An AI assistant answers questions — retrieving and synthesizing information from connected knowledge sources. An AI agent takes actions — executing sequences of operations across connected systems to complete a multi-step task. In an engineering context, an assistant answers ‘what are the approved surface treatments for this material specification?’ An agent, given ‘process this ECR and route it for approval,’ reads the ECR in Teamcenter, analyzes the BOM impact, generates an impact assessment, creates the Engineering Change Order, assigns review tasks to defined approvers, creates a SAP notification, and reports completion — all autonomously. EMUG deploys both capabilities with agent automation scoped to workflows where human oversight gates are defined for safety-critical decision points.
EMUG integrates AI agents with Teamcenter through Teamcenter’s Service Oriented Architecture (SOA) API layer. For Windchill, integration uses Windchill’s REST API and Java Content Repository interface. For 3DEXPERIENCE, integration uses 3DSpace REST APIs. Authentication uses the same credentials as the engineering user’s PLM session — enforcing PLM access controls within agent tool calls. All API calls are logged in an agent audit trail with user identity, timestamp, operation type, and affected objects. EMUG’s integration layer handles session management, rate limiting, error handling, and retry logic for reliable production operation.
EMUG designs three layers of safeguards. First, confidence gating — agents evaluate their confidence before execution, and low-confidence situations are automatically escalated to human review. Second, human-in-the-loop gates — all agent workflows have defined decision points where human engineer review and approval is required before the agent proceeds. Third, dry-run mode — before any agent workflow is trusted for autonomous execution, it runs in parallel with manual process execution for comparison of agent versus manual outputs. Agent error rate monitoring and automatic escalation to human review is a standard component of all production deployments.
EMUG builds engineering AI agents on LangChain and LangGraph frameworks, which provide production-grade agent orchestration, tool call management, state machine design for multi-step workflows, and human-in-the-loop integration. LangGraph is used for complex multi-agent workflows requiring explicit state management. LLM backends are selected based on client infrastructure: Azure OpenAI Service (GPT-4o) for Microsoft Azure deployments, AWS Bedrock (Claude 3) for AWS deployments, and self-hosted Llama 3 or Mistral for clients requiring on-premise or ITAR-compliant air-gapped deployments.
A focused engineering AI agent deployment covering one workflow such as ECM automation in Teamcenter with SAP notification integration runs 14 to 18 weeks using the EMUG GUIDE Framework. A multi-workflow agent suite covering three to five engineering workflows across PLM and SAP runs 20 to 28 weeks. The integration complexity of each PLM and SAP system API is the primary schedule driver — Teamcenter SOA and Windchill REST integrations typically require six to eight weeks of integration development and testing before agent capability development begins.
Yes. For ITAR-classified engineering environments, EMUG designs agent architectures that ensure all product data and technical information remains within ITAR-compliant infrastructure perimeters. This requires on-premise or private cloud LLM deployment (Llama 3 or Mistral on ITAR-accredited infrastructure) rather than public LLM API calls that would route classified technical data through non-ITAR-cleared infrastructure. The RAG vector database is also hosted within the ITAR perimeter. EMUG provides ITAR compliance architecture documentation confirming data handling controls for the full agent data flow path.
EMUG addresses multilingual engineering AI in two ways. First, the RAG knowledge base indexes documents in their original language with multilingual embedding models (multilingual-e5 or similar) that retrieve relevant content across languages from a single query. Second, the LLM layer uses GPT-4o or Claude 3 multilingual generation capability to respond in the user’s language regardless of the language of the retrieved source documents. For engineering contexts requiring precise technical translation, EMUG implements terminology management that enforces approved technical vocabulary in each language.
EMUG delivers engineering AI assistant and agent programs to automotive OEMs and Tier 1 suppliers (ECM, FMEA, PPAP automation with IATF 16949 compliance), aerospace and defense organizations (configuration control, airworthiness documentation, MRO agents with AS9100 and ITAR compliance), industrial machinery manufacturers (engineer-to-order design management agents), energy, oil, and gas companies (engineering document management and regulatory submission agents), and engineering services and EPC firms. Delivery countries include Germany, France, UK, Netherlands, Sweden, Italy, Spain, Poland, Czech Republic, UAE, Saudi Arabia, Qatar, Kuwait, Bahrain, India, China, Japan, South Korea, Malaysia, Thailand, USA, Canada, Mexico, Brazil, South Africa, Nigeria, and Kenya.
