Nuclearn has secured $10.5 million in Series A funding to scale its artificial intelligence software across the nuclear power sector, betting that smarter automation can shave hours off paperwork, sharpen maintenance decisions, and keep gigawatts of carbon-free electricity online. The round was led by Blue Bear Capital with participation from AZ-VC, Nucleation Capital, and SJF Ventures.
Founded by industry veterans who cut their teeth at the Palo Verde Nuclear Generating Station, Nuclearn says its tools are already in use across more than 65 reactors worldwide. The company’s pitch is simple: bring modern data science and domain-tuned AI to one of the most procedure-driven, highly regulated slices of the energy system—without compromising safety.
Why nuclear wants AI now
Demand for round-the-clock clean power is surging as data centers, electrified transport, and heavy industry seek low-carbon baseload. Major tech firms have inked supply deals with reactor operators and advanced fission startups to lock in 24/7 electricity. At the same time, many existing plants are operating well beyond 30 years of age, and every hour saved on documentation, inspections, or outage planning helps sustain reliability.
According to the International Atomic Energy Agency, roughly 440 reactors supply about 10% of global electricity, with U.S. units routinely posting capacity factors above 90% per the Nuclear Energy Institute. Small efficiency gains can be material: for a large reactor, an unplanned outage can cost hundreds of thousands to over a million dollars per day in foregone generation, depending on market conditions.
What Nuclearn actually does
Nuclearn develops models trained on nuclear-specific terminology, procedures, and equipment hierarchies. The software automates routine document generation—think work packages, corrective action drafts, and procedure updates—before handing the final sign-off to licensed staff. It also assists with classification of condition reports, triage of maintenance tickets, and extraction of insights from decades of plant records, technical specifications, and vendor manuals.
The platform runs in the cloud for utilities that permit it, but can be deployed on hardware inside plant networks when cybersecurity or export-control constraints require. Many operators prefer that option to align with digital instrumentation and control policies and to preserve air-gapped environments.
A key feature is adjustable automation. Operators set thresholds that determine when the system drafts, recommends, or simply organizes information for human review. If confidence in an output is low or a task falls outside approved scopes, the workflow routes back to the appropriate engineer or supervisor. In practice, customers often treat the software like a junior analyst that accelerates the grunt work without changing who is accountable.
Safety, compliance, and guardrails
Regulators are watching closely but are not standing in the way of productivity tools. In current practice, the U.S. Nuclear Regulatory Commission treats most AI in plants as a decision-support tool rather than an autonomous control system, which means the licensed operator remains responsible for final decisions. That framing aligns with existing quality assurance regimes such as 10 CFR Part 50, Appendix B, which require traceability, verification, and auditable records.
Industry groups have been laying groundwork for broader adoption. The Electric Power Research Institute has published guidance on AI validation and human factors, while the IAEA has explored use cases ranging from predictive maintenance to fuel inspection analytics. Nuclearn’s emphasis on provenance—recording sources, model versions, and reviewer sign-offs—speaks to those expectations.
The business case in numbers
Plant staff spend significant time on documentation and data retrieval because nuclear work is procedure-centric by design. If AI can reliably pre-populate forms, reconcile equipment IDs, and surface the right technical basis in seconds, the labor savings compound across thousands of tasks each month. During refueling outages, when schedules compress and coordination is intense, even small reductions in rework translate into measurable schedule risk reduction.
Beyond paperwork, utilities see value in condition-based maintenance and anomaly detection. Combining sensor trends, work history, and operating experience can flag components that merit inspection before they fail. EPRI case studies in conventional generation suggest such approaches cut maintenance costs and unplanned downtime; Nuclearn is aiming to package similar benefits in a nuclear-ready, auditable workflow.
Investors and what’s next
Blue Bear Capital’s participation fits its thesis around software that improves asset productivity in energy and infrastructure. Nucleation Capital focuses on innovations that accelerate nuclear deployment, while AZ-VC and SJF Ventures bring regional and climate-tech experience. The new capital will likely go toward model expansion, safety and compliance features, and deeper integrations with utilities’ document control and asset management systems.
Competition is emerging as established vendors pilot AI add-ons and national labs advance digital twins under U.S. Department of Energy programs. But nuclear operators tend to buy what has already proven itself at peer plants, and Nuclearn’s reported footprint across more than 65 reactors gives it a credible reference base.
The thesis is not that AI will run reactors; it is that well-governed software can relieve skilled people of low-value tasks and sharpen their judgment when it matters. For a fleet under pressure to extend lifetimes, add capacity, and supply an increasingly digital economy, that may be the most practical path to impact.