Retrieval Augmented Generation (RAG) for PLM Systems: Architecture, Use Cases, and Maturity

January 28, 2026 Uncategorized Like: 0

ByIlan Madjar

In this post, Ilan Madjar explores how Retrieval Augmented Generation (RAG) can be applied to PLM environments to populate generative AI in enterprise system data and support an Agentic AI approach in the organization. The article breaks down the technical architecture behind RAG, explains how it differs from execution-focused approaches like MCP (model context protocol), and outlines practical PLM use cases and maturity stages.

Generative AI is increasingly being evaluated for use in PLM environments, but applying large language models directly to engineering and manufacturing data presents well-known challenges. PLM information is fragmented across systems, governed by complex schemas, and heavily dependent on context such as revision, state, and effectivity. Without grounding, AI systems produce responses that may be fluent but unreliable. Retrieval Augmented Generation (RAG) addresses this problem by anchoring AI responses in enterprise system data. To see RAG at work, view our live demo on YouTube. In this post, I’ll outline what RAG is in the context of PLM systems, how it differs from Model Context Protocol (MCP), and the technical foundation required to implement it in a practical, vendor-neutral way.

What is RAG for PLM?

RAG combines semantic retrieval with generative AI at runtime. Instead of prompting a language model directly, RAG introduces an intermediate retrieval step that pulls preprocessed relevant content from enterprise data sources. The retrieved content is then passed to the model as grounded context for response generation (this is similar to populating a structured dictionary database). In PLM environments, this means answers are based on:

Actual BOM structures
Real change descriptions
Verified ERP and PLM records
Associated metadata such as part number, revision and lifecycle state

This approach significantly reduces hallucinations because the model is not inventing answers — it is summarizing and reasoning over retrieved data.

RAG vs. MCP: Different Problems, Same Runtime

It is important to distinguish RAG from Model Context Protocol (MCP).

RAG focuses on knowledge retrieval from existing data sources, typically static or semi-static
MCP focuses on execution — how a model accesses, updates, or acts on live systems through workflows and APIs, although MCP can also be used for live data retrieval as we have shown in our previous demo in the series.

Both operate at runtime, but they solve different problems. In practice, RAG and MCP are complementary. RAG provides grounded context; MCP enables controlled action. This live demo focuses exclusively on RAG.

Why RAG Matters for PLM Data

PLM data rarely exists in a single system. Relevant information is typically distributed across:

PLM and PDM platforms
ERP and MES systems
PDFs, change documents, and specifications stored in sites like sharepoint, etc..

Each system uses different schemas, identifiers, and data models. RAG allows this fragmented data to be queried semantically without requiring full schema unification or manual cross-system searches. From a PLM perspective, high-value use cases include:

BOM exploration and summarization
Engineering change summaries
Cross-system context retrieval (PLM + ERP)
Reduction in time spent manually locating related data

Technical Foundation: Vectors, Embeddings, and Metadata

At the core of RAG are embeddings — numerical representations of semantic meaning. Textual PLM fields such as material descriptions or change reasons are converted into vectors. These vectors are stored in a vector database, enabling similarity-based retrieval rather than keyword matching. In the demonstrated architecture:

Structured PLM objects (e.g., BOMs) remain structured
Text fields are embedded for semantic search
Attributes such as revision and state are stored as metadata
Retrieval respects object identity and lifecycle context

This separation is critical. RAG does not flatten PLM data into text. It preserves structure while enabling semantic access where it adds value.

Vendor-Neutral Architecture

The RAG approach demonstrated is intentionally vendor-neutral. It works with:

Any PLM, PDM, or ERP system
Any vector database
Any LLM

In the demo, ChromaDB is used as the vector database and Gemini as the language model, but these are implementation choices — not architectural constraints. A simple console application acts as the agent, retrieving data from ERP CSV sources and passing the results through the RAG pipeline. The same idea can be implemented as a web application, widget, etc.

The RAG Process: Ingestion, Retrieval, Generation

RAG consists of three phases:

Ingestion: Data is parsed and preprocessed, relevant fields are selected, metadata is preserved, and embeddings are generated implicitly for relevant fields.
Retrieval: User queries matched against vectors in the database by the LLM to retrieve semantically relevant content.
Generation: The language model generates responses grounded in the retrieved data.

Key technical considerations include field mapping, semantic selection of primary content, and consistent ID management across systems.

PLM RAG Maturity Model

RAG adoption in PLM environments typically progresses through stages:

Search assist
Q&A over PLM and ERP data
Contextual insights
Cross-system intelligence
Agent-assisted decision support

Each stage builds on the previous one, increasing both capability and responsibility. RAG provides a practical foundation for applying AI to PLM systems without sacrificing accuracy or governance. By grounding generative models in enterprise data, it enables meaningful interaction with complex engineering information while respecting the realities of PLM architectures. We at xLM are actively researching and pursuing how AI can augment PLM systems and make engineering and manufacturing processing more efficient outside of what PLM vendors offer, whether is this reviewing current data and cleaning it up, improving searches and reports with such data, have AI agents make real world recommendations on data, and more. We see many possibilities on how AI will improve users’ work experiences and the processes itself. We are excited to go on this journey and share our thoughts and solutions with you. Future sessions will explore low-rate adaptation (LoRA), enabling improved accuracy for engineering terminology without full model retraining. In the meantime, questions and comments are welcome. Please get in touch to discuss RAG and emerging areas for AI in PLM. I also recommend viewing the live demo on YouTube.