Coray is the Director of GSI Partner Marketing at PTC, based in London, UK. She has led Analyst Relations and Corporate Communications within PTC’s marketing organization. Prior to PTC, Coray ran Industry Relations at CRM startup Creatio, overseeing investor, analyst, and PR engagement. She began her career in financial services as an equity sales trader at Needham & Co. in New York, covering Industrials, including PTC, from an investment perspective.
Ask an enterprise leader why their AI initiative stalled, and the answer is rarely the model. It's the data.
Most engineering and manufacturing AI today runs on PLM environments that weren't built for this moment. They've been customized for decades, spread across tools and teams, and are often still on legacy platforms like Oracle Agile PLM that can't give AI reliable, connected context. Pilots get built anyway, inside a single tool, and that's where they stay.
ProductAscentAI™ changes that. It's AI-Powered PLM Standardization from PTC and Transition Technologies PSC. It standardizes your PLM on Windchill and puts governed AI to work across it, so your AI moves from isolated experiments to a connected engineering environment that scales.
AI is easy to pilot. It's hard to scale.
Your teams are already using AI. Copilots draft requirements, assistants suggest test cases and tools summarize documents. Each one helps, but none of them share a common, governed view of the product. Three blockers show up every time:
Fragmented legacy PLM
Product data is spread across customized, non-standardized systems, so AI has no single source it can trust.
Manual, isolated AI pilots
Engineers still lose hours searching for information, reviewing requirements and preparing tests by hand. One-off experiments lack the context and traceability to scale.
Governance that arrives too late
Security, IP protection and human oversight have to be built in from day one, not bolted on once AI is already in production.
Every disconnected pilot adds another version of the truth, and every customization makes the next integration harder. The enterprises that win with AI fix their data foundation first.
Why AI Needs a Strong Data Foundation
See why trusted, connected product data is what turns AI from pilot to payoff.
Read the BlogTwo halves of one answer
PTC brings the platform. You get governed product lifecycle platforms with embedded AI across Windchill, Codebeamer and Creo. That covers AI-supported requirements development, architecture proposals, test generation, design guidance and change impact analysis, all inside one traceable digital thread. Windchill AI puts that intelligence where your product data already lives, and Model-Based Systems Engineering (MBSE) adds AI-generated design review for system-of-systems designs.
TT PSC brings the path to get there. It starts with an AI & Data Readiness Assessment and use-case prioritization. TT PSC then consolidates fragmented or end-of-life PLM environments, including Oracle Agile PLM, into Windchill. It prepares your data, implements PTC AI, and uses its AI framework to connect customer-specific agents with existing infrastructure and workflows across PLM, ALM, MBSE and other enterprise systems. Its proprietary accelerators cut project time and cost.
Embedded AI on fragmented data just gets you wrong answers faster, and clean data without AI never reaches the payoff. Together, they give you AI that scales instead of stalling.
Four pillars. One governed foundation.
AI-Augmented Engineering: TT PSC combines its AI framework, customer-specific agents and existing enterprise infrastructure to turn complex engineering information into a high-quality architecture proposal that engineers validate.
PLM Standardization: You get one governed Windchill environment, with embedded AI and a digital thread from engineering to manufacturing.
Trusted, Connected Data: You get governed, connected product data with end-to-end traceability from requirements to service.
Governed AI Adoption: AI and data readiness, custom AI agents, human-in-the-loop validation, security and IP protection are built into how AI scales from pilot to production.
AI proposes. Engineers validate. Windchill anchors governance.
Step 1: AI analyzes what already exists
TT PSC's AI framework connects customer-specific agents with your existing infrastructure to analyze requirements, specifications and product knowledge. It then proposes an initial architecture: functional breakdown, candidate system blocks, interfaces, and key insights such as dependencies and conflicts.
Step 2: Engineers validate and approve
Your engineers confirm scope, refine functions and system blocks, assign ownership and resolve open risks. The first draft becomes a validated baseline in PTC Modeler, and human judgment stays in charge.
What's New in PTC Modeler 10.3
See the latest capabilities for validating and governing system models.
Meet PTC Modeler 10.3Step 3: Govern the lifecycle across connected engineering systems
After validation, the results are transferred in a controlled manner to Windchill and the other engineering systems involved. Windchill provides the governed product data backbone, while ALM, MBSE, CAD/ECAD, ERP, test, quality, production and service systems support their respective lifecycle processes. Together, these connected systems maintain traceability from requirements and architecture through design, manufacturing, verification and service. AI supports each stage by analyzing change impact, identifying risk earlier and helping prepare traceable, audit-ready evidence for compliance. In a recent AI-augmented systems engineering engagement, this approach cut early-phase requirements and architecture analysis effort by 60–80%. Weeks of manual document review became a structured proposal that engineers could review in days.
Model Systems Engineering With Confidence
Explore how PTC MBSE connects requirements, architecture and design.
Explore PTC MBSEWhat you get
60–80% faster early-phase requirements and architecture analysis
Faster time to market and higher product quality
Lower TCO through reuse and standardization
Lower risk, with complete transparency and audit-ready, compliance-ready evidence
Clean legacy system retirement, with no stranded data
Built for the problems you're solving now
Legacy PLM standardization: Move off Oracle Agile PLM or other customized PLM into one governed Windchill environment.
AI-augmented requirements and architecture: Generate first-draft system architectures from the documents you already have.
Change impact and compliance: Automate impact analysis across functions, components and tests.
AI-supported test and design: Get AI-assisted test generation and design guidance in Codebeamer and Creo.
Scaling AI responsibly: Move from isolated pilots to governed, production-grade AI.
Enterprise traceability: Trace every customer requirement through to service.
Proven in production
At Schaeffler, TT PSC helped build a connected digital thread across engineering systems. TraceApp, a custom PTC ThingWorx application, creates bidirectional, tamper-proof traces between Codebeamer and RV&S, delivering a 100% reduction in manual data synchronization and full bidirectional traceability.
For a leading premium automotive manufacturer, TT PSC automated PLM testing end to end, cutting testing time by 80% with full test coverage of the PLM system.
Behind it is a partnership built for scale: 20+ years of PTC delivery partnership, 200+ PTC-dedicated specialists, 1,500+ enterprise customer projects and Platinum PTC GSI partner status.
Your path from pilot to scale
- AI & Data Readiness Assessment: Map your PLM and legacy footprint, prioritize AI use cases and build your roadmap into Windchill.
- Pilot and validate: Launch your highest-value use case with governed AI and measure the results before you scale.
- Scale across the connected digital thread: Extend AI-augmented engineering across Windchill and the engineering and enterprise systems supporting the lifecycle, from requirements through service.
The engineering leaders who pull ahead won't be the ones running the most AI pilots. They'll be the ones whose product data is trustworthy enough for AI to deliver. That's what ProductAscentAI is built for.
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