Author: sondra

Shadow AI: The New Trend Nuclear Cannot Afford

Posted on by sondra

Executive Summary

A new pattern is emerging in enterprise AI adoption: Shadow AI. Employees, frustrated with slow-moving official systems, are turning to consumer-grade AI tools like ChatGPT to get work done. MIT’s State of AI in Business 2025 study reports that over 90% of knowledge workers now use unsanctioned AI for drafting, research, and analysis.

In many industries, this trend raises governance and security concerns. In nuclear, it introduces regulatory, safety, and compliance risks that cannot be tolerated.

This paper examines Shadow AI as an enterprise trend, analyzes why it is incompatible with nuclear operations, and outlines how regulator-ready, domain-specific AI addresses the gap.

1. Shadow AI: A Growing Trend

MIT’s research identifies Shadow AI as one of the fastest-growing dynamics in AI adoption:

  • Broad worker adoption. Employees bypass enterprise systems and use consumer AI tools directly.

  • Enterprise lag. Corporate IT and compliance groups struggle to deploy secure alternatives at the same pace.

  • Risk exposure. Sensitive information is copied into cloud-based tools with little oversight.

In industries like retail or marketing, the risks are financial. In nuclear, they are regulatory and existential.

2. Why Nuclear Cannot Tolerate Shadow AI

Nuclear operations rely on strict compliance regimes that consumer AI tools cannot meet:

  • Part 810 Regulations. Export control prohibits the uncontrolled transfer of nuclear technical data. Shadow AI platforms, typically hosted on global cloud infrastructure, are non-compliant by default.

  • Licensing-Basis Sensitivity. Technical Specifications, FSARs, and design-basis documents cannot be exposed to uncontrolled platforms. Even summaries must be regulator-ready.

  • Audit Requirements. NRC oversight requires every evaluation and document to be traceable and verifiable. Shadow AI outputs are not.

Simply put: Shadow AI creates compliance gaps that nuclear regulators and operators cannot accept.

3. Case Study: Licensing Research

Observed Behavior:
Frustrated by slow search systems, engineers tested consumer AI tools to summarize licensing requirements. The AI produced text that appeared helpful but lacked citations and omitted key references.

Outcome:
Outputs could not be defended in NRC-facing documentation. The practice created risk, not efficiency.

Domain-Specific Alternative:
Nuclearn’s Gamma 2 model retrieves licensing basis documents from secure, on-premise repositories. Outputs include full citations, reasoning steps, and maintain alignment with IV&V. Engineers remain in control, but repetitive search is automated.

Result: regulator-ready documentation, compliant with Part 810, without Shadow AI risk.

4. Shadow AI vs. Secure AI

The Shadow AI trend reflects a workforce reality: employees want faster, more usable tools. Restrictive policies alone will not stop Shadow AI. Without secure alternatives, adoption will continue underground.

The solution is not prohibition, but replacement. Nuclear operators must provide systems that are:

  • As usable as consumer AI. Engineers will only adopt what improves their daily work.

  • As secure as required. On-premise, Part 810 compliant, and regulator-ready.

  • Domain-specific. Trained on nuclear acronyms, licensing structures, and workflows.

5. Implications for Nuclear Operators

  • Shadow AI is not theoretical. It is already happening across industries. Nuclear cannot assume immunity.

  • Regulatory exposure is immediate. Even one instance of sensitive data entered into a consumer AI platform may trigger compliance investigations.

  • Workforce demand must be addressed. Engineers will seek usable AI. If utilities don’t provide compliant systems, Shadow AI will fill the gap.

Conclusion

Shadow AI is the new trend shaping enterprise AI adoption. In nuclear, it is untenable. The compliance, regulatory, and safety demands of the industry mean that consumer-grade AI tools cannot be tolerated inside the plant.

The solution is not banning AI use — it is providing secure, domain-specific alternatives that meet the same standard as the industry itself: safety, compliance, and reviewability.

Nuclearn demonstrates that when AI is designed for nuclear — Part 810 compliant, regulator-ready, and embedded in real workflows — it delivers measurable value while eliminating the risks of Shadow AI.

From Pilots to Production: Why Nuclear AI Must Cross the Divide

Posted on by sondra

Executive Summary

Enterprise adoption of generative AI is widespread, but measurable impact remains rare. The MIT State of AI in Business 2025 report found that only 5% of enterprise AI pilots advance into production. The remainder stall due to integration challenges, lack of compliance alignment, and outputs that do not withstand scrutiny.

In nuclear energy, this failure rate cannot be tolerated. Pilots that never scale waste engineering hours, introduce compliance risk, and erode workforce trust. This paper examines why most AI efforts fail to transition, why nuclear’s regulatory environment magnifies the risk, and what design principles are required for AI systems to succeed in production.

1. The Pilot Trap

Across industries, the “pilot trap” is common. Demos and small-scale trials show potential but collapse when scaled. Three recurring factors are identified in MIT’s research:

  1. Workflow Misalignment – Pilots address isolated tasks but fail when integrated into enterprise systems.

  2. Compliance Blind Spots – Outputs lack the transparency needed for audit or regulatory review.

  3. Cultural Resistance – After repeated failures, workforces lose trust in AI initiatives.

For most industries, these failures represent opportunity costs. In nuclear, the consequences are higher. Every pilot requires engineering time, often from senior staff. If the pilot fails, scarce expertise has been diverted from safety and operational priorities.

2. Why Nuclear Is Different

Nuclear operations impose requirements that generic AI tools rarely meet:

  • Independent Verification and Validation (IV&V): All calculations, evaluations, and analyses must be reviewable. Outputs that cannot be traced to source data are unusable.

  • Part 810 Compliance: U.S. export control regulations prohibit uncontrolled data transfer. Cloud-hosted consumer AI platforms cannot meet this requirement.

  • Licensing Basis Alignment: Documentation associated with plant licensing must withstand regulatory audit. Outputs that lack defensibility introduce unacceptable risk.

These conditions mean that nuclear cannot rely on general-purpose AI. Tools must be designed specifically for regulated, documentation-heavy workflows.

3. Case Study: Condition Report Screening

Nuclear plants generate thousands of Condition Reports annually. Each requires screening for safety significance, categorization, and assignment. Historically, this workload demands dedicated teams of experienced staff.

Pilot attempts with generic AI:

  • Demonstrated short-term gains in categorization speed.

  • Failed to provide traceable reasoning or regulatory-suitable documentation.

  • Stalled at the pilot stage due to lack of reviewability.

Production deployment with nuclear-specific AI:

  • Automated initial screening with embedded reasoning steps and citations.

  • Retained IV&V by keeping engineers in the review loop.

  • Scaled to full fleet use, saving tens of thousands of engineering hours annually.

This example illustrates the critical distinction: pilots demonstrate potential; production requires compliance-ready outputs.

4. Case Study: 50.59 Evaluations

The 50.59 process determines whether plant modifications require NRC approval. Evaluations typically require 8–40 hours of engineering time and extensive document research.

Pilot attempts with generic AI:

  • Produced draft summaries of licensing documents.

  • Lacked sufficient traceability for NRC acceptance.

  • Failed to progress beyond trial use.

Production deployment with nuclear-specific AI:

  • Retrieved relevant licensing basis documents with citations.

  • Assembled draft evaluations in ~30 minutes.

  • Enabled engineers to complete reviews in ~2 hours, maintaining full compliance.

The ability to produce regulator-ready outputs was the determining factor in moving from pilot to fleet deployment.

5. Lessons from MIT Applied to Nuclear

MIT’s research identifies three conditions for bridging the gap between pilots and production:

  1. Domain Specificity: Tools must be trained on industry-specific data sets.

  2. Workflow Integration: Systems must embed within existing processes rather than operate in isolation.

  3. Adaptive Learning: AI must improve with use and align with regulatory context.

Applied to nuclear, these principles translate to:

  • Training models on NRC filings, license renewals, and utility documents.

  • Embedding tools into CAP, 50.59, and outage workflows.

  • Designing outputs for traceability, citation, and regulatory review.

Without these conditions, AI pilots in nuclear will remain demonstrations with no lasting impact.

6. Implications for Nuclear Operators

The findings have clear implications:

  • Evaluate vendors beyond demos. Demand evidence of regulator-ready outputs, not just functional prototypes.

  • Prioritize compliance from the start. Systems must be Part 810 compliant and built for IV&V.

  • Focus on critical workflows. Target documentation-heavy processes where measurable impact can be achieved without compromising safety.

  • Guard against cultural fatigue. Each failed pilot increases resistance. Operators should commit only to systems designed for production.

Conclusion

The majority of enterprise AI pilots fail to transition into production. In nuclear, this failure rate is not sustainable. Documentation is safety-critical, compliance is non-negotiable, and workforce trust is essential.

To bridge the gap from pilot to production, AI systems must be domain-specific, workflow-integrated, and regulator-ready. Evidence from early deployments shows that when these conditions are met, nuclear plants can save thousands of engineering hours annually while maintaining safety and compliance.

The lesson is clear: nuclear must move beyond pilots. Production-ready AI, designed for nuclear, is not optional — it is required.

The GenAI Divide — Why Generic AI Fails in Nuclear

Posted on by sondra

Introduction

Across industries, generative AI is being tested in pilots, proof-of-concepts, and trials. The promise is simple: automate routine work, generate documentation faster, and let knowledge workers focus on higher-value tasks.

But the data tell a different story. In its State of AI in Business 2025 report, MIT found that 95% of enterprise GenAI pilots fail to deliver measurable value. Most never move beyond a demonstration. They stall because they don’t integrate into workflows, they forget context, or they produce outputs that can’t be trusted in regulated environments.

For nuclear, this failure rate isn’t just disappointing — it’s unacceptable. Documentation in nuclear isn’t optional; it is the backbone of safety, compliance, and regulatory oversight. If an AI tool cannot produce outputs that are traceable, reviewable, and regulator-ready, it has no place inside the plant.

This is the GenAI Divide. Most industries are struggling to cross it. Nuclear requires a different approach.

What MIT Found

MIT researchers analyzed more than 300 AI initiatives and interviewed senior leaders across industries. Their conclusions highlight why adoption is high but impact is low:

  • High pilot activity, low production: More than 80% of organizations have tested tools like ChatGPT or Copilot. Fewer than 5% of custom AI solutions made it to production.

  • Generic adoption, limited disruption: Consumer tools help with quick drafting, but enterprise-grade deployments stall.

  • The learning gap: Most tools don’t retain context, adapt to workflows, or improve over time. This brittleness means they can’t handle complex processes.

In short, pilots succeed at showing potential. They fail at delivering operational transformation.

Why Nuclear Can’t Afford the Divide

In many industries, failed pilots mean lost time or missed efficiency. In nuclear, they can undermine safety and compliance.

  1. Documentation is not peripheral.
    Every Condition Report, Corrective Action Program entry, or 50.59 evaluation is required by regulation. These aren’t internal notes; they are part of the permanent regulatory record.

  2. Traceability is essential.
    Every calculation, every engineering judgment, every modification review must be linked back to source material. If outputs cannot be cited and verified, they cannot be used.

  3. Workforce turnover magnifies the need.
    With a quarter of the nuclear workforce set to retire within five years, plants need tools that help new engineers become productive quickly. AI that generates unreviewable or inaccurate documentation wastes scarce expertise instead of preserving it.

The conclusion is clear: nuclear cannot tolerate the 95% failure rate seen in other industries. AI must meet the same standards as the industry itself — safety, transparency, and compliance.

Nuclearn’s Approach

Nuclearn was founded by nuclear professionals who saw these challenges firsthand at Palo Verde. Our approach is fundamentally different from generic AI deployments:

  • Nuclear-specific data sets: Our Gamma 2 model is trained on NRC filings, license renewals, technical specifications, and utility-provided documentation. It understands the acronyms, licensing basis requirements, and processes unique to nuclear.

  • Reviewable outputs: Every output includes citations back to source material and exposes the AI’s reasoning steps. Engineers can perform independent verification and validation (IV&V) just as they would for junior engineer work.

  • Workflow integration: Nuclearn doesn’t sit on the side as a chatbot. It is embedded into CAP screening, 50.59 evaluations, outage planning, and licensing research — the real processes that consume plant resources.

  • On-premise, secure deployment: Data never leaves plant control. Our systems are Part 810 compliant and designed to meet U.S. export control regulations.

Case Example: CAP Screening

At a typical reactor, thousands of Condition Reports are filed every year. By regulation, every CR must be screened and categorized: is it adverse to quality? Does it require corrective action? Which group is responsible?

Historically, this requires full-time teams of experienced staff. It is repetitive, manual, and essential.

With Nuclearn:

  • AI automates the screening and categorization process.

  • Experienced engineers remain in the loop, reviewing and verifying.

  • Plants save tens of thousands of hours annually, freeing highly skilled staff for higher-value work.

The process is faster and more consistent — but still compliant with regulatory expectations for reviewability.

Case Example: 50.59 Evaluations

The 50.59 process requires engineers to determine whether a proposed modification changes the plant’s licensing basis and whether NRC notification is required. It is one of the most documentation-intensive processes in the industry.

Traditionally:

  • Each evaluation takes between 8 and 40 hours.

  • Engineers must search thousands of pages of licensing documents.

  • Work often involves multiple layers of review and verification.

With Nuclearn’s agent-based workflows:

  • Relevant licensing basis documents are retrieved automatically.

  • Key requirements and citations are assembled.

  • Engineers receive a draft evaluation in about 30 minutes.

The final review still takes human expertise, but the process now takes ~2 hours instead of several days. Outputs remain fully traceable, with citations back to source material for regulatory confidence.

Aligning with Industry Findings

Where most AI pilots fail, Nuclearn succeeds because our approach directly addresses the barriers highlighted by the industry reports:

  • Process-specific customization: We don’t try to solve everything. We focus on CAP, 50.59, outage planning, and licensing.

  • Workflow integration: Our tools are embedded in actual plant processes, not running in isolation.

  • Learning and adaptation: Our models are trained on nuclear-specific data and tuned for each utility.

  • Compliance and traceability: Outputs are regulator-ready, built for IV&V.

This is exactly what MIT identifies as the path across the GenAI Divide: adaptive, embedded, domain-specific systems

Closing Thought

The MIT study is a warning. Most enterprises will spend money and time on AI tools that never scale. They will produce demos, not durable solutions.

Nuclear does not have that luxury. Our industry requires AI that can withstand NRC oversight, peer review, and decades of operational scrutiny. That is what Nuclearn delivers: solutions that are reviewable, verifiable, and regulator-ready.

If AI can meet nuclear’s bar, it can meet any bar.

Nuclearn Secures $10.5 Million Series A to Power the Next Era of AI for Nuclear Operations

Posted on by sondra

The nuclear industry stands at a critical juncture. As global demand for clean, always-on electricity surges, nuclear energy has reemerged as an essential pillar in the world’s energy mix. But the industry also faces immense challenges: workforce retirements, stringent regulatory requirements, and the need to modernize decades-old processes without ever compromising safety.

Today, Nuclearn is proud to announce the closing of our $10.5 million Series A funding round, with participation from leading energy and impact-focused investors.  This milestone allows us to accelerate our mission of bringing purpose-built AI to one of the world’s most complex, safety-critical industries.

A Platform Built by Nuclear Professionals, for Nuclear Professionals
At its core, Nuclearn was born from lived experience inside the power plants we now serve. Co-Founders Bradley Fox and Jerrold Vincent first met at Palo Verde, the largest nuclear generating station in the U.S. As early members of the industry’s first data science team, they quickly realized that the nuclear sector was ripe for AI-driven transformation.

“One of the first problems we recognized right away was corrective action program screening,” recalls Fox. “Every issue identified in a plant must be evaluated for safety, assigned actions, and tracked. It’s critical work, but also highly repetitive and documentation-heavy. We knew AI could help make this process safer and faster.”

This insight became CAP AI, the company’s first product. It automated weeks of manual screening into minutes while retaining full traceability—an essential requirement in nuclear operations. From there, Nuclearn expanded its portfolio to include solutions for outage planning, regulatory documentation, supply chain forecasting, and more.

Meeting the Industry Where It Stands
Unlike broad AI platforms, Nuclearn is intentionally vertical. Our technology is trained on millions of nuclear-specific documents, diagrams, and regulatory frameworks. As Vincent puts it:

“Our platform doesn’t just process nuclear terminology—it understands the operational context, regulatory implications, and safety considerations behind every decision.”

This industry specificity matters. Nuclear plants cannot adopt generic AI solutions that fail to meet compliance standards or recognize the nuances of NRC and DOE Part 810 export control laws. That’s why Nuclearn operates securely—whether deployed on-premise, hosted, or in government cloud environments—and ensures that every customer model respects the boundaries of plant-specific data.

Tackling the Workforce Challenge
The nuclear sector is grappling with a profound demographic shift: nearly one in four nuclear workers is expected to retire in the coming years. At the same time, demand for nuclear is increasing—both in extending the lifespan of existing reactors and in constructing new advanced designs.

“Nuclear plants were built decades ago, and many of the original workers are now retiring,” explains Vincent. “AI becomes a bridge for knowledge transfer. Everything in nuclear is documented, but accessing and applying that knowledge efficiently is the challenge we’re solving.”

By capturing tribal knowledge in algorithms and enabling new engineers to work more efficiently, Nuclearn ensures the industry’s high safety standards endure across generations.

From Early Pioneers to Industry Leaders
What sets Nuclearn apart is not just technology, but timing. The company launched in early 2021, before the recent wave of mainstream AI hype.

“Because nuclear is so documentation- and process-heavy, we knew early on that AI would be transformative,” says Fox. “Back then, natural language processing was hard. We were experimenting with transformer networks before they were mainstream. That experience gave us a head start.”

From its bootstrapped beginnings to winning prestigious industry awards, to attracting customers across more than 65 reactors worldwide, Nuclearn has grown steadily and deliberately.

What Series A Funding Unlocks
The $10.5 million investment is more than capital—it’s fuel for scale. With this funding, Nuclearn will:
Advance product development: expanding our suite of AI tools for condition report analysis, design modification reviews, and engineering document generation.

Grow our workforce: hiring engineers, data scientists, and nuclear professionals to deepen our expertise.
Expand market reach: supporting more utilities worldwide as demand for carbon-free baseload power accelerates.
This round ensures that our solutions, already trusted by dozens of reactors, continue to grow in capability and impact.

Transparent, Trustworthy AI
Trust is the foundation of everything in nuclear operations. Nuclearn’s software doesn’t aim to replace engineers—it empowers them. Our platform provides confidence intervals, audit trails, and random human-in-the-loop reviews to ensure AI is never a black box. Engineers become reviewers and decision-makers rather than manual processors of endless documentation.

“It’s about turning nuclear professionals from writers into editors,” explains Fox. “AI handles the boilerplate and the repetitive work. People focus on the high-value engineering decisions.”

Looking Ahead
The future of energy requires resilience, reliability, and zero carbon. Nuclear power provides all three—but only if the industry evolves to meet modern challenges. With Series A funding secured, Nuclearn is positioned to be the trusted partner in that transformation.

We are proud to be nuclear professionals building AI for nuclear professionals. Our journey began inside the plants we now serve, and with the support of our customers, partners, and community, we are scaling that vision globally.
For more on our Series A and the future of nuclear AI, visit www.nuclearn.ai.

The Top 5 Misconceptions About AI in Nuclear

Posted on by sondra

Artificial Intelligence is gaining momentum across every sector of the energy industry, and nuclear is no exception. Yet, despite real-world deployments and growing acceptance among engineers and regulators, outdated assumptions about AI persist—slowing adoption, blocking innovation, and ultimately costing facilities time, money, and human capital.

At Nuclearn, we’ve seen firsthand how AI can help nuclear teams solve critical challenges—from accelerating 50.59 evaluations to digitizing field work execution. But to unlock its full potential, we must first confront the myths.

This post breaks down the five most persistent misconceptions we encounter when discussing AI with utilities, engineers, regulators, and the public—and why each one is due for retirement.

1. AI Replaces People

The most common misconception—and often the most emotional—is that AI is here to replace engineers, planners, or operations staff. This could not be further from the truth.

Every Nuclearn solution is built on a “human-in-the-loop” foundation. Our AI is designed to assist professionals by automating repetitive or time-consuming tasks, allowing them to spend more time applying their critical thinking, experience, and judgment.

For example, Engineering AI can scan through thousands of condition reports (CRs) in seconds to surface relevant issues. But the decision about whether a 50.59 threshold has been met? That still belongs to the engineer.

“We don’t take engineers out of the loop—we give them better tools inside it.”

2. Regulators Won’t Allow AI

It’s true that nuclear is one of the most heavily regulated industries on Earth. And that’s a good thing. But assuming that regulation and AI are incompatible misses an important shift happening in the industry.

Regulators are not opposing AI—they are increasingly participating in conversations about how it can support safer, more auditable operations.

Nuclearn’s AI systems are designed to enhance compliance. Every recommendation is traceable. Every interaction can be logged. Outputs are consistent, reviewable, and auditable.

In fact, our human-in-the-loop workflows often provide more accountability than existing paper-driven systems.

We work within the bounds of 10 CFR 50.59, 50.72, Appendix B, and Part 810. AI isn’t a loophole—it’s a tool to execute regulatory responsibilities with greater efficiency and rigor.

3. Generic AI Tools Work Just Fine

You may have heard that ChatGPT or a plug-in LLM can “handle nuclear documentation.” We’ve even heard teams ask if they could simply drop their procedures into a public chatbot and get answers back.

Let’s be clear: generic AI tools are not built for nuclear.

They do not understand:

  • Plant-specific licensing basis and design basis rules
  • Condition reporting systems
  • Corrective action protocols
  • Engineering workflows governed by QA and safety compliance

By contrast, Nuclearn’s tools are trained with nuclear-specific language, rulesets, and domain experience. Our platforms know the difference between a maintenance rule failure and a licensing threshold. That context matters.

“You wouldn’t use a kitchen timer to run a reactor. Don’t use generic AI to run your plant.”

4. AI Can’t Be Deployed Securely

Security is not an afterthought. It’s a starting point.

All Nuclearn solutions are deployable on-premise, behind your firewall, and air-gapped if necessary. We are fully Part 810–compliant, meaning none of your data is ever exposed to public models or cloud APIs.

In secure deployments, we:

  • Run local inference on utility-controlled infrastructure
  • Support role-based access and authentication
  • Provide complete audit trails for every AI decision
  • Operate within closed systems with full IT visibility

Our customers run these systems in the most critical environments in the U.S. nuclear fleet. And they trust our infrastructure to meet their cyber and export control needs without compromise.

5. AI is Only for Advanced Utilities

Some leaders assume their plant is too small, too traditional, or too legacy to benefit from AI. That’s another myth.

In reality, the tools that make the biggest difference are often the ones that address universally painful problems—like documentation backlog, mod review time, or CR screening.

That’s why we’ve built our solutions to be modular, phased, and adaptable. Whether you’re a one-unit site with a lean staff or a multi-site operator with enterprise systems, AI can work for you.

In fact, some of our most successful deployments have been at facilities that viewed digital transformation as a necessity—not a luxury.

Conclusion: Moving Beyond Myths

The nuclear industry is evolving. Energy demand is rising. The workforce is retiring. And the expectations on plant performance, compliance, and efficiency have never been higher.

AI isn’t a futuristic concept. It’s a present-day enabler. But only if we let go of the myths that have held us back.

At Nuclearn, we’re ready to work with you to explore what secure, explainable, human-centered AI looks like in your plant. Let’s move beyond misconceptions—and get to work.

Confident Engineering Starts Here: Inside Nuclearn’s Agentic 50.59 Workflows

Posted on by sondra

Few areas in nuclear engineering are as foundational—or as complex—as 10 CFR 50.59. Whether evaluating a plant modification, equipment upgrade, or digital system implementation, the question remains the same: Does this change require prior NRC approval? For decades, answering that question has required long hours of research, interpretation, and justification—often across fragmented guidance, buried precedent, and aging internal documentation.

With the launch of Agentic 50.59 Workflows, Nuclearn is changing that. Part of our Engineering AI solution suite, these new 50.59 capabilities bring nuclear-specific AI directly into the licensing workflow—helping engineers and licensing professionals move faster, with more clarity, and without compromising traceability or safety.

“Every minute spent searching through fragmented documentation is a minute not spent on what matters most—safe, reliable nuclear operations,” said Brad Fox, CEO and Co-Founder of Nuclearn.

“Our 50.59 solution transforms weeks of manual research into minutes of AI-powered analysis, giving engineers the confidence to move forward with complete regulatory clarity. This isn’t just about efficiency—it’s about empowering the nuclear workforce to focus on innovation while maintaining the highest safety standards.”

The Problem: Complex, Manual, and Costly

The traditional 50.59 process relies heavily on institutional memory, document repositories, and scattered evaluations. Teams tasked with screening a proposed change must often:

  • Manually search through decades of precedent
  • Cross-check NRC guidance, internal evaluations, and site-specific bases
  • Align interpretations that vary between individuals or sites
  • Spend valuable engineering time on documentation—not decision-making

This approach isn’t just inefficient. It slows innovation, increases the risk of misinterpretation, and puts undue burden on engineers who should be focused on solving real-world problems.

The Solution: Agentic 50.59 Workflows

With Agentic 50.59, Nuclearn introduces a transformative way to evaluate whether a proposed change impacts the facility’s licensing basis. This isn’t a form fill or a static checklist—it’s a dynamic, interactive workflow powered by AI built for nuclear.

Here’s how it works:

  • 🧠 Semantic Search: Quickly surface relevant past 50.59 evaluations, NRC precedent, and guidance based on meaning—not just keywords.
  • 🔗 Cited Authority: Automatically reference regulatory sources, site-specific licensing bases, and industry standards to support decision-making.
  • 💬 Conversational Interface: Ask detailed questions and receive context-aware responses from an AI trained on real nuclear data.
  • 📁 Traceability: Capture every step of the evaluation process, including justification, sourcing, and reasoning—ready for audit or peer review.

The result? A workflow that reduces hours of research into minutes of meaningful analysis, and one that scales with your team—whether you’re evaluating a single change or hundreds during an outage or upgrade window.

More Than a Feature—It’s a Foundation

Agentic 50.59 is more than just a search tool or a digital form. It’s a new foundation for how engineering teams can tackle complex licensing workflows using purpose-built AI.

It serves as the cornerstone of Engineering AI, a larger solution suite designed to support engineering, QA, and licensing teams with smarter tools for repetitive, high-stakes tasks.

Other key products in the Engineering AI suite include:

📋 Reportability Screener

Quickly pre-screen CRs and proposed changes against NRC reporting criteria. Offers rapid insights into whether a condition may require reporting—reducing uncertainty and speeding up response.

🔎 CR Smart Search

AI-driven search that allows users to explore past condition reports based on similarity, outcome, and resolution—ideal for CAP, QA, and engineering teams trying to learn from precedent.

🧾 Document Comparison Tool

Compare procedures, licensing documents, design packages, or evaluations side-by-side. Highlights structural and content differences for easier review, traceability, and QA.

Together, these solutions create a connected workflow environment that supports end-to-end engineering decisions—especially in high-impact areas like plant modifications, configuration control, and licensing justification.

Built for What’s Next

Nuclearn built Agentic 50.59 in partnership with top utilities and engineering firms—teams who know what it means to screen hundreds of modifications in a matter of weeks during an outage or digital upgrade.

They asked for a solution that could:

  • Reduce the manual burden of research
  • Help newer engineers ramp up faster
  • Preserve institutional knowledge
  • Deliver fast, consistent results that hold up to regulatory scrutiny

And that’s exactly what Engineering AI delivers.

Who Benefits?

The release of this product comes at a critical time for the industry. Many plants are navigating:

  • License renewal and extension projects
  • Power uprates and equipment modifications
  • Aging management strategies
  • Fleet-wide standardization of licensing practices

In all of these efforts, 50.59 plays a defining role. Nuclearn’s solution doesn’t remove engineering judgment—it strengthens it with data, precedent, and intelligent workflows that match the pace and complexity of the industry today.

Smarter Doesn’t Mean Riskier

At Nuclearn, we know that in nuclear, speed means nothing without safety. That’s why the Agentic 50.59 workflows are built with Part 810 compliance, on-premise deployment options, and full traceability baked in from the start. You get efficiency, yes—but never at the cost of oversight, documentation, or regulatory alignment.

See It in Action

The Agentic 50.59 capability is now available as part of the Nuclearn Platform and can be demoed live with your engineering or licensing team.

Whether you’re a utility preparing for your next project window or an EPC firm supporting client upgrades, this is your chance to see how AI can meaningfully improve your engineering outcomes—without changing your standards.

📅 Schedule a live demo
🌐 Explore Engineering AI at: www.nuclearn.ai

Final Thought

Modernizing 50.59 isn’t just a nice-to-have—it’s essential to nuclear innovation, safety, and performance. With Agentic 50.59 Workflows, Nuclearn is giving engineers what they’ve long needed: a faster, smarter, and more transparent path to confident licensing decisions.

Because real modernization doesn’t start with a new form. It starts here—with the power to ask better questions, find better answers, and act with clarity.

Trusted by Nuclear. Built for Engineers. Powered by Nuclearn.

Nuclearn Joins Texas Nuclear Alliance to Help Power the Future of Clean Energy

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Nuclearn was proudly welcomed as a founding member of the Texas Nuclear Alliance (TNA)—a milestone that reflects both our commitment to the future of nuclear energy and the importance of Texas in leading that future.

As a company built by nuclear engineers for nuclear engineers, Nuclearn has long recognized that modernizing nuclear operations requires more than just better hardware—it requires smarter software. That’s where our AI-powered, nuclear-specific solutions come in, and that’s why joining the Texas Nuclear Alliance is so meaningful to us.

“In our rapidly growing AI economy, Nuclearn is meeting the moment by modernizing nuclear operations and bringing new levels of efficiency to safe, reliable energy,” said TNA President Reed Clay. “TNA is proud to partner with the bright minds at Nuclearn and looks forward to working together to unlock the full potential of nuclear energy right here in Texas.”

Why This Partnership Matters

Formed in the aftermath of Winter Storm Uri in 2022, the Texas Nuclear Alliance is the only organization in the state dedicated solely to the advancement of nuclear technology. TNA’s mission is bold and clear: to make Texas the Nuclear Capital of the World.

That’s a mission we fully align with.

Texas has long been a leader in energy, with a strong track record in oil, gas, wind, and solar. But as the demand for secure, reliable, low-carbon energy accelerates, nuclear is increasingly recognized as the only always-on clean energy source that can scale fast enough to meet the moment.

Nuclearn’s decision to join TNA as a founding member is a reflection of our commitment to ensuring nuclear energy remains at the core of Texas’s energy strategy—and of the global clean energy transition.

“Texas’s leadership in energy innovation, combined with Nuclearn’s nuclear AI expertise, further reinforces that the future of nuclear energy is secure, efficient, and scalable,” said Brad Fox, CEO and Co-Founder of Nuclearn.

What Nuclearn Brings to the Table

Our mission has always been grounded in one simple idea: Nuclear deserves better tools.

Nuclearn creates AI-powered software designed specifically for nuclear operations—solutions built by engineers who understand the complexity of the field. Our platform is trusted by more than 60 nuclear reactors worldwide, helping teams enhance safety, streamline operations, and reduce time spent on repetitive, manual tasks.

Our software capabilities include:

  • Outage planning and scheduling automation

  • AI-assisted documentation and reporting

  • CAP and QA process acceleration

  • On-premise deployment for maximum security and compliance

  • Pre-trained, nuclear-specific large language models (LLMs)

“We see incredible opportunity in aligning with the Texas Nuclear Alliance to accelerate next-generation nuclear deployment,” said Jerrold Vincent, CFO and Co-Founder of Nuclearn. “We’re eager to support the mission of making Texas the global leader in clean, reliable nuclear energy.”

Shared Vision, Shared Impact

By joining the Texas Nuclear Alliance, we’re adding our voice—and our capabilities—to a powerful coalition that includes policymakers, industry leaders, utilities, reactor developers, and community stakeholders. Together, we are focused on four key goals:

  1. Accelerating Deployment: From advanced reactors to SMRs, our tools can help streamline regulatory approvals, documentation, and training—reducing friction in the deployment process.

  2. Workforce Modernization: AI isn’t replacing people—it’s enabling them to focus on high-value work. We’re building solutions that support knowledge transfer, training, and field productivity.

  3. Operational Excellence: From CAP and QA to maintenance and planning, our platform enables faster, safer decision-making at every level of plant operations.

  4. Trust and Transparency: Our explainable AI models are designed to meet nuclear’s high standards for compliance and traceability.

With this founding membership, we’re not just talking about innovation—we’re actively investing in it. Together with the TNA, we’re committed to creating real momentum for nuclear’s growth in Texas and beyond.

Texas as a Launchpad for the Future

Texas is uniquely positioned to lead the next generation of nuclear innovation. With its expansive energy infrastructure, technology-forward business climate, and a growing need for dispatchable clean energy, it provides the perfect proving ground for what’s possible when policy, technology, and purpose align.

The Texas Nuclear Alliance provides a platform to accelerate this alignment. By bringing industry and innovation together, TNA is setting the stage for long-term, scalable progress.

For Nuclearn, being part of this founding group means we’re not only helping shape the future of nuclear in Texas—we’re also gaining valuable insights and partnerships that will inform our broader mission across the country and around the world.

Looking Ahead

We’re honored to work alongside TNA and its members to advance nuclear energy through AI, innovation, and purpose-driven collaboration.

As a company, we’ll continue building software that enables nuclear teams to do their best work—whether that’s preparing for an outage, training a new generation of operators, or planning the next phase of reactor development.

The opportunity is enormous. The need is urgent. And the time is now.

We’re ready.

Gamma2: The Dawn of AI-Powered Nuclear Innovation

Posted on by sondra

The Nuclear Industry’s Critical Juncture

The nuclear energy industry stands at a pivotal moment. While it remains a cornerstone of clean, reliable power, the industry faces mounting pressure from rising O&M costs, workforce attrition, and complex regulatory demands. At the same time, global climate goals are accelerating the demand for sustainable, carbon-free energy. The challenge? Keep reactors online, safe, and cost-effective for the long haul.

Enter Gamma2 from Nuclearn—a breakthrough that’s redefining what’s possible in nuclear operations.

Meet Gamma2: The World’s Largest AI Model for Nuclear Power

Gamma2 is a 32-billion-parameter AI large language model purpose-built for nuclear power. Created by Nuclearn, the first AI solutions provider dedicated to the nuclear sector, Gamma2 is trained on more than 4 terabytes of industry-specific data from the NRC, INPO, IAEA, and utilities. Unlike general-purpose AI models, Gamma2 was developed by nuclear professionals who understand the demands, context, and precision required in nuclear operations.

This isn’t just a smart system. Gamma2 is a digital nuclear expert—one that speaks the industry’s language and delivers answers with unmatched speed and accuracy.

Native to Nuclear, Ready on Day One

Gamma2’s real power lies in its specificity. Generic AI often misinterprets critical terminology and regulatory nuance. Gamma2 doesn’t. It understands acronyms, licensing language, technical procedures, and safety standards the way a seasoned operator does.

As Jerrold Vincent, Co-Founder and CFO of Nuclearn, explains: “Every dollar we save on operations keeps reactors online and carbon out of the atmosphere. With Gamma2, the nuclear industry finally has an AI brain fluent in its language.”

Real-World Results, Immediate Impact

Gamma2 is already delivering results across 23 reactors in the U.S., Canada, and the U.K. In one standout case, it resolved a two-year auxiliary feedwater pump issue at a major Texas plant in under four hours. Gamma2 synthesized two decades of pump data and pinpointed the fix—before the next shift clocked in.

Users rely on Gamma2 for FSAR lookups, safety evaluations, root cause analyses, and procedure development. Tasks that used to take weeks now take minutes. On average, troubleshooting and documentation development time is down by up to 98%.

Phil Zeringue, Chief Revenue Officer at Nuclearn, captures the scale of this shift: “This feels like the nuclear industry’s iPhone moment. While others debated what AI could do, we built what it can do for nuclear energy.”

Secure, Compliant, and Scalable

Gamma2 supports secure deployments across 10 CFR 810-compliant environments, including air-gapped, on-premise, and SaaS configurations. Real-time QA tools, full audit trails, and traceable outputs ensure accountability and compliance every step of the way.

A Strategic Advantage for the Nuclear Industry

Gamma2 is not just a tool—it’s a strategic enabler. By reducing O&M costs by 30–50%, it directly supports the economic sustainability of existing nuclear assets. It also bridges the workforce gap, empowering teams with institutional knowledge and ensuring consistent, high-quality decision-making even as experienced professionals retire.

Available Now

Gamma2 is live and available through the Nuclearn platform. Utilities and operators ready to modernize their operations and improve their bottom line can request a demo today at www.nuclearn.ai.

Gamma2 isn’t a vision of the future. It’s here, delivering real impact for nuclear operators today. Built by Nuclearn for the nuclear industry, it’s changing how teams solve problems, share knowledge, and sustain reactor performance for decades to come.

For an industry built on precision, Gamma2 is the breakthrough that brings power, speed, and certainty to every decision.

Beyond the Buzz: How GenAI Is Delivering Real Results in Nuclear and Utility Operations

Posted on by sondra

The rise of Generative AI (GenAI) has changed how the world thinks about automation—but in nuclear and utility operations, the conversation has already shifted to what AI is doing in the field. Across operations, planning, safety, and engineering, GenAI is now part of how real work gets done.

At Nuclearn, we don’t build for hype. We build for the realities of secure, highly regulated environments. Our AI platform is designed specifically for nuclear and utility teams and is deployed in the field, supporting work at over 48 facilities in the U.S., Canada, and the U.K.

In this post, we’re breaking down what GenAI is currently doing on the ground, where it’s having a measurable impact, and why success depends on aligning AI with real operational workflows, not theoretical possibilities.

From Concept to Capability

For many organizations, the GenAI conversation started with curiosity. Could it make processes more efficient? Could it help with documentation? Could it reduce repetitive manual work?

Today, those questions are being answered by field teams using Nuclearn.

The most successful sites didn’t ask AI to transform their world overnight. They started with well-defined use cases, aligned their internal teams, and focused on delivering outcomes with clear value and traceability.

Here are the areas where GenAI is already embedded in day-to-day nuclear work.

Current Use Cases for GenAI in Nuclear and Utility Operations

✅ FSAR and Tech Spec Research

Engineers often spend time manually searching across large, version-controlled documents to find design references or validate assumptions. With GenAI, they can enter a natural-language question and receive a detailed response with specific citations from source materials.

Validation Path:

  • Every output is sourced and linked to exact regulatory documentation.

  • All citation paths are transparent for engineering review.

  • Sites control the source data.

✅ Procedure Cross-Referencing

Procedure writers and reviewers use GenAI to identify where one change might impact other connected procedures or protocols. This is especially useful when dealing with cascading effects across systems or plant conditions.

Validation Path:

  • AI flags linked procedures but does not finalize changes.

  • Suggested impacts are provided with excerpts from each document for human review.

  • Peer reviewers use the tool as a checklist enhancer, not a replacement.

✅ Safety Observation Summarization

Frontline staff and supervisors use Nuclearn’s AI to turn field notes and observations into structured summaries. These summaries are then reviewed and integrated into corrective action programs.

Validation Path:

  • The platform does not “decide” root causes—rather, it surfaces consistent language based on previous entries.

  • Users are prompted to review and confirm summaries before submission.

  • All content generated can be traced to original user input.

Why These Use Cases Succeed

One of the reasons Nuclearn’s AI delivers value where others fall short is because our approach is focused on augmentation, not automation. AI isn’t replacing engineers or operators—it’s giving them a faster, more informed starting point.

What makes that possible?

  • Security-First Design: Deployed on-premise or in government-approved environments.

  • Explainable Outputs: All responses are documented with reasoning and source path.

  • Persona-Based Logic: AI behavior adjusts based on whether the user is a procedure writer, planner, or safety engineer.

  • Custom Knowledge Bases: Data belongs to the site, not to a public model or shared server.

We’ve found that success with GenAI depends on three things:

  1. Contextual accuracy

  2. Security integration

  3. Staff involvement in adoption

What Field Teams Are Saying

Across facilities, we’ve heard similar feedback:

  • Engineers want faster access to structured references, not more data dumps.

  • Planners want a second set of eyes on tagging logic, not a black box.

  • Safety teams want cleaner summaries, not templated outputs.

When GenAI is introduced with those needs in mind, it’s quickly seen not as a threat, but as a support system.

Avoiding Common Pitfalls

Not all AI models are ready for nuclear. Some platforms are built for commercial use or are too generalized to handle regulatory nuances. Here are a few flags that suggest a solution may not be fit for this environment:

  • No citation support: If it doesn’t show its sources, it can’t be trusted.

  • One-size-fits-all logic: Nuclear doesn’t operate like finance or retail, and neither should its AI.

  • Cloud-only deployment: Sites need control over data—public cloud models may not meet that need.

  • No understanding of standards: If it doesn’t align with NQA-1 or 10 CFR principles, it shouldn’t be in your stack.

At Nuclearn, every deployment is supported by onboarding, site-specific configuration, and training aligned to real workflows.

Final Thoughts

GenAI has moved beyond buzzwords in the nuclear sector. It’s in the field, in the workflow, and in the hands of professionals who are validating its value daily.

By focusing on purpose-built design, explainability, and secure deployment, Nuclearn is showing what it looks like to implement GenAI the right way—without shortcuts, compromises, or gimmicks.

This is not future-state talk. This is now.
And it’s only just beginning.

Why New Entrants Validate What We’ve Already Built

Posted on by sondra

The energy industry is experiencing a long-overdue shift—one where AI is no longer a novelty but a necessity. And in the nuclear and utility sectors, we’re beginning to see something we welcome: more vendors entering the space.

It’s a positive sign. The growing interest from startups, enterprise AI labs, and newly formed nuclear-focused technology companies is a clear signal that the market is ready to modernize. Everyone—from operators to regulators—is looking for smarter, faster, and more secure ways to manage highly regulated, high-impact work.

But here’s the truth: not all solutions are created equal.

Some new entrants are offering early-stage beta tools. Others are repackaging general-purpose AI under the banner of “nuclear transformation.” What many still lack is what we’ve spent the last four years building at Nuclearn—deep operational understanding, embedded security architecture, and proven use cases deployed at scale.

Validation of the Mission

We’re not threatened by more players in the space. We welcome them. Every new entrant, every investor conversation, every “nuclear AI” LinkedIn post is validating what we’ve already proven: AI is no longer optional in this industry—it’s essential.

Since 2021, we’ve been supporting real-world operations across 48+ nuclear and utility sites in the U.S., Canada, and the U.K. We’ve worked inside secure environments, with live operational data, building tools that move the needle on efficiency, accuracy, and safety.

In other words, we’re not experimenting. We’re executing.

How Nuclearn Sets the Standard

Nuclearn wasn’t adapted for nuclear—it was built for it. Our team of nuclear engineers, planners, and outage veterans knew that generic AI couldn’t meet regulatory, compliance, or cultural requirements. So we designed a platform that could.

Here’s what differentiates Nuclearn in an increasingly noisy space:

  • Field-Proven Deployment: Our tools are actively in use at commercial nuclear sites—not in simulation, not in “pilot purgatory.”
  • Part 810 Compliant: Our system architecture was designed with export control, cyber resilience, and data sovereignty in mind from day one.
  • On-Prem & GovCloud Options: We know what IT, security, and operations teams need—and we offer deployment flexibility to match.
  • Designed for Real Workflows: Procedure updates, FSAR crosswalks, outage readiness, tagging validation, safety documentation—these aren’t buzzwords. They’re everyday challenges we solve.

While others are still preparing for the work, Nuclearn is already helping teams:

  • Cut hours from outage document prep
  • Reduce the review burden on procedure writers
  • Accelerate tagging accuracy during planning
  • Analyze safety observations and generate reports in real-time
  • Our platform doesn’t just talk about nuclear. It speaks fluently.

Why Competition Matters

Yes, we’re leading this category—but we don’t want to be alone in it. Innovation benefits from pressure and perspective. When more companies try to build for this space, we all learn what works, what doesn’t, and what’s required to earn trust in high-stakes environments.

Healthy competition pushes everyone to do better—for customers, for industry standards, and the future of nuclear.

But let’s be clear: this isn’t an industry that has time for AI that “might” work. This is a mission-critical environment. There is no room for hallucinated citations, opaque black boxes, or half-secure integrations.

So while we’re glad the space is growing, we’ll continue focusing on the things that matter most:
Security. Compliance. Trust. And results.

Customers Aren’t Looking for Options—They’re Looking for Outcomes

What we’re hearing in the field is that buyers aren’t overwhelmed—they’re skeptical. Leaders at plants, utilities, and national labs are asking:

  • Is it secure?
  • Is it proven?
  • Does it integrate with our workflow?
  • Can we deploy it without adding risk?

This is where Nuclearn continues to stand apart. Because our answers are:
✅ Yes.
✅ Yes.
✅ Yes.
✅ And yes.

We’ve never been interested in tech for tech’s sake. We’re here to build solutions that reduce friction, reclaim hours, and elevate the work of nuclear professionals.

The Bar Is High—And That’s a Good Thing

We’ve helped raise expectations. And we’re proud of that. Because when we hold ourselves—and our peers—to a higher standard, the entire industry benefits.

We want a world where:

  • AI-powered documentation becomes the norm, not the exception
  • Safety data is reviewed with contextual intelligence
  • Engineers are free to engineer, not just fill out forms

That’s not science fiction. That’s what our users are doing with Nuclearn—right now.

Final Word

The rise of new entrants into the nuclear and utility AI space is exciting. It means this sector is finally getting the innovation attention it deserves.

But we’re not racing to catch up. We’re defining the pace.
We’re already supporting operations, delivering value, and earning the trust of nuclear’s most security-conscious customers.

We’re not the future of nuclear AI.
We’re its present.