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AI Agents for Manufacturers: Video Demo at Siemens Realize LIVE 2025
See Manufacturing AI Agents in Action
Digital Thread Intelligence, Engineering Knowledge Retrieval, and Production Decision Support
Siemens Realize LIVE is the annual conference that brings together the global community of engineers, manufacturing IT leaders, and digital transformation professionals building the next generation of industrial software on the Siemens ecosystem. It is where the most technically demanding manufacturing organizations evaluate what is possible with AI in production environments and where Sinequa’s AI agents for manufacturing were demonstrated in 2025 to an audience of exactly those buyers.
In this video demo, Jeff Evernham, Chief Product Officer at Sinequa, explores how our AI agents help manufacturers:
- Optimize production through continuous feedback loops
- Boost productivity by surfacing insights from unstructured data
- Discover key insights across the digital thread
- Streamline decision-making with AI research assistants
Whether you’re starting your digital transformation or scaling it, this demo shows what’s possible when AI-Powered Search meets manufacturing innovation.
Watch the demo
What the Demo Shows
Digital Thread Intelligence: Unlocking the Value Across the Full Product Lifecycle
The digital thread is the connected information flow that runs from initial product design through engineering, manufacturing, quality, maintenance, and service, the continuous record of a product’s lifecycle across every system that touches it. For most manufacturers, this thread is broken: data exists in PLM systems, CAD environments, MES platforms, ERP systems, and service records, but no single query can reach across all of them simultaneously. Sinequa’s AI agents connect across the digital thread, allowing engineers and decision-makers to ask questions that span systems and receive answers synthesized from the full lifecycle record with every response grounded in and traceable to actual source documents.
Engineering Knowledge Retrieval at the Speed Decisions Actually Require
Engineers at large manufacturers spend a significant portion of their working time searching for information that already exists somewhere in the organization: looking for a previous design decision, finding the relevant section of a technical specification, retrieving a maintenance procedure for a variant of a component, or identifying which colleagues have worked on a related problem. Sinequa’s AI search and RAG capabilities eliminate this retrieval overhead surfacing the right engineering knowledge in seconds rather than hours, with natural language queries that do not require knowledge of which system contains the answer.
Production Optimization Through Continuous AI Feedback Loops
Production decisions that rely on periodic manual review of disconnected data sources are slower and less reliable than decisions supported by AI agents that monitor production signals continuously. The demo demonstrates how Sinequa’s AI agents surface insights from unstructured production data, quality reports, maintenance logs, operator notes, sensor data summaries in real time, flagging patterns and anomalies that matter for production outcomes before they become incidents.
AI Research Assistants That Understand Manufacturing Context
The demo shows Sinequa’s AI research assistant in a manufacturing context: an engineer asks a natural language question spanning technical documentation, past project records, and regulatory compliance requirements and receives a synthesized, cited response drawn from the firm’s actual knowledge environment. This is the difference between a generic AI assistant that answers from public training data and an enterprise AI agent that answers from the manufacturing organization’s proprietary knowledge with access controls that ensure engineers only retrieve information from systems they are authorized to access.
Frequently Asked Question
The digital thread is the integrated data flow connecting every stage of a product’s lifecycle — from initial design and engineering through manufacturing, quality assurance, maintenance, and end-of-life service. It represents the complete information record of a product: CAD models, engineering specifications, bill of materials, manufacturing process documentation, quality inspection records, maintenance histories, and service records. In most large manufacturing organizations, this thread is fragmented: the data exists, but it lives in disconnected systems — PLM, MES, ERP, CMMS, document repositories — that cannot be queried together. AI agents unlock the value of the digital thread by connecting across these systems and enabling engineers and decision-makers to ask questions that span the full lifecycle, receiving synthesized answers grounded in the actual data environment rather than the subset any single system can provide. The result is engineering decisions informed by the full picture of a product’s history and operational data, rather than the fragment visible from whichever system the engineer happens to be working in.
Sinequa’s enterprise AI platform is deployed on four manufacturing workflow categories where the productivity and quality impact is highest. Engineering knowledge retrieval: AI-powered search across technical documentation, engineering specifications, past project records, and design decisions — reducing the time experienced engineers spend finding information that already exists in the organization. Production optimization: AI agents monitoring production data continuously for patterns and anomalies that affect quality and throughput, enabling faster identification and resolution of production issues. Maintenance and service intelligence: AI-assisted retrieval and synthesis of maintenance procedures, component histories, and technical documentation for field service and maintenance teams. R&D and innovation support: AI agents that surface relevant prior research, patent landscapes, regulatory requirements, and technical precedents for engineering and R&D teams working on new product development.
Sinequa connects to Siemens Teamcenter and other PLM systems through its enterprise connector library, indexing engineering data — parts, documents, configurations, relationships, revision histories — and making it available to Sinequa’s AI search and agent layer alongside data from other enterprise systems. This means engineers can ask a question that spans Teamcenter engineering data, ERP production records, maintenance system documentation, and unstructured technical documents simultaneously — without needing to know which system holds the relevant information or switching between interfaces. The access control architecture ensures that Teamcenter permissions and security classifications are respected at the retrieval layer: AI responses only incorporate data from Teamcenter objects the requesting user is authorized to access under the existing PLM governance model.
Engineering data in manufacturing environments carries some of the highest intellectual property sensitivity in any industry: design specifications, proprietary manufacturing processes, performance data, and competitive technical knowledge that took years and billions in R&D investment to produce. Sinequa’s early-binding security architecture enforces data access permissions at the retrieval layer — the same moment the AI agent accesses a document — rather than as a post-processing filter. This means that access controls from PLM, ERP, and document management systems are enforced within the AI environment without requiring separate security configuration: if a user does not have access to a CAD file or specification document in Teamcenter or SharePoint, the AI agent will not incorporate that document’s content into responses for that user. Deployment architecture options — on-premise, private cloud, or hybrid — ensure that sensitive engineering data can remain within the manufacturer’s controlled infrastructure throughout, with no requirement for proprietary technical data to transit external networks for AI processing.
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