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AI-Powered PLM in Manufacturing: Making PLM Content AI-Ready

How ENOVIA + Adlib turn CAD, BOMs, and SOPs into reliable knowledge for search, RAG, and compliance

Modern manufacturers want AI-assisted design reviews, faster change approvals, and trustworthy digital twins. The blocker isn’t the model, but the content. Most PLM ecosystems are packed with multi-format files (drawings, specs, procedures, supplier packets) that are hard to search, hard to trust, and rarely AI-ready.
This white paper explains a practical path: using Adlib to transform and validate documentation inside Dassault ENOVIA so your downstream search, analytics, and LLM/RAG use cases run on structured, governed content, not guesswork.

What you’ll learn

  • The AI-readiness gap in PLM: Why “store & route” isn’t enough for retrieval, traceability, or audit defense.
  • Content ops for engineering data: A repeatable approach to normalize 300+ file types, preserve fidelity, and add the right metadata.
  • Search & RAG that cite sources: How chunking, OCR, and object separation improve retrieval quality and reduce hallucinations.
  • Compliance by construction: Generating pixel-faithful, PDF/A outputs with signatures, bookmarks, and version integrity for audits.
  • An implementation blueprint: Where automation fits in ENOVIA workflows (intake → convert → validate → assemble → deliver).

Who should read this

  • Executives in Engineering, Manufacturing, Quality, or Digital/AI
  • PLM/ECM leaders responsible for findability, governance, and interoperability
  • KM & Data teams supporting search, RAG, and analytics on product documentation

Inside the paper

  • The State of PLM Content: Unstructured vs. AI-ready, and why it matters for knowledge reuse
  • Pattern: AI-Ready Pipeline for ENOVIA: From raw CAD/BOM/SOPs to governed, machine-navigable assets
  • Design Principles: Fidelity, provenance, minimal HITL, and regulator-friendly outputs
  • RAG in Regulated Environments: Reducing token bloat, boosting citation quality, protecting sensitive data
  • Field Notes & Outcomes: Cycle-time reductions, fewer rework loops, cleaner audits, and better supplier throughput

Why it matters to KM World readers

  • Findability that sticks: Clean structure + metadata = better recall/precision in enterprise search and RAG.
  • Trust at scale: Document-of-record outputs that preserve context, lineage, and signatures.
  • Interoperability: ENOVIA remains the system of work; content becomes usable everywhere (QMS, MES, ERP, analytics, LLMs).