Trey Lauderdale’s fascination with nuclear energy traces back to San Luis Obispo, California, where he often encountered people working at the nearby Diablo Canyon Power Plant. Through conversations with plant employees, he discovered the massive challenge these facilities face managing vast archives, with Diablo Canyon alone holding close to 2 billion pages of documentation.
Drawing from his experience as a healthcare entrepreneur, Lauderdale believed artificial intelligence might offer a way to navigate this overwhelming mountain of paperwork. In response, he launched Atomic Canyon about a year and a half ago, initially funding it himself, to develop AI-driven tools to help engineers and compliance teams retrieve vital documents more efficiently.
The startup secured its first agreement with Diablo Canyon in late 2024 and soon found itself fielding interest from additional nuclear operators. That growing demand made it clear to Lauderdale that more financial backing was necessary, and Atomic Canyon recently closed a $7 million seed round led by Energy Impact Partners, with input from a variety of venture funds and angel investors.
Harnessing Supercomputers for Smarter Search
At the outset, Atomic Canyon’s team experimented with various AI models, but they ran into trouble. Existing language models struggled to understand nuclear plant terminology, often generating irrelevant or misleading responses due to a lack of industry context.
To bridge this gap, Lauderdale connected with Oak Ridge National Laboratory, home to one of the world’s fastest supercomputers. The lab was interested in the concept and granted Atomic Canyon access to 20,000 GPU hours, giving the fledgling company the computing muscle needed to tailor AI models for nuclear industry specifics.
Atomic Canyon’s approach revolves around advanced document indexing using sentence embedding and retrieval augmented generation. This method allows large language models to respond to queries by grounding their answers in the actual content of nuclear plant archives, which helps minimize the risk of producing inaccurate information. For more detail on best practices, regulated sites such as nuclear power plant document management are often used to supplement these AI-driven tools.
The company is currently focusing on search tools that help users locate documents reliably, a task Lauderdale considers less risky than full generative AI. According to him, mistakes in titling or finding documents might be annoying, but they do not compromise safety at a nuclear facility.
He sees a future where Atomic Canyon’s AI systems draft entire documents and include references, but he insists that human oversight will always be essential at every stage. For now, perfecting search is the priority. Initiatives like Atomic Canyon’s are just one example of the broader transformation, as seen in projects harnessing AI in nuclear industry.
With billions of documents still to be indexed and understood, Lauderdale believes that there is substantial potential just in making plant records accessible. This foundational effort is expected to keep his company busy for the foreseeable future.
