summary or suggested design modification is generated, logged, and presented back in the dashboard, with references to source items. All tool calls are authenticated via OAuth 2.0 and respect the user's native 3DEXPERIENCE roles and permissions.

For rollout, we recommend a phased approach: start with a single Collaborative Industry Innovator role (e.g., Design Engineer) and a focused use case like simulation data summarization. Deploy the AI services in your cloud (AWS, Azure, GCP) or on-premise, connecting via a dedicated integration service account with scoped API permissions. Implement audit logging for all AI-generated outputs and a human-in-the-loop approval step for any agent-suggested changes before they are written back to the 3DEXPERIENCE database via POST or PATCH operations. This ensures governance and maintains the integrity of your product, process, and resource (PPR) model.

Implementing AI within 3DEXPERIENCE requires a governance-first architecture. This means treating AI agents as controlled extensions of the platform's native roles and permissions. Agents should operate within a dedicated, secure context—often a custom 3DEXPERIENCE Role—with scoped access to specific Collaborative Spaces, Business Objects (like Parts, Products, Change Requests), and ENOVIA program data. All AI-generated outputs, such as design suggestions or simulation summaries, should be logged as new revisions or comments within the relevant object's history, creating a full audit trail. This ensures compliance with rigorous design control and intellectual property (IP) protection standards inherent to engineering workflows.

A phased rollout is critical for user adoption and risk management. Start with a read-only pilot focused on knowledge retrieval, such as a RAG-powered semantic search across PLM Documents, CAD metadata, and Simulation Results. This delivers immediate value without modifying core data. The next phase introduces assistive automation in low-risk areas, like drafting meeting notes for Design Reviews or auto-tagging uploaded files. The final phase enables agentic workflows, where AI can propose actions—like initiating a Change Request or updating a BOM Line—but always requires a human-in-the-loop approval via the native 3DEXPERIENCE task or dashboard before execution. This staged approach builds confidence and isolates complexity.

Security is enforced at multiple layers. AI calls to external models (e.g., OpenAI, Anthropic) should never send raw, sensitive IP. Instead, use a secure proxy layer that strips PII and confidential design details, or employ on-premise/private cloud models where required. Data residency for vector embeddings and prompt histories must align with your 3DEXPERIENCE deployment model (on-cloud or on-premise). Finally, establish a clear rollback and oversight protocol. Designate platform administrators as AI workflow stewards who can monitor agent activity through dedicated dashboards and instantly disable specific functions if outputs drift from expected quality or compliance boundaries. For related patterns on connecting AI to complex engineering systems, see our guide on PLM System Integration and APIs.