AI Research

How Nuclearn is using AI to streamline nuclear development

Published

2 days ago

September 9, 2025

Photo credit: Inha Khrushchova / Shutterstock

Running nuclear power plants is a labor-intensive affair, with plants employing more than 500 workers per plant. And it requires a host of processes, such as condition report analysis, outage planning, and regulatory documentation — each of which can take teams of highly skilled people weeks to complete.

As delicate and essential as these processes are, Jerrold Vincent, CFO and co-founder of Nuclearn, believes that they’re also well-suited to be automated by artificial intelligence. The startup, founded in 2021, has created an AI platform dedicated to nuclear operations. And today, it announced that it has raised $10.5 million in Series A funding, led by Blue Bear Capital, with additional investments by SJF Ventures, AZ-VC, and Nucleation Capital.

“Because nuclear is so documentation- and process-heavy, there’s a really unique opportunity to use AI and machine learning to make plants run more efficiently,” Vincent told Latitude Media. “The majority of costs for running a nuclear power plant [aren’t] fuel or equipment. It’s actually people.”

And those people are in short supply. The nuclear sector is facing the same labor shortages that challenge many other energy sectors. This will be exacerbated by an expected wave of retirements; in 2022, the Global Energy Talent Index estimated that around 25% of nuclear workers were over 55 years old.

At the same time, nuclear is going through something of a renaissance, prompted by the booming data center sector’s hunt for abundant baseload power, which has led hyperscalers such as Microsoft and Google to invest heavily in nuclear solutions old and new. The increase in demand is expected to put additional strain on the dwindling workforce.

Nuclearn aims to use AI and machine learning to support, especially the work of engineers who perform documentation-heavy, repetitive work. Using advanced tools, Nuclearn can help workers write and classify work orders and engineering evaluations, assess whether new tests or changes to a facility are in compliance or require approval by the Nuclear Regulatory Commission, and renew licences, among other things. It does so using both generic models trained on publicly available data, and customer-specific models trained on the needs and data sets of specific power plants.

For example, the startup’s first product, called Corrective Action Program AI, or CapAI, automates the mandatory systems for identifying, assessing, and resolving issues. “Historically, at a plant, there’s been a team of people to do that job,” Vincent said. “With our platform, we can do it automatically, saving them time and effort.” Despite the fact that all nuclear power plants are required to have a corrective action program, the product requires customer-specific training because all the programs look slightly different, and general modeling techniques can’t achieve the needed levels of accuracy.

Nuclearn’s platform is already deployed in more than 65 reactors globally. The startup uses an annual subscription business model, which Vincent says isn’t common in the industry, but that customers like because it’s transparent and easy to use.

Nuclear challenges

Because of how the nuclear industry is structured — and its safety and security needs — it comes with some unique challenges for an AI platform like Nuclearn. For one, many of the documents Nuclearn uses for training its models are “ancient,” according to Vincent, and require “a lot of work on data cleanup.”

As Ernst Stack, founding partner at Blue Bear Capital, explained, the industry “has had very little innovation, especially in software that’s been applied to it.”

Additionally, the things Nuclearn automates are often complicated, multi-step processes involving many different documents. “When you’re doing that kind of work, you can’t just provide an answer to a nuclear engineer and have them accept that and sign off on it,” Vincent said. “You need auditability and visibility into the process [and] the reasoning that goes through it, so that they can review, adjust as needed, and really trust that kind of output.”

And in the U.S., access to sensitive nuclear energy information is restricted under federal law to U.S. citizens, and in many cases also to lawful permanent residents. “What that means in practice is that a lot of sites will not send that data outside of their network; or if they do, there are very strict requirements on where that data is hosted, which make most cloud providers basically a no-go for that kind of data,” Vincent said.

This means that Nuclearn owns its own hardware, which it keeps in a co-location data center in Phoenix, and a small part of its newly-announced fundraise will go into its expansion.

“It’s not something many startups do because they use cloud, and [hardware] comes across as an expense,” Vincent said. “But we plan our hardware strategy in a way that allows us to have the hardware we need to train the latest and greatest models for our hosted customers, and do so with a cost-effective approach.”

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AI Research

Companies Bet Customer Service AI Pays

Published

45 minutes ago

September 11, 2025

PYMNTS

Klarna’s $15 billion IPO was more than a financial milestone. It spotlighted how the Swedish buy-now-pay-later (BNPL) firm is grappling with artificial intelligence (AI) at the heart of its operations.

Back in 2023, Chief Executive Sebastian Siemiatkowski suggested AI could replace large parts of the company’s customer-service workforce. The remarks sparked pushback from employees and skepticism from customers, many of whom doubted whether the technology was advanced enough to provide empathy and reliability at scale.

Pivoting and Learning

Klarna’s first wave of AI adoption proved too rigid, with customers finding the experience inconsistent. The company now pivoted toward a blended approach: AI for speed and scale, humans for empathy and trust. That adjustment echoes a lesson resonating across industries. AI works best when it augments, rather than replaces, human agents.

The company’s focus on human-powered customer support shows how the firm is hiring again to ensure customers always have the option of speaking to a person. “From a brand perspective, a company perspective, I just think it’s so critical that you are clear to your customer that there will be always a human if you want,” Siemiatkowski told Bloomberg News, as reported by PYMNTS.

As Vinod Muthukrishnan, vice president and chief operating officer of Webex Customer Experience Solutions at Cisco, explained, many financial institutions are moving past pilots and into deployment.

“These firms are increasingly leveraging their AI focus on hyper-personalized CX [customer experience] such as personal financial advice or dynamic credit limit adjustments and offers, all enabled via real-time analytics,” he told PYMNTS. Retailers and service providers face similar opportunities, provided they align strategy with measurable ROI.

Five Areas for AI, Customer Care

1. Proactive Issue Resolution

AI can anticipate problems before customers complain. Declined payments, unexpected fees or delivery delays can be flagged and addressed in real time, turning frustration into loyalty. Most firms still operate reactively, in part because data remains siloed across payments, logistics and support and closing these gaps could sharply reduce call volumes.

2. Hyper-Personalized Support

Consumers now expect service that reflects their history and preferences. AI can tailor repayment options, loyalty incentives, or offers based on real-time data. Walmart, for example, has deployed AI-powered personalization tools to refine its app and eCommerce experience. Predictive analytics can also flag anomalies that suggest fraud or disputes, thereby reducing chargebacks. Yet many retailers still rely on generic scripts.

3. Multilingual, 24/7 Coverage

Global commerce does not keep office hours. AI chatbots and voice systems provide round-the-clock, multilingual support. New multimodal systems can handle voice, text, and even images, creating richer customer interactions. PYMNTS has reported that customers value this always-on flexibility, but many firms still lean on nine-to-five call centers or outsourced night shifts.

4. Sentiment Detection and Emotional Intelligence

Speed matters, but empathy builds loyalty. AI can read tone and phrasing in real time, alerting human agents when a customer is upset. This hybrid model ensures efficiency without sacrificing trust. Rezolve’s Brain Suite applies empathy-driven AI to reduce cart abandonment, which accounts for nearly 70% of lost online sales. Yet sentiment detection remains rare in many call centers.

5. Insights Beyond the Call Center

Complaints can expose flaws in checkout flows, packaging or design. AI can analyze these patterns, turning customer service into a source of business intelligence. Google’s Vision Match tools, for example, feed insights from shopping behavior back into product strategy. Few enterprises close this loop.

ROI as the Deciding Factor

For executives, ROI is the real test. Projects that fail to deliver lower handle times, better satisfaction scores, or reduced churn rarely scale. “AI as with any new technology risks adoption and integration without a clear strategic alignment,” Muthukrishnan warned. “Too many pilots or implementations can lead to a fragmented focus.”

“We’re already in market with our AI agent for autonomous and scripted self-service,” Todd Fisher, CEO and co-founder of CallTrackingMetrics, told PYMNTS.

“In a recent survey, 72% of respondents rated Webex AI Agent as equal, if not better, than a human agent. And our customers have reported an 85% reduction in agent call escalations, a 22% reduction in average handle time, and a 39% increase in CSAT [customer satisfaction] scores.”

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AI Research

Artificial intelligence (AI)-powered anti-ship missile with double the range

Published

1 hour ago

September 11, 2025

John Keller

Questions and answers:

What is the primary feature of the LRASM C-3 missile compared to earlier variants? It has nearly double the range of previous versions, with a range of about 1,000 miles, compared to 200 to 300 miles for the C-1 and 580 miles for the C-2.
How does artificial intelligence enhance the LRASM C-3’s capabilities? AI helps the missile with autonomous mission planning, target discrimination, and attack coordination, adjust flight paths based on real-time data, identify and track moving targets, and adapt to changing conditions like jamming and interference.
What can launch the LRASM C-3 missile? U.S. Air Force B-1B bombers, Navy F/A-18E/F Super Hornets, and F-35 Lightning II jets, with possible future launches from Navy ships and attack submarines.

PATUXENT RIVER NAS, Md. – U.S. Navy surface warfare experts are asking Lockheed Martin Corp. to move forward with developing the new LRASM C-3 anti-ship missile with double the range of previous versions.

Officials of the Naval Air Systems Command at Patuxent River Naval Air Station, Md., announced a $48.1 million order last month to the Lockheed Martin Missiles and Fire Control segment in Orlando, Fla., for engineering to establish the Long Range Anti-Ship Missile (LRASM) C-3 variant.

The subsonic LRASM is for attacking high-priority enemy surface warships like aircraft carriers, troop transport ships, and guided-missile cruisers from Navy, U.S. Air Force, and allied aircraft.

LRASM is designed to detect and destroy high-priority targets within groups of ships from extended ranges in electronic warfare jamming environments. It is a precision-guided, standoff anti-ship missile based on the Lockheed Martin Joint Air-to-Surface Standoff Missile-Extended Range (JASSM-ER).

1,000-mile range

The LRASM C-3 variant has a range of nearly 1,000 miles, compared to the 200-to-300-mile C-1 variant, and 580-mile range of the LRASM C-2 variant.

LRASM C-3 also introduces machine learning and advanced artificial intelligence (AI) algorithms to enhance autonomous mission planning, target discrimination, and attack coordination, even amid intense electronic warfare (EW) jamming.

The C-3 also can exchange information from military satellites, and has an enhanced imaging infrared and RF seeker for survivability and target identification.

The C-3 also can be launched form the Air Force from B-1B strategic jet bomber, as well as the Navy F/A-18E/F Super Hornet jet fighter-bomber and the F-35 Lightning II attack jet. Navy leaders also envision using the Navy MK 41 shipboard vertical launch system with the LRASM C-3, and are considering options to launch the missile from attack submarines.

Tell me more about applying artificial intelligence to missile guidance …

Applying artificial intelligence to missile guidance will enhance precision, adapt to dynamic environments, and improve decision-making in real-time. AI can help missiles navigate autonomously by using real-time data from radar, infrared sensors, and GPS to adjust flight paths. AI also can help missiles visually identify targets from images or video feeds, and not only enhance the missile’s ability to recognize and track moving targets, but also to predict and follow moving targets even if they change direction or speed. Using AI, missile guidance systems can make real-time adjustments to their trajectory based on changing conditions like wind, RF interference, and jamming. Missiles also may use AI to other weapons in swarm tactics, and operate effectively against countermeasures.

Helping to extend the LRASM C-3’s range are an advanced multi-mode sensor suite; enhanced data exchange and communications; digital anti-jam GPS and navigation; and AI and machine learning capabilities.

The missile’s multi-mode sensor suite is expected to blend imaging infrared and RF sensors to help the weapon identify and attack targets. Its communications will have data links for secure real-time communication with satellites, drones, and strike aircraft.

Digital anti-jam GPS and navigation will provide midcourse guidance to target areas far beyond the effective range of traditional systems. AI and machine learning, meanwhile, should help the missile identify targets and plan its routes autonomously. The LRASM C-3 version should enter service next year.

On this order, Lockheed Martin will do the work in Orlando and Ocala, Fla.; and in Troy, Ala., and should be finished in November 2026. For more information contact Lockheed Martin Missiles and Fire Control online at https://www.lockheedmartin.com/en-us/products/long-range-anti-ship-missile.html, or Naval Air Systems Command at www.navair.navy.mil.

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