A structured maturity benchmark across PLM, engineering, and data management - measured against what a governed digital thread and a part-centric operating model actually demand. We turn scattered capability, data-quality, and governance gaps into a single prioritized, fundable improvement path, so your first investment goes where it moves the needle, not where the noise is loudest.

An enterprise-architecture-grounded capability map linking strategy to execution. It links business objectives to the PLM capabilities, applications, and data that deliver them. It gives leadership one common, governed view to sequence investment and anchor the roadmap - so business intent survives the journey to the shopfloor instead of getting lost in translation between the boardroom and the tool.

A reference model connecting product, process, and enterprise data across the lifecycle - defining authoritative sources, the data model, and the governance that holds them together. It's the precondition for analytics and AI you can trust, structured the way decisions are actually made: Business Objective → Capability → Process → PLM Component → Value. Build this first, and every use case after it inherits the foundation instead of rebuilding it.

A rapid, structured method to separate genuine AI value from hype across PLM - sizing benefit, risk, and readiness before a euro is committed. It answers the question most pilots skip: not "can AI do this?" but "will the next use case be easier than this one?" Proven first on our own delivery workflows before we ever apply it to yours.

A repeatable blueprint for moving off legacy or fragmented PLM environments - data mapping, cleansing, staging, cut-over criteria, and fallback logic, refined across Teamcenter and multi-CAD landscapes over two decades of live programs. It turns the highest-risk, highest-anxiety part of any transformation into a controlled, auditable process - so cut-over weekend is a checklist, not a gamble.