Generative AI for engineering applies large language models and RAG specifically to engineering productivity tasks: finding and synthesizing information from PLM and SAP knowledge bases, drafting structured technical documents, checking designs against engineering standards, and accelerating engineering change management. It differs from general enterprise AI in three ways. First, it operates on engineering-domain knowledge sources including Teamcenter, Windchill, CAD metadata, and SAP document repositories. Second, it requires domain fine-tuning and hallucination mitigation specific to safety-critical engineering contexts where inaccurate AI outputs create compliance and liability risks. Third, it integrates into engineering workflows within PLM clients, SAP Fiori, and CAD environments. EMUG’s EMUG SPARK Framework addresses all three requirements.
EMUG designs RAG pipeline integrations for Teamcenter (including Active Workspace and BMIDE document services), Windchill (PDMLink, MPMLink, and Windchill document management), 3DEXPERIENCE (ENOVIA document and MBOM services), SAP Document Management System (DMS), SAP QM document handling, and SharePoint engineering libraries. CAD metadata connections are available for NX, Creo, and CATIA through their respective API layers. The RAG pipeline connects to these sources through governed API integrations that respect document access controls, revision status filters, and data classification policies.
EMUG implements four hallucination mitigation mechanisms. First, strict RAG-only retrieval — every answer must be grounded in retrieved source documents from the connected PLM and SAP knowledge base. Second, answer confidence scoring with defined thresholds below which the assistant declines to answer and directs the user to a human expert. Third, source citation enforcement — every answer includes the specific document, revision, and section from which information was retrieved. Fourth, red-team testing against an engineering query benchmark covering the top 100 most common question types for each deployed use case, with answer accuracy validated before production release.
A focused engineering knowledge assistant deployment covering one knowledge source and one primary use case runs 10 to 14 weeks using the EMUG SPARK Framework. A full engineering AI platform covering multiple knowledge sources and use cases runs 20 to 28 weeks. EMUG provides a functional prototype demonstrating answer accuracy on a representative query set within four weeks of engagement start, giving stakeholders evidence of production viability before committing to full deployment investment.
EMUG designs and deploys generative AI on GPT-4o and GPT-4-turbo (Azure OpenAI Service), Claude 3 Opus and Claude 3.5 Sonnet (AWS Bedrock or Anthropic API), Mistral Large and Mistral 8x22B (Mistral API or self-hosted), and Llama 3 variants for clients requiring on-premise or air-gapped deployment. For ITAR-classified programs and EU AI Act high-risk deployments, EMUG designs local or private cloud LLM deployments ensuring no customer engineering data leaves the approved infrastructure perimeter.
Yes. EMUG has designed generative AI documentation programs specifically for IATF 16949 (automotive), AS9100 Rev D (aerospace), and EU regulatory submission requirements. All regulated documentation AI programs include mandatory human review gates, source citation for every generated statement, audit trail logging of AI generation events, and integration with the existing document approval workflows in Teamcenter or Windchill. The AI generates first drafts that engineers review, verify, and approve — delivering time savings of 60 to 70 percent while preserving the human accountability required by regulated quality management systems.
Access controls from the source PLM or SAP system are enforced at the retrieval layer — engineers can only retrieve content they are authorized to access in the source system. For ITAR-classified programs, all engineering data remains within ITAR-compliant infrastructure with no transmission to public LLM APIs. For EU-based deployments, data residency within EU Azure or AWS regions is enforced by architecture. All AI interactions are logged with user identity, query content, and retrieved sources for audit trail compliance.
EMUG delivers generative AI for engineering programs across Europe (Germany, France, UK, Netherlands, Sweden, Italy, Spain, Poland, Czech Republic), the Middle East (UAE, Saudi Arabia, Qatar, Kuwait, Bahrain), Asia-Pacific (India, China, Japan, South Korea, Malaysia, Thailand), the Americas (USA, Canada, Mexico, Brazil), and Africa (South Africa, Nigeria, Kenya). For multi-region deployments, EMUG designs data residency architectures that satisfy GDPR for European operations, ITAR for US defense programs, and India DPDP Act requirements for Indian operations.
