Tag: Automation

The Role of AI Agents in Driving Digital Transformation for Nuclear and Utilities Sectors

Posted on by sondra

Unlocking Efficiency and Innovation Through AI-Powered Process Engineering

The nuclear and utilities industries are experiencing an era of digital transformation, with AI agents at the heart of this evolution. These intelligent tools are revolutionizing traditional process engineering by automating repetitive tasks, analyzing complex datasets, and generating actionable insights. By embracing AI-driven solutions, organizations can optimize workflows, improve decision-making, and unlock new avenues for innovation.

This blog explores the profound impact of AI agents, the strategies for seamless integration, and the key benefits they bring to nuclear and utilities companies.

How AI Agents Are Reshaping Process Engineering

Process engineering in highly regulated industries like nuclear and utilities has long been a manual and resource-intensive effort. Engineers and operators spend countless hours analyzing workflows, identifying inefficiencies, and ensuring compliance with stringent regulations. AI agents take these capabilities to the next level, offering:

  • Bottleneck Identification: AI can analyze historical data and operational trends to pinpoint inefficiencies within workflows.
  • Predictive Maintenance: By monitoring equipment performance in real time, AI helps prevent failures and reduce unplanned downtime.
  • Regulatory Compliance Automation: AI automates documentation reviews, ensuring compliance with industry standards and reducing administrative overhead.
  • Data-Driven Insights: AI extracts and summarizes key findings from vast datasets, enabling faster, more informed decision-making.

Rather than replacing human expertise, AI agents augment teams, allowing them to focus on strategic and high-value tasks. This shift not only enhances efficiency but also fosters a culture of continuous improvement within organizations.

Laying the Foundation for AI Integration

To successfully integrate AI agents into existing operations, organizations must align people, processes, and technology. A structured approach ensures seamless adoption and maximizes the value AI brings to business operations. Here are the essential strategies:

1. Cross-Departmental Collaboration

AI adoption should be a collaborative effort, involving engineers, IT teams, operations managers, and compliance experts. Establishing a cross-functional team ensures diverse perspectives and alignment with organizational goals.

2. Prioritizing High-Impact Use Cases

To build momentum, organizations should focus on AI applications with measurable impact. Key areas include:

  • Automating regulatory document reviews
  • Enhancing maintenance scheduling
  • Streamlining incident reporting
  • Real-time performance monitoring

3. Assigning AI Champions

To drive accountability and ensure success, appointing AI process owners within departments is crucial. These individuals oversee AI deployment, monitor performance, and advocate for continuous improvement.

4. Standardizing Workflows for AI Adoption

By creating uniform workflows, organizations make it easier for AI agents to integrate into operations. Standardized processes enable scalability and consistency across departments.

Achieving Early Wins to Drive AI Adoption

Demonstrating early success is vital for building confidence in AI technology. Quick-win initiatives can help showcase the immediate value AI delivers. Some effective early-stage applications include:

  • Automating Regulatory Document Searches: AI reduces manual effort by quickly extracting and summarizing critical compliance information.
  • Streamlining Maintenance Logs: AI assists in identifying and prioritizing critical maintenance tasks, reducing equipment downtime.
  • Real-Time Reporting on Equipment Performance: AI-powered monitoring enables predictive maintenance, improving overall asset reliability.

By implementing these quick wins, organizations create a foundation for broader AI adoption and long-term digital transformation.

Tracking and Measuring AI’s Impact

To validate the success of AI initiatives, organizations must establish a framework for tracking efficiency gains. Key steps include:

  • Setting Baseline Metrics: Measure time spent on manual processes before AI integration.
  • Monitoring AI-Enabled Processes: Track improvements in efficiency and accuracy.
  • Using Real-Time Dashboards: Visualize AI performance and workflow enhancements.
  • Collecting User Feedback: Engage employees to gather insights on AI’s effectiveness and identify areas for further refinement.

By continuously assessing AI’s impact, organizations can optimize their strategies and ensure sustained improvements.

Bridging the AI Knowledge Gap

Despite AI’s potential, many organizations face challenges in adoption due to knowledge gaps and resistance to change. To overcome these hurdles, consider the following approaches:

  • Collaborate with AI Experts: Partner with process re-engineering specialists to guide AI integration efforts.
  • Develop Role-Specific Training Programs: Ensure employees understand how AI enhances their work.
  • Leverage External Consultants: Work with AI implementation specialists to streamline deployment.
  • Host Knowledge-Sharing Sessions: Encourage employees to discuss AI successes and challenges.

Bridging the knowledge gap empowers teams to embrace AI confidently and maximize its value.

A Vision for the Future: AI as a Strategic Partner

The digital transformation of the nuclear and utilities sectors is not about replacing human workers but enabling them to focus on more impactful tasks. Traditional methods of process engineering, while effective in their time, are now being revolutionized by AI’s ability to enhance precision, reduce human error, and adapt to evolving industry needs.

Organizations that proactively integrate AI into their workflows will:

  • Increase operational efficiency
  • Reduce compliance risks
  • Improve asset reliability
  • Foster innovation and adaptability

The key to success lies in starting small, demonstrating early value, and scaling AI initiatives with a structured governance framework.

The Time for Innovation is Now – Download the Research Brief

The nuclear and utilities industries are at a critical juncture where embracing AI is no longer optional—it is essential for staying competitive in a rapidly evolving landscape. By strategically integrating AI agents, organizations can drive operational excellence, ensure regulatory compliance, and unlock unprecedented efficiencies.

Ready to take the next step? Download our latest research brief today and discover how AI agents are transforming nuclear and utilities operations. Learn practical strategies for AI adoption, explore real-world use cases, and equip your organization with the knowledge to harness AI’s full potential.

Download the Research Brief Now

The future of AI-driven transformation begins with informed decision-making—secure your competitive advantage today.

Establishing Governance Frameworks for AI in the Nuclear and Utilities Sectors

Posted on by sondra

Redefining Governance for Digital Transformation

The nuclear and utilities industries are undergoing a major digital transformation, and AI agents are at the forefront of this evolution. These advanced tools can streamline workflows, optimize decision-making, and automate repetitive tasks. However, as organizations embrace AI-powered solutions, establishing a well-structured governance framework is essential to ensure security, transparency, and accountability without stifling innovation.

In this article, we explore the importance of governance for AI agents in nuclear and utilities, outline best practices, and present key strategies to balance agility with compliance. By implementing a governance framework tailored to AI’s capabilities, organizations can unlock AI’s full potential while mitigating risks and maintaining operational integrity.

The Role of AI Agents in Nuclear and Utilities

AI agents, especially those powered by generative AI and large language models (LLMs), offer game-changing capabilities in process automation and operational efficiency. Unlike traditional automation tools, AI agents continuously learn and adapt, making them ideal for managing complex workflows in regulated industries like nuclear and utilities.

Some of the primary benefits of AI agents in these industries include:

  • Process Optimization: AI can identify inefficiencies, suggest improvements, and automate routine tasks, reducing operational costs and improving productivity.
  • Data-Driven Decision Making: AI analyzes vast amounts of structured and unstructured data to provide actionable insights, enabling organizations to make more informed decisions.
  • Compliance and Risk Management: AI enhances regulatory compliance by automating documentation, monitoring adherence to protocols, and identifying potential risks before they escalate.
  • Workforce Augmentation: By handling repetitive and mundane tasks, AI allows human experts to focus on strategic and high-value activities.

While these benefits are compelling, they also introduce new challenges related to accountability, security, and transparency. This is where a robust AI governance framework becomes essential.

Building a Strong AI Governance Framework

Establishing governance for AI in the nuclear and utilities sectors requires a structured approach that ensures AI adoption aligns with organizational goals while maintaining compliance and trust. A well-designed framework includes the following components:

1. Define Roles and Responsibilities

AI governance should be a collaborative effort involving cross-functional teams, including IT, compliance, security, and operations. Each team member should have clear responsibilities for overseeing AI implementation, monitoring performance, and ensuring compliance with industry regulations.

2. Emphasize Explainable AI (XAI)

AI systems must be transparent in their decision-making processes. Implementing Explainable AI (XAI) ensures that AI-generated insights can be traced back to their source data, algorithms, and logic. This enhances trust and allows stakeholders to understand and validate AI-driven decisions.

3. Develop Audit Trails for Accountability

Governance frameworks should include robust auditing mechanisms to track AI decision-making. For example, when an AI agent recommends an operational adjustment, the system should document the data inputs, parameters, and final recommendations. Audit trails provide transparency and help organizations refine AI strategies over time.

4. Ensure Data Security and Regulatory Compliance

AI governance must prioritize data security, especially in highly regulated sectors. Best practices include:

  • Deploying AI solutions in secure, on-premise environments.
  • Ensuring that sensitive data remains within controlled organizational infrastructure.
  • Implementing stringent access controls and encryption to protect proprietary and regulatory-sensitive data.

Balancing Governance with Agility

While strict governance is crucial, it must not become a bottleneck for AI innovation. Organizations should adopt flexible and scalable governance strategies that allow AI systems to evolve while ensuring compliance. Key approaches include:

  • Start with a Minimal Viable Governance Framework: Implement an initial governance model that can scale as AI adoption grows.
  • Use Industry-Aligned Governance Templates: Standardized frameworks streamline compliance without excessive bureaucracy.
  • Automate Compliance Checks: AI-driven governance tools can monitor adherence to policies in real time, reducing the manual burden on compliance teams.

Maintaining agility in AI governance ensures that organizations can quickly adapt to evolving technologies and regulatory landscapes while minimizing risk exposure.

Future-Proofing AI Governance

To sustain AI adoption in the long term, governance models should be dynamic and forward-thinking. Future-proofing AI governance involves:

1. Implementing Adaptive Governance Models

Rather than relying on static governance policies, organizations should adopt adaptive models that evolve as AI agents improve. This ensures that governance structures remain relevant and effective as AI capabilities advance.

2. Real-Time Oversight Dashboards

Organizations should leverage AI-powered dashboards that provide real-time visibility into AI activity, compliance status, and key performance indicators. These dashboards enable proactive decision-making and rapid response to emerging risks.

3. Embedding Ethical AI Practices

AI governance should incorporate ethical considerations, such as fairness, accountability, and bias mitigation. Ensuring that AI operates without unintended biases helps build trust and strengthens AI adoption across stakeholders.

Achieving Business Outcomes with AI Governance

A well-structured AI governance framework is not just about risk mitigation—it also drives meaningful business outcomes. By implementing governance best practices, organizations can:

  • Enhance transparency and trust in AI-driven decisions.
  • Accelerate process re-engineering with secure and accountable AI tools.
  • Free human resources from mundane tasks, enabling them to focus on strategic initiatives.
  • Foster a culture of continuous improvement as AI systems evolve.

With the right governance strategy, AI can transition from being a standalone tool to a strategic asset that enhances organizational efficiency and resilience.

Download the Full Research Brief Today

The nuclear and utilities sectors are on the brink of unprecedented transformation. AI-powered agents have the potential to redefine industry standards, but their success hinges on well-structured governance frameworks. By proactively establishing AI governance, organizations can drive innovation, maintain compliance, and ensure operational excellence.

To gain deeper insights into AI governance and best practices, download our latest research brief today. Learn how to navigate the complexities of AI adoption while safeguarding your organization’s integrity.

Download the Research Brief Now

The future of AI-driven transformation starts with a strong governance strategy—take the first step today.

Powering the Future: Amazon and Google’s Investment in SMRs and How NuclearN is Driving AI Integration for a Carbon-Free Tomorrow

Posted on by sondra

NuclearN applauds the groundbreaking investments from Amazon and Google in Small Modular Reactors (SMRs), recognizing how these developments align with the future of clean, sustainable energy. Amazon’s $500 million partnership with Energy Northwest and Google’s collaboration with Kairos Power are significant milestones in the integration of nuclear energy with advanced technology. These partnerships emphasize that nuclear energy, combined with cutting-edge technologies like artificial intelligence (AI), is crucial to powering the infrastructure required for future technological advancements.

As Phil Zeringue, VP of Strategic Partnerships at NuclearN, points out, “These partnerships are critical to powering the future technologies we rely on, and the synergy between nuclear and AI is key to a sustainable, energy-secure future.” Zeringue’s perspective reflects the evolving role of nuclear energy as a foundational pillar for the world’s energy needs, especially as global tech giants like Amazon and Google take action to secure carbon-free power sources for their growing infrastructure.

NuclearN is well-positioned at the intersection of nuclear energy and technology. With over 48 nuclear reactors across North America and Europe currently utilizing our AI-driven tools to enhance their operations, we understand the immense potential that AI holds in transforming how nuclear energy is deployed. We also recognize that Amazon’s and Google’s investment in SMRs goes beyond simply meeting their energy needs; it represents a commitment to long-term sustainability and a clear acknowledgment of the need for innovation to address the global energy crisis.

The Intersection of AI and Nuclear Energy

Nuclear energy has long been regarded as a critical component of achieving a carbon-free future. However, the rise of SMRs provides a more flexible, scalable option for energy generation. Unlike traditional, larger nuclear reactors, SMRs can be deployed in a wider range of locations, require less upfront capital, and offer shorter construction timelines. Yet, despite these advantages, challenges such as human error, design changes, and logistical issues remain. This is where AI comes into play.

At NuclearN, we see AI as a critical enabler in the SMR deployment process. By integrating AI into SMR projects, we can significantly reduce human error, streamline design changes, and improve operational efficiency. Our AI-driven tools are already being used at nuclear sites to automate planning, streamline documentation processes, and enhance safety protocols. These tools allow engineers and operators to focus on building and maintaining SMRs efficiently while minimizing risks.

Phil Zeringue underscores the importance of these technologies, stating, “The integration of AI with SMRs is crucial for enhancing safety, reducing risks, and increasing the overall efficiency of these projects. These technologies not only align with our social impact goals but are also necessary to ensure the reliable, secure, and sustainable energy needed to power the future.”

By leveraging AI, we can address the two largest drivers of delays in SMR construction: human error and design changes. AI solutions can automate repetitive tasks, provide real-time insights for decision-making, and ensure that complex data is handled with precision. This not only improves the safety and efficiency of SMR projects but also reduces costs and keeps projects on schedule.

Amazon and Google: Leading the Charge in SMR Development

The recent announcements from Amazon and Google demonstrate that some of the world’s most innovative companies are betting on nuclear energy to meet their future energy needs. Amazon’s $500 million investment in partnership with Energy Northwest, along with Google’s collaboration with Kairos Power, showcases a growing recognition of SMRs’ potential to provide reliable, carbon-free energy. These companies are not just investing in their own infrastructure—they are signaling to the world that nuclear energy, paired with advanced technology, is a critical solution to the energy and environmental challenges we face.

Zeringue notes, “As more technology companies like Amazon and Google invest in nuclear technologies to power their infrastructure requirements, new nuclear power is needed more than ever. Which mean new workforce, new manufacturing and new tools to shrink the timelines to bring these crucial assets online”

These tech giants rely on vast amounts of energy to power their global operations, data centers, and the technologies that billions of people use every day. As they continue to expand their reach, the demand for secure and sustainable energy sources grows. Nuclear energy, particularly SMRs, offers a viable path forward. By investing in these solutions, Amazon and Google are not only securing their own energy futures but also helping to pave the way for the wider adoption of SMRs.

NuclearN’s Role in Supporting the Future of Energy

NuclearN’s mission is to drive forward the advancement of AI technologies alongside scalable energy solutions like SMRs. With our expertise in AI and nuclear energy, we are uniquely positioned to support the growth of SMRs and help meet the world’s increasing energy demands. Our work with 48 nuclear sites across North America and Europe has provided us with valuable insights into how AI can transform nuclear operations. By automating critical processes, improving safety, and reducing human error, we help nuclear facilities operate more efficiently and safely.

Our alignment with companies like Amazon and Google goes beyond shared goals of carbon neutrality. We recognize the importance of building partnerships that drive innovation and ensure a sustainable future. As SMRs become an increasingly important part of the energy landscape, the role of AI will only continue to grow. By integrating AI into SMR projects, we can accelerate the deployment of these reactors and ensure they operate safely and efficiently.

As Phil Zeringue emphasizes, “AI is a major force driving the need more carbon free energy, it is not lost on us that we are providing AI solution to make nuclear power the responsible, reliable and affordable choice which can power more AI which can further improve nuclear, it’s a virtuous cycle we are proud to be at the forefront of.”

Looking Ahead: A Carbon-Free, Energy-Secure Future

The investments from Amazon and Google mark an exciting chapter in the future of nuclear energy. Their leadership in this space highlights the importance of sustainable energy solutions to meet the demands of the modern world. NuclearN remains committed to advancing AI technologies that support the deployment and operation of SMRs, ensuring that the energy needs of the future are met with innovation, safety, and sustainability.

As the energy landscape evolves, it’s clear that partnerships between technology companies and nuclear energy providers are key to driving the development of scalable, carbon-free energy. NuclearN is proud to be part of this movement and looks forward to collaborating with industry leaders to create a cleaner, safer, and more secure energy future for all.



Enhance Your Outage Management with NuclearN

Posted on by sondra

Managing nuclear plant outages is a critical task that demands meticulous planning, precise execution, and rapid responses to unforeseen challenges. At NuclearN, our platform, designed by nuclear engineers for nuclear engineers, offers a comprehensive solution to transform your outage management process, ensuring safety, compliance, and cost-effectiveness.

Comprehensive Outage Schedule Support

NuclearN provides robust schedule support to help you manage and mitigate risks effectively:

  • Identify Risks: Quickly identify high-schedule risk activities by department to prioritize and mitigate potential delays.
  • Accurate Predictions: Obtain precise outage duration predictions to plan effectively.
  • Flexibility in Planning: Run “what-if” scenarios to understand the impact of scope changes and adjust plans proactively.

Financial Support for Optimal Budget Management

Efficient financial management is crucial during outages. NuclearN offers tools to optimize your budget and ensure financial accuracy:

  • Budget Optimization: Strategically reduce online and spring outage O&M charges to accommodate outage expenses.
  • Asset Validation: Ensure all planned outage work orders are correctly classified as fixed assets, preventing financial discrepancies.

Engineering Support for Efficient Operations

NuclearN enhances engineering support by speeding up processes and ensuring compliance:

  • Expedite Changes: Speed up the process for DCNs and temporary alterations to keep projects on track.
  • Compliance Checks: Efficient 50.59 screening for replacing obsolete parts, ensuring regulatory compliance.
  • Scope Review: Accelerate the engineering review of scope, ensuring all aspects are covered in time.

Outage Readiness: Preparing for Success

With NuclearN, your facility is well-prepared before the outage begins, setting the stage for success with fewer resources:

  • Daily Monitoring: See changes to schedule risks daily as tasks are complete, allowing quick adjustments.
  • Impact Analysis: Understand each activity’s percentage impact on the critical path to prioritize effectively.
  • Scenario Planning: Continuously run “what-if” scenarios to adapt to new challenges and changes.
  • Risk Identification: View high-schedule risk activities by group to allocate resources where needed most.

Observation Program for Continuous Improvement

NuclearN supports an efficient observation program to drive continuous improvement:

  • Efficient Observations: Supervisors can conduct faster, lower-friction observations.
  • Real-Time Analysis: Benefit from real-time trending and analysis to detect and address issues promptly.
  • Enhanced Quality: Ensure higher quality and more meaningful observations.

Issue Resolution and Enhanced Operations

NuclearN enables your team to react swiftly and efficiently during outages:

  • Expedite Changes: Speed up DCNs and temporary alterations.
  • Compliance Checks: Efficient 50.59 screening for obsolete parts.
  • Scope Review: Accelerate the engineering review of scope.

Optimize Financial and Engineering Efficiency

NuclearN helps you optimize budgets and ensure accurate financial management:

  • Expense Management: Reduce unnecessary O&M charges.
  • Asset Classification: Ensure accurate classification of planned outage work orders.

Stay ahead with NuclearN and transform your outage management strategy today!

How AI is Powering Up the Nuclear Industry 

Posted on by Courteney_Fox


Sequoyah Nuclear Power Plant 

In an era where digital fluency is the new literacy, Large Language Models (LLMs) have emerged as revolutionary game-changers. These models are not just regurgitating information; they’re learning procedures and grasping formal logic. This isn’t an incremental change; it’s a leap. They’re making themselves indispensable across sectors as diverse as finance, healthcare, and cybersecurity. And now, they’re lighting up a path forward in another high-stakes arena: the nuclear sector.



The Limits of One-Size-Fits-All: Why Specialized Domains Need More Than Standard LLMs

In today’s digital age, Large Language Models (LLMs) like GPT-4 have become as common as smartphones, serving as general-purpose tools across various sectors. While their wide-ranging training data, which spans from social media to scientific papers, is useful for general capabilities, this limits their effectiveness in specialized domains. This limitation is especially glaring in fields that require precise and deep knowledge, such as nuclear physics or complex legal systems. It’s akin to using a Swiss Army knife when what you really need is a surgeon’s scalpel.

In contrast, specialized fields like nuclear engineering demand custom-tailored AI solutions. Publicly-available LLMs lack the precision needed to handle the nuanced language, complex protocols, and critical safety standards inherent in these areas. Custom-built AI tools go beyond mere language comprehension; they become repositories of essential field-specific knowledge, imbued with the necessary legal norms, safety protocols, and operational parameters. By focusing on specialized AI, we pave the way for more reliable and precise tools, moving beyond the “Swiss Army knife” approach to meet the unique demands of specialized sectors.

LLMs are Swiss Army knives in that they are great at a multitude of tasks; this is paradoxical to their utility in a field like nuclear where nuance is everything.


The Swiss Army Knife In Action

Below is a common response from a public chatbot on most plant specific questions. The information about this site is widely available online and has been published well before 2022 with the power plant’s commission date occurring in 1986.

From the chatbot’s response, the generic information provided by this public-available model does not give enough clarity for experts to rely on. To answer the above question, the model will need to be adapted to a specific domain.

Adapting general models to be domain specific is not easy however.  Some challenges with this task include:

  1. Financial and Technical Hurdles in Fine-Tuning—Fine-tuning public models is a costly affair. Beyond the financial aspect, modifications risk destabilizing the intricate instruct/RLHF tuning, a nuanced balance established by experts.
  2. Data Security: A Custodian Crisis —Public models weren’t built with high-security data custodianship in mind. This lack of a secure foundation poses risks, especially for sensitive information.
  3. A Dead End for Customization—Users face a brick wall when it comes to customizing these off-the-shelf models. Essential access to model weights is restricted, stifling adaptability and innovation.
  4. Stagnation in Technological Advancement —These models lag behind, missing out on revolutionary AI developments like RLAIF, DPO, or soft prompting. This stagnation limits their applicability and efficiency in evolving landscapes.
  5. The Impossibility of Refinement and Adaptation—Processes integral for optimization, such as model pruning, knowledge distillation, or weight sharing, are off the table. Without these, the models remain cumbersome and incompatible with consumer-grade hardware.


NuclearN

NuclearN specializes in AI-driven solutions tailored for the nuclear industry, combining advanced hardware, expert teams, and a rich data repository of nuclear information to create Large Language Models (LLMs) that excel in both complexity and precision. Unlike generic LLMs, ours are fine-tuned with nuclear-specific data, allowing us to automate a range of tasks from information retrieval to analytics with unparalleled accuracy.


What makes our models better than off-the-shelf LLMs? 

Large Language Models (LLMs) from NuclearN are trained on specialized nuclear data that are transforming several core tasks within the nuclear industry, leveraging their vast knowledge base and advanced understanding of nuclear context-specific processes. These models, when expertly trained with the right blend of data, algorithms, and parameters, can facilitate a range of complex tasks and information management functions with remarkable efficiency and precision.

NuclearN is training our LLMs to enhance several core functions:

  1. Routine Question-Answering: NuclearN’s trains LLMs on a rich dataset of nuclear terminologies, protocols, and safety procedures. They offer accurate and context-aware answers to technical and procedural questions, serving as a reliable resource that reduces the time needed for research and minimizes human error.
  2. Task-Specific and Site-Specific Fine Tuning: Even though our LLMs are trained to be nuclear-specific, different sites can have very specific plant designs, processes, and terminology.  Tasks such as engineering evaluations or work instruction authoring may be performed in a style unique to the site.  NuclearN offers private and secure, site and task-specific fine tuning of our LLMs to meet these needs and deliver unparalleled performance.
  3. Neural Search: The search capabilities of our LLMs go beyond mere keyword matching. They understand the semantic and contextual relationships between different terminologies and concepts in nuclear science. This advanced capability is critical when one needs to sift through large volumes of varied documents—be it scientific papers, historical logs, or regulatory guidelines—to extract the most pertinent information. It enhances both the efficiency and depth of tasks like literature review and risk assessment.
  4. Document Summarization: In an industry awash with voluminous reports and papers, the ability to quickly assimilate information is vital. Our LLMs can parse through these lengthy documents and distill them into concise yet comprehensive summaries. They preserve key findings, conclusions, and insights, making it easier for professionals to stay informed without being overwhelmed by data.
  5. Trend Analysis from Time-Series Data: The nuclear industry often relies on process and operational data gathered from sensors in the plant to track equipment performance and impacts from various activities. NuclearN is training our LLMs to be capable of analyzing these time-series data sets to discern patterns, correlations, or trends over time. This allows our LLMs to have a significantly more comprehensive view of the plant, which is particularly valuable for monitoring equipment health and predicting operational impacts.

By leveraging the capabilities of NuclearN’s specialized LLMs in these functional areas, the nuclear industry can realize measurable improvements in operational efficiency and strategic decision-making.

Stay informed and engaged with everything AI in the nuclear sector by visiting The NuclearN Blog. Join the conversation and be part of the journey as we explore the future of AI in nuclear technology together. 

Nuclearn v1.8 – Neural Search and Easier Automation

Posted on by Courteney_Fox

Nuclearn recently released version 1.8 of its analytics and automation platform, bringing major upgrades like neural search for intuitive queries, configurable automation routines, expanded analytics outputs, and enhanced ETL data integration. Together these features, some of them AI-driven, aim to optimize workflows and performance.

Neural Search

The neural search upgrade allows searching based on intent rather than keywords, even with ambiguous queries. Neural algorithms understand semantics, context, synonyms, and data formats. This saves time compared to traditional keyword searches, and provides significant advantages when context-sensitive information retrieval is crucial.

Some of the benefits of neural search include:
Precision of Search Results: Traditional keyword-based searches often yield an overwhelming number of irrelevant results, making it difficult for plant personnel to find the specific information they need. Neural search engines deliver results with ranked relevance. This means results are not just based on keyword match but on the basis of how closely the content of the document matches the intent of the search query.  

Contextual Understanding: Boolean queries, which are typically used in traditional search engines, lack the ability to understand the contextual nuances of complex technical language often found in engineering and compliance documentation. Neural search algorithms have a kind of “semantic understanding” that can understand the context behind a query, providing more relevant results. In addition, Neural search understands synonyms and related terms, crucial when dealing with the specialized lexicon in nuclear, thus making searches more robust.

Multiple Data Formats: Nuclear plants often store data in different formats, such as PDFs, Word documents, sensor logs, and older, legacy systems. A neural search engine can be trained to understand and index different types of data, providing a unified search experience across multiple data formats. 

Selective Classification for Unmatched Automation Accuracy

AutoCAP Screener also saw major improvements in v1.8. You can now set desired overall accuracy levels for automation templates. The Nuclearn platform then controls the confidence thresholds using a statistical technique called “selective classification” that enables theoretically guaranteed risk controls. This enables the system to ensure it operates above a user-defined automation accuracy level.

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With selective classification, plants can improve automation rates and efficiency without compromising the quality of critical decisions. Risk is minimized by abstaining from acting in uncertain cases. The outcome is automation that consistently aligns with nuclear-grade precision and trustworthiness. By giving you accuracy configuration control, we ensure our AI technology conforms to your reliability needs. 

Additionally, multiple quality of life enhancements were added to the AutoCAP audit pages. Users can now sort the audit page results, add filters, integrate PowerBI dashboards with audit results, and even export the automation results to csv. These enhancements make it easier and more flexible for users to assess, evaluate, and monitor the automation system.

Analytics & Reporting Enhancements

On the analytics front, our customers wanted more customizations. v1.8 answers their request with the ability to upload their own custom report templates. In addition, customers can change date aggregations in reports to tailor the visualizations for specific audiences and uses. Enhanced dataset filtering and exporting also allows sending analyzed data to PowerBI or Excel for further manipulation or presentation.

Buckets

Editing analytics buckets is now more flexible too, with overwrite and save-as options. We added the ability to exclude and filter buckets from the visualization more easily and make changes to existing buckets, including their name.  

Data Integration

Behind the scenes, ETL workflows (meaning “extract, transform, load” data) were upgraded to more seamlessly ingest plant data into the Nuclearn platform. Users can now schedule recurring ETL jobs and share workflows between sites. With smooth data onboarding, you can focus your time on analytics and automation rather than manually uploading data. 

With advanced search, configurable automation, expanded analytics, and optimized data integration in v1.8, the Nuclearn Platform is better equipped to drive operational optimization using AI-powered technology. This release highlights Nuclearn’s commitment to meaningful innovation that solves real-world needs.

DARSA: The Guide to Full Process Automation Using AI

Posted on by Courteney_Fox

You don’t automate right away…

Process automation using Artificial Intelligence is a complex endeavor. To successfully automate a process, automation systems and their implementers need to effectively incorporate complex technologies, a deep understanding of the business processes, risk-based decision making, and organizational change management all at once! This challenge can feel insurmountable to many organizations looking to start adopting AI-driven process automation. And unfortunately for some, it has proven to be so.

Luckily, there are battle-tested methods for bringing an automation system to life and avoiding the potential pitfalls. Here at Nuclearn, we have developed a project implementation process we call DARSA that helps guide us through automation projects. DARSA helps us deliver maximum value with minimal risk by leveraging an iterative, agile approach to AI-driven automation.

So what is DARSA? DARSA is a five-step linear process that stands for Decisions-Data-Direction, Assess, Recommend, Semi-Automate, and Automate. Each step in DARSA is a distinct phase with distinct characteristics, and transitions between these phases are planned explicitly and usually require system changes. To learn more about DARSA, we must dive into the first phase: “Decisions, Data and Direction”.

1) Decisions – Data – Direction

Before starting AI-driven automation, it is important to specify several key factors that will guide the project. These items are Decisions, Data, and Direction.

Decisions are the first, and most critical item to define early in an AI-driven automation project. At the end of the day, if you are embarking on an AI-driven automation project, you are doing so because you need to automate challenging decisions that currently require a human. If there is no decision to be made, then AI will not help automation in any meaningful way. If the decisions are trivial or based on simple rules, there is no need for AI. These processes should be automated by traditional software. So the first, essential part of an AI-driven automation project is identifying and defining the decisions that are planned to be automated.

Data is the next item that must be specified. An AI-driven automation project needs to identify two key sets of data: the input data being used to influence decisions, and the data created as a result of those decisions. These are critical, as AI-driven automation relies heavily on machine learning. To learn how to make decisions automatically, machine learning requires historical data. For each decision, there should be a detailed log of all data used at decision time, and a log of all historical human decisions.

Direction. AI projects must begin with clear direction, but unlike large traditional software projects, AI-driven automation projects cannot begin with a detailed project plan laying out detailed requirements gathering and design. AI and machine learning are notoriously unpredictable – even the most experienced practitioners have challenges predicting how well models will perform on new datasets and new challenges. Automation systems often have to evolve around the unpredictable strengths and weaknesses of the AI. As a result, it is important to specify a clear direction for the project. All members of the project should be aligned with this direction, and use it to guide their iterations and decisions. For example, in an AI-driven automation system for helping generate Pre-Job Brief forms, the direction for the project might be “Reduce the amount of time required to assemble Pre-Job Briefs while maintaining or improving the quality of Pre-Job Briefs”. This simple statement of direction goes a long way towards bounding the project scope, ruling out system design decisions that are unacceptable, and fostering potential innovation.

2) Assess

Once the Decisions, Data and Direction are specified, the most important factors for success in an AI-Driven automation project are fast iterations and good feedback. That is why the second phase in DARSA is “Assess”. During this stage of the project, nothing is actually automated! The “automation” results are being shared with subject matter experts, so they can assess the results and provide feedback. Automation system designers and Data Scientists are already quite familiar with testing how well their system works via various traditional methods. While these can help with generating accuracy metrics (the model is 95% accurate!), these methods are quite poor at evaluating exactly where the automations will fail or be inaccurate, why they are that way, and what the impacts are. The Assess phase is often where important risks and caveats are identified, and where additional considerations for the project are discovered.

Let’s take for example my experience with a project attempting to automate the screening of Condition Reports (CR) at a Nuclear Power Plant. One of the key decisions in screening a CR is determining whether the documented issue has the potential to affect the safe operation of the plant, often referred to as a “Condition Adverse to Quality”. Before even showing our AI model to users, my team had produced some highly accurate models, north of 95% accurate! We knew at the time that the human benchmark for screening was 98%, and we figured we were very close to that number, surely close enough to have successful automation. It was only after going through the “Assess” phase that we learned from our subject matter experts that we had missed a key part of the automation.

We learned during the Assess phase that not all Condition Reports are the same. In fact, there was a drastically asynchronous cost associated with wrong predictions. Overall accuracy was important, but what would make or break the project was the percentage of “Conditions Adverse to Quality” that we incorrectly classified as Not Conditions Adverse to Quality. The reverse error (classifying Not Adverse to Quality as Adverse to Quality) had a cost associated with it – we might end up performing some unnecessary paperwork. But get a few high-profile errors the other way, and we would potentially miss safety-impacting conditions, undermining regulatory trust in the automation system and the CAP Program as a whole.

As a result, we made some fundamental changes to the AI models, as well as the automation system that would eventually be implemented. The AI models were trained to be more conscious of higher-profile errors, and the automation system would take into consideration “confidence” levels of predictions, with a more conservative bias. A thorough Assessment phase reduced the risk of adverse consequences and ensured any pitfalls were detected and mitigated prior to implementation.

3) Recommend

After the Assess phase, Recommendation begins. The Recommend phase typically involves providing the AI results to the manual task performers in real-time, but not automating any of their decisions. This stage is often very low risk – if the AI system is wrong or incorrect there is someone manually reviewing and correcting the errors, preventing any major inaccuracies. This is also the first stage that realizes delivered value of an AI-driven automation system.

Increased manual efficiency is often recognized as a benefit in the Recommend phase. In the majority of cases, it is physically faster for someone to perform a review of the AI’s output and make small corrections versus working the decision task from start to finish. Paired with a proper Human/AI interface, the cognitive load, manual data entry, and the number of keystrokes/mouse clicks are drastically reduced. This helps drive human efficiencies that translate to cost savings.

The Recommendation phase also permits the capture of metrics tracking how well the automation system is performing under real-world use. This is absolutely critical if partial or complete automation is desired. By running in a recommendation setup, exact data about performance can be gathered and analyzed to help improve system performance and gain a deeper objective understanding of your automation risk. This is important for deciding how to proceed with any partial automation while providing evidence to help convince those skeptical of automation.

This stage may last as short as a few weeks, or as long as several years. If the automation system needs additional training data and tweaking, the recommendation phase provides a long runway for doing so in a safe manner. Since the system is already delivering value, there is relieved pressure to reach additional levels of automation. On some projects, this phase may provide enough ROI on its own that stakeholders no longer feel the need to take on additional risk with partial or complete automation.

4) Semi-Automate

The next step in DARSA is “Semi-Automate”. This is the first stage to both fully realize the benefits and risks of automation. This phase is characterized by true automation of a task – but only for a subset of the total tasks performed.

The metrics gathered in the Recommend phase play a key role here, as they can inform which parts of the task are acceptable for automation. As the system encounters different inputs and situations, total system confidence will vary. Based on this confidence, among other metrics, automation can be implemented as a graded approach. Low-risk, high-confidence tasks are usually automated first, and after the system continues to learn, and stakeholder confidence is improved, higher-risk automations can be turned on.

For example, take a system intended to automate the planning and scheduling functions required for Nuclear Work Management. Such a system would begin to partially automate the scheduling of work activities that have low safety and operational impacts, and the planning of repetitive activities that have little historical deviation in the execution work steps. These activities are low-risk (if something goes wrong there are minimal consequences) and high-confidence (the AI has lots of previous examples with defined conditions).

During semi-automation, it is prudent to still have a manual review of a portion of automated tasks to monitor model performance, as well as provide additional training data. Without manual review, there is no longer a “ground truth” for items that have been automated. This makes it challenging to know whether the system is working well! Additionally, AI performance may begin to stagnate without the inclusion of new training examples, similar to how human performance may stagnate without new learnings and experiences.

5) Automate

The final phase that every automation system aims to achieve: complete automation. This phase is characterized by the complete automation of all tasks planned in the scope of the project. The system has been running for long enough and has gathered enough data to prove that there is no human involvement necessary. From this point forward, the only costs associated with the task are the costs associated with running and maintaining the system. Complete automation is more common with tasks that have a lower overall level of risk, yet require a lot of manual effort without automation. The most common example of this in the Nuclear Industry today is automated Corrective Action Program trend coding.

It is expected to jump back and forth between partial and complete automation at some frequency. A common case where this can occur is when the automated task or decision is changed, and the automation system hasn’t learned what those changes are. There will need to be some amount of manual intervention until the system learns the new changes, and full automation can be turned back on. An example of this would be in a “trend-coding” automation system when the “codes” or “tags” applied to data are altered.

Start Using DARSA

DARSA provides a proven roadmap to designing, building, implementing, and iterating an AI-driven automation system. Using this process, organizations embarking on the development of new automation systems can deliver maximum value with minimal risk, using a methodology appropriate for modern AI in practical automation applications.

Visit https://nuclearn.ai to learn more about how Nuclearn uses DARSA to help Nuclear Power Plants achieve AI-driven automation.