Back to profile

PDM / Engineering Data Platforms

AI-native PDM systems that scale across CAD, BOMs, and analytics.

Overview

Problem: engineering data is siloed and slow to query.

Why it matters: decisions depend on clean data lineage.

For: hardware, manufacturing, supply chain engineering.

Vision & Roadmap

• Phase 0: normalize CAD + BOM data models.
• Phase 1: searchable knowledge layer with analytics.
• Phase 2: agentic change impact forecasting.
• Done means: cross-team data in seconds, not days.

Architecture

Diagram placeholder: ingest -> normalize -> serve -> analyze.

• CAD/PLM connectors with lineage tracking.
• Unified BOM normalization service.
• Search, analytics, and AI retrieval layer.

Data & AI

• Sources: Teamcenter, Active Workspace, PLM metadata.
• Models: graph RAG + metadata embeddings.
• Evaluation: recall accuracy, cycle time reduction.

Progress

• CAD metadata normalization
• Search relevance tuning
• Analytics overlay
• Change impact AI assistant

Blockers: governance on shared datasets.

Next 30/60/90: align schemas, ship analytics, pilot AI.

Learnings

• Search latency drops unlock cross-team adoption.
• Data ownership clarity is the core scaling lever.