Atomic Canyon Aims to be the ChatGPT for the Nuclear Industry, Secures $7 Million Seed Funding

As tech companies increasingly invest in nuclear power to meet the energy demands of their AI initiatives, Atomic Canyon is emerging as a key player in modernizing the nuclear industry through artificial intelligence. Trey Lauderdale, the founder, envisions AI as the catalyst to accelerate nuclear processes, addressing the urgent power needs of data centers.

Lauderdale’s connection to nuclear energy began in San Luis Obispo, California, where he observed the significant presence of Diablo Canyon Power Plant employees in his community. This exposure revealed to him the vast quantities of documentation inherent in nuclear power plants—Diablo Canyon alone holds approximately 2 billion pages. Drawing from his experience as a serial healthcare entrepreneur, Lauderdale recognized the potential of AI to streamline document management within the nuclear sector.

Founded a little over a year and a half ago and initially self-funded, Atomic Canyon utilizes AI to assist engineers, maintenance technicians, and compliance officers in efficiently locating necessary documents. The startup secured a contract with Diablo Canyon in late 2024, which sparked interest from other nuclear power companies. This pivotal moment prompted Lauderdale to seek external funding to scale the company’s operations.

Atomic Canyon has successfully closed a $7 million seed round, spearheaded by Energy Impact Partners, with participation from Commonweal Ventures, Plug and Play Ventures, Tower Research Ventures, Wischoff Ventures, and existing angel investors, as exclusively reported by TechCrunch.

Initially, Atomic Canyon’s AI engineers encountered challenges with existing models, noting that the AI tended to hallucinate when processing nuclear-specific terminology. “We quickly realized the AI hallucinates when it sees these nuclear words,” Lauderdale explained. “It hasn’t seen enough examples of the acronyms.”

To overcome this, Lauderdale secured access to Oak Ridge National Laboratory’s supercomputer, the second fastest globally, obtaining 20,000 GPU hours to develop a more accurate AI model tailored for the nuclear industry.

Atomic Canyon’s models now employ sentence embedding, making nuclear power plant documents searchable through retrieval-augmented generation (RAG). This approach leverages large language models (LLMs) that reference specific documents to minimize inaccuracies when responding to queries.

Currently, Atomic Canyon is concentrating on document search to minimize potential risks. “One of the reasons we’re starting generative work around the titles of documents is because getting that wrong might cause someone a little frustration. It doesn’t put anyone at risk at the plant,” Lauderdale said.

Looking ahead, Lauderdale envisions Atomic Canyon’s AI drafting initial versions of documents with complete references, emphasizing that human oversight will remain crucial. He stated that search is “the foundational layer,” and added, “You have to nail the search.” Given the extensive documentation in the nuclear industry, “we have a long runway in search alone,” he concluded.