The result? Faster decisions, fewer errors, and a way to preserve institutional knowledge. Let\u2019s explore what makes this possible and how you can start using Teamcenter Copilot today.<\/p>\n\n\n\n
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\ud83d\udcda What are knowledge bases<\/h3>\n\n\n\n
In Teamcenter, a knowledge base is a curated collection of documents already managed in your PLM system. You define the scope \u2013 maybe all regulations for a product line, or all work instructions for a manufacturing process, and Teamcenter keeps that collection up to date.<\/p>\n\n\n\n
The point of using knowledge bases is control: you decide which files are \u201cin\u2011bounds\u201d for a given conversational session, and Teamcenter Copilot restricts its grounding context to that data. Responses are returned with citations to the specific Teamcenter files used, so you can click through to validate and dig deeper.<\/p>\n\n\n\n
Knowledge bases are query\u2011backed and stay current as content is added, modified, or retired. Access policies are enforced at retrieval time, so a user only gets context from documents they\u2019re entitled to \u2013 no special configuration needed beyond your existing Teamcenter permissions model.<\/p>\n\n\n\n
![\"\"](\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/14\/2025\/12\/TC-Copilot-BOM-chat-with-docs-600x337.png\")
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\ud83d\udd0d How Teamcenter Copilot’s chat with knowledge bases works \u2013 at a high level<\/h3>\n\n\n\n
User flow: <\/strong> From the user\u2019s perspective, it\u2019s simple: pick a knowledge base, ask a question in plain language, and get an answer with citations. Behind the scenes, two core services make this possible:<\/p>\n\n\n\n These services work together in a retrieval\u2011augmented generation (RAG) architecture.<\/p>\n\n\n\n General\u2011purpose chatbots can be helpful, but their knowledge is limited to their training data and what\u2019s available on the internet. They don\u2019t have access to your PLM system of record, so they can\u2019t accurately represent your data. And if you do share your organization\u2019s data with one of these general-purpose chatbots, you run the risk of data leakage.\u200b<\/p>\n\n\n RAG was designed to solve just this problem. It\u2019s a design pattern that adds an information\u2011retrieval step between user input and response generation, with indexing, security, and refresh cycles.<\/p>\n\n\n\n RAG in one line:<\/strong> With Teamcenter Copilot\u2019s RAG architecture, the LLM provides language and reasoning \u2013 but the knowledge comes from your PLM data.<\/p>\n\n\n\n And since Teamcenter Copilot is integrated with your PLM system of record, it’s grounded in your continuously updated data and context, enforces access controls, establishes guardrails, and protects your data.\u200b<\/p>\n\n\n\n We\u2019ll explain this by walking through the three main components of Teamcenter Copilot’s architecture:<\/p>\n\n\n\n Teamcenter Copilot utilizes advanced AI technologies to enable conversational chat with documents:<\/p>\n\n\n\n Embedding model<\/strong> Vector database<\/strong> Large language model (LLM)<\/strong> The embedding service manages the flow of data and coordinates the different steps involved in vectorizing Teamcenter data. It also ensures knowledge bases are continuously updated with documents you\u2019ve added, removed, or modified. Transforming files from your Teamcenter instance into embeddings in a vector database is a multi-step process:<\/p>\n\n\n\n Extract and transform<\/strong> Chunk and embed<\/strong> Store for retrieval<\/strong> The inference service handles the flow of data and coordinates the different steps involved in the question-answering process. When you ask a question of one or more knowledge bases, the inference service takes over. Here\u2019s what happens:<\/p>\n\n\n\n Vectorize the query<\/strong> Retrieve relevant context<\/strong> Apply access controls<\/strong> Generate the answer<\/strong> Getting started with Teamcenter Copilot is straightforward once you understand the building blocks. The steps below outline the process:<\/p>\n\n\n\n Don\u2019t let your organization\u2019s knowledge stay hidden. With Teamcenter Copilot, you can preserve institutional expertise, accelerate decision-making, and empower every team member with instant access to the answers they need.<\/p>\n\n\n\n Contact us today to learn how you can get started with Teamcenter Copilot and unlock the hidden knowledge in your organization\u2019s data. <\/p>\n\n\n\n Read our other blogs to learn more about the power of Teamcenter AI<\/a>. <\/p>\n\n\n\n Start fast and grow with Teamcenter X<\/a> | Make smarter decisions with AI-powered PLM<\/a> <\/strong><\/p>\n\n\n\n
select a knowledge base \u2192 ask a question \u2192 receive an answer with citations<\/strong><\/p>\n\n\n\n\n
\n\n\n\n\ud83e\udde0 What is Retrieval-augmented generation (RAG)<\/h3>\n\n\n\n
![\"\"](\"https:\/\/blogs.sw.siemens.com\/wp-content\/uploads\/sites\/14\/2025\/12\/Teamcenter-Copilot-RAG-architecture-scaled.png\")
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LLM fluency + your PLM data + permission checks = reliable answers you can trust<\/strong><\/p>\n\n\n\n
\n\n\n\n\u2699\ufe0f How Teamcenter Copilot’s chat with knowledge bases works \u2013 in detail<\/h3>\n\n\n\n
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\n\n\n\n\ud83e\udde9 AI stack<\/h3>\n\n\n\n
Converts text into dense vectors that capture meaning. This enables semantic search, so Teamcenter Copilot can find relevant content even when phrasing differs.<\/p>\n\n\n\n
Stores embeddings and supports similarity search. Instead of scanning entire documents, Teamcenter Copilot retrieves only the most relevant chunks, making responses fast and focused. It\u2019s continuously being updated with new and modified data.<\/p>\n\n\n\n
Generates the final answer in natural language. The LLM doesn\u2019t \u201cknow\u201d your data by itself, it relies on the retrieved context to ground its response.<\/p>\n\n\n\n
\n\n\n\n\u2714\ufe0f Embedding service<\/h3>\n\n\n\n
Teamcenter runs an extract, transform, load (ETL) process on the documents in your knowledge base, extracting text and breaking it into smaller passages (\u201cchunks\u201d) suitable for semantic search. Text is extracted from common formats like PDF, Word, Excel, and PowerPoint using Apache Tika, and OCR is applied to image\u2011embedded text (e.g., TIFF\/JPEG scans). It also indexes structured information from NX\/Solid Edge drawings, Teamcenter Requirements, and Teamcenter Requirement Specifications.<\/p>\n\n\n\n
Each chunk is converted into a vector embedding by an embedding model. These embeddings capture semantic meaning, so similar concepts can be matched even if the wording differs.<\/p>\n\n\n\n
The embeddings are stored in a vector database, optimized for fast similarity search. As documents change or you add\/remove files, the embedding service updates the index so your knowledge bases stay current.<\/p>\n\n\n\n
\n\n\n\n\ud83d\udcac Inference service<\/h3>\n\n\n\n
Your question is converted into an embedding using the same model that processed your documents. This ensures both queries and content live in the same semantic space.<\/p>\n\n\n\n
Teamcenter Copilot searches the vector database for chunks most similar to your question. This isn\u2019t keyword matching, it\u2019s semantic retrieval. So, \u201cheat treatment guidelines\u201d will find relevant specs even if the document says \u201cthermal processing.\u201d<\/p>\n\n\n\n
Before anything moves forward, Teamcenter enforces your existing permissions. If you don\u2019t have access rights to a document, its content is excluded from the response.<\/p>\n\n\n\n
The retrieved context and your question are combined into a prompt for an LLM. The LLM uses this context to generate a natural language answer \u2013 augmented by your data, not the internet. Teamcenter Copilot then returns the answer along with links to the source files.<\/p>\n\n\n\n
\n\n\n\n\ud83d\ude80 Getting started<\/h3>\n\n\n\n
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Teamcenter supports leading options like AWS, Microsoft Azure, or Meta Llama for on-prem deployments, providing flexibility while keeping your data under your control.
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Follow the deployment guides for your chosen platform to configure the core components: the embedding model, vector database, and LLM that power Teamcenter Copilot\u2019s conversational capability.
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This step ensures all services are properly integrated with your Teamcenter environment. Use Deployment Center for a guided installation.
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Scope your knowledge bases with the document collections you want Teamcenter Copilot to use. Share the exciting news with your colleagues and start chatting!<\/li>\n<\/ol>\n\n\n\n
\n\n\n\n\ud83d\udd13 Unlock the hidden knowledge in your enterprise<\/h3>\n\n\n\n
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