This repository hosts the materials Enola Technologies (and our co-presenters) share at the 2026 CATIA User Symposium Americas at the Grand Sierra Resort and Casino in Reno, Nevada. If you've scanned a QR from one of our talks, this is the right place — slides and demo models for each session live in the folders below.
Just here for the materials? Jump to Sessions or straight to What's in this repo.
Enola Technologies is a passionate team of digital and systems engineers serving the U.S. Department of Defense, defense primes, and commercial systems organizations. We work across multiple engineering domains and modeling languages with one goal — to change how systems are designed, integrated, and optimized.
Our focus is making MBSE work at program scale: the tooling, the training, the automation, and the configuration-managed infrastructure that keeps the whole stack pulling in the same direction. We've supported NAVAIR (MQ-25, SET, Naval IME), Army PEO Aviation (FLRRA), and a range of other DoD digital engineering programs.
"We will train, coach, and mentor your staff to be independently successful — quickly."
🦊 Outfox the Competition.
Our service catalog at enola.com/services covers the full picture. The capabilities most relevant to the CUSA audience:
Industry-leading MBSE training, mentorship, and consulting tailored to any domain. SysML v1 and v2, UML, UAF, profile design, DSLs, methodology (MagicGrid, OOSEM), MOSA standards (FACE, GCIA, DAML), behavioral and parametric simulation, and full MagicDraw API and tool automation work — plugins, macros, scripts, Velocity Template Language reporting.
→ Engineers who can drive program decisions with their models — not just draw them.
Connecting your tools, your data, and your teams with the configuration-managed infrastructure that synchronizes data across the digital thread. Tool selection, Teamwork Cloud setup, cross-tool integration (PLM, ALM, requirements, simulation), governance, and the training pipeline that keeps a deployment sticking.
→ Programs that move faster because their tools talk to each other and the data stays trustworthy.
Enterprise architecture services that align engineering execution with business goals — and give your organization the competitive edge that comes from tech, process, and strategy actually pointing the same direction.
→ Strategic alignment between business goals, the technology stack, and the engineers who build it.
AI techniques applied where they earn their keep in systems engineering — automated model augmentation, solution-space exploration, knowledge capture, and LLM-assisted authoring, validation, and analysis. Pragmatic, not magical.
→ Authoring, validation, and analysis sped up where it counts — accelerating innovation and protecting your competitive edge.
Direct training, curated bundles, focused workshops, and open-enrollment public schedules — for engineers, architects, and program leadership. We don't just cover SysML.
→ Teams that don't need us next year.
Browse our Resource Library for briefs, example code, example models, and useful links.
When · Wednesday, May 20, 2026 · 12:30 PM – 1:30 PM PDT
Where · Nevada 12
Presenter — Neil Patel · Co-Founder / CEO, Enola Technologies
neil.patel@enolatech.com · LinkedIn
Co-Presenter — Dr. Sarah Rudder · Principal Systems Engineer, Enola Technologies
sarah.rudder@enolatech.com · LinkedIn
CATIA MOSA supports modular open system architecture (MOSA) goals with the ability to create domain-specific languages via profiles for standards such as GCIA, DAML, and FACE. A modular system has precisely defined interfaces, which enables independent suppliers to provide improved capabilities, accelerates the delivery of advanced technologies, and drives down integration risk and lifecycle cost.
Because MOSA puts interfaces first, it also supports infrastructures that integrate with legacy components. This session gives the audience a guided look at the tool's MOSA capabilities and how they support mission requirements.
→ Folder: mosa-implementation/
Hidden in Plain Sight: Data Aggregation Risks Across the Digital Engineering Landscape
When · Thursday, May 21, 2026 · 10:30 AM – 11:20 AM PDT
Where · Nevada 1/2
Presenter — Patrick Rose · ESS AEGIS TECHREP, Systems Engineer Principal, BAE Systems
patrick.rose2@baesystems.us · LinkedIn
Co-Presenter — David Fields · Co-Founder / CTO, Enola Technologies
david.fields@enolatech.com · LinkedIn
The aggregation of data poses a distinct challenge to sensitive data protection: seemingly innocuous data can become sensitive when considered in relation to other data points. The issue is particularly pronounced in digital tools, which rely on querying and relation-based architectures. As these tools continue to evolve, the rules governing data protection have to evolve with them.
This session walks through concrete case studies in CATIA Magic / MagicDraw to help engineers identify where protected data hides in their tools, and makes the case for adaptive data-protection strategies in modern digital engineering environments.
→ Folder: hidden-in-plain-sight/
cusa26/
│
├── mosa-implementation/
│ └── mosa-implementation.pdf # Slides
│
└── hidden-in-plain-sight/
├── hidden-in-plain-sight.pdf # Slides
└── hidden-in-plain-sight.mdzip # Demo model — open in CATIA Magic / MagicDraw
Heads-up on demo models: every classification or sensitivity marking in any
.mdzipfile is notional — included to demonstrate the gap or capability under discussion, not to represent real classification decisions on any program.
- Web · enola.com · About us · Resource Library
- Company LinkedIn · linkedin.com/company/enolatech
- General inquiries · solutions@enolatech.com
- Neil Patel · CEO · neil.patel@enolatech.com · LinkedIn
- David Fields · CTO · david.fields@enolatech.com · LinkedIn
- Dr. Sarah Rudder · Principal Systems Engineer · sarah.rudder@enolatech.com · LinkedIn
If you sat through one of our talks at CUSA 2026 and want to keep the conversation going, drop us a line. The whole point of Hidden in Plain Sight is that this is a community problem — we'd genuinely like to hear what you're seeing in your own program. And if MOSA, profiles, automation, or AI-assisted MBSE is on your roadmap, we'd love to compare notes.
Materials in this repository are shared for reference and discussion. Slides and demo models are the work of their respective authors; classification and sensitivity markings shown in any artifact are notional and for demonstration only.