WORKING SMARTER | DIGITAL & IT


Above: As the nuclear sector contends with aging plants, workforce turnover, and the need to scale new generation, Nuclearn aims to set a new standard for what AI can do and how it should be done


We founded Nuclearn because we were living through that friction. We believed there had to be a better way and AI, properly applied, offered the answer.


NEI: So this came from your personal experience inside the plants? Jerrold Vincent, CFO & Co-Founder: Absolutely. Brad and I both spent years in nuclear operations. We were on the floor. We wrote the reports, sat through the reviews, got the same procedure kicked back five times because a citation was one character off. It became obvious that most of the work we were


doing wasn’t technical, it was matching patterns. It was compliance logic. That’s where AI shines. We started small: document automation, CAP screening,


repetitive risk assessments. But as we built trust and capability, the demand grew. Now, Nuclearn’s platform supports use cases across maintenance, safety, engineering, and regulatory functions.


NEI: What does the platform actually do – and how is it deployed? Fox: At its core, Nuclearn is a private AI ecosystem that connects natural language processing with plant-specific data, processes, and security models. Our customers use it


to: ● Write and validate procedures ● Analyze safety observations ● Classify work for capital vs. expense ● Perform document-based research across licensing and QA archives


● Assist with outage prep and planning, and more


It’s available as a private, on-premise system or a secure hosted model, built with compliance in mind. Everything is permission-based and auditable. The data stays private, and every customer environment is isolated.


NEI: How does Nuclearn ensure it’s “nuclear- grade” when it comes to AI? Vincent: What we’ve built isn’t a generic AI model, it’s a platform trained on nuclear data, regulatory structures, procedural formats, and real-world use cases. Our AI doesn’t hallucinate because it’s grounded in context. If


you’re pulling up a licensing document or CAP history, the system isn’t guessing, it’s searching verified documents in your environment and surfacing real excerpts. We’ve also built in what we call “human-centered workflows.” That means the AI isn’t replacing the expert; it’s partnering with them. You’re always in control, verifying the output, editing as needed, and reviewing before submission.


NEI: You’ve mentioned the concept of an AI marketplace for nuclear. What does that look like? Fox: That’s where it gets exciting. Right now, every plant is solving its own problems in isolation. A utility in Georgia may have built a brilliant AI-based outage prep tool, but no one in Illinois knows about it. What we’re building is a secure, nuclear-specific AI marketplace where utilities can share, buy, or adopt pre-trained models, prompts, and apps. It’s a way to accelerate innovation across the industry while respecting IP and data privacy. The marketplace also supports community-driven


learning. As more plants use similar tools, we can build in best practices and improve the model quality over time. It’s open innovation, but within a secure, nuclear- compliant framework.


NEI: Let’s talk about your work beyond the US Fox: Right now, most of our deployments are US-based, but that’s changing quickly. We’ve had strong traction in Canada with CNSC-regulated sites, and we’re starting to engage customers in the UK as well. The beauty of our system is that it’s customisable. Even


if the base AI was initially trained on US regulations, our clients can input their own documents – regulatory guides, site manuals, licensing histories – and the AI can search and reference those sources. So even before we’ve fully trained an international model, the system is still delivering value. Vincent: And we’re working on that now. One of our


goals for the next year is to build a more comprehensive international model. That means incorporating UK ONR standards, Canadian licensing terms, and other country- specific guidance into the core AI engine. We’ve already laid the foundation with our architecture. Now it’s about scaling that intelligently.


www.neimagazine.com | August 2025 | 21


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