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, a startup that uses AI to shadow human experts in heavy industries such as energy and replicate their specialized workflows into autonomous agents, has raised $4.6 million in seed funding, the company tells 禁漫天堂 News exclusively.

led the raise, which included participation from , , , and St. Elmo Venture Capital, the investment arm of customer . It brings the Raleigh, North Carolina-based startup鈥檚 total raised to $5.35 million since its 2023 inception.

The idea for Cloneable traces back to a bottleneck its founders encountered years earlier while working in the field.

In 2019, as wildfires ravaged California, co-founders , and 鈥� founding employees at drone company 鈥� were deployed to help inspect critical infrastructure. Their team sent out 150 drone pilots to survey thousands of miles of transmission lines.

But reviewing that data proved far less scalable.

When Reich visited a utility command center weeks later, she saw hundreds of workers manually scrubbing through video footage, while only a handful of experts knew what to look for.

“It was an ‘aha’ moment,” she recalled. 鈥淲e realized this cannot be the way. If we know what the expert is looking for, why can’t we just clone that expertise?鈥�

The startup鈥檚 founders realized that heavy industries 鈥� energy, oil and gas and agriculture 鈥� face a 鈥渒nowledge crisis鈥� as experienced workers retire faster than they can be replaced.

鈥淔or every young worker entering the energy workforce, 2.4 experienced ones are walking out the door toward retirement. And it’s happening right as energy demand is set to double by 2050,鈥� Reich, the company鈥檚 CEO, told 禁漫天堂 News.

Cloneable aims to capture and preserve that kind of institutional knowledge.

In February 2025, it launched Cloneable Field for automated infrastructure inspection targeting the energy sector.聽 Alongside the fundraise, the company is now launching an agentic product that codifies expert knowledge and deploys it as scalable AI agents.

The funding will also support expansion into infrastructure-heavy industries such as public utilities, vegetation management, construction, rail, mining, agriculture and manufacturing.

鈥淭hese are markets chronically underserved by point solutions,鈥� Reich said. 鈥淣o one has combined in-field data collection with agentic automation at the scale these industries require.鈥�

That includes workers鈥� judgment and institutional knowledge not captured in documentation or general AI models, according to Reich. 鈥淐loneable automates workflows that have traditionally been considered too complex for automation,鈥� she said.

The company claims that a process that typically takes a human engineer eight hours, such as structural calculations for a project where a firm is going to replace, upgrade or install 25 utility poles can be completed by a Cloneable agent in under two minutes.

鈥淎 single engineer can process roughly 4,500 to 5,500 poles a year before they hit a capacity ceiling,鈥� Reich said. 鈥淥ur agent runs at 2 million to 3 million poles a year. For a mid-size engineering firm with five to 10 people spending half their time on this work, that’s $115,000 to $312,000 a year in labor that’s not being redirected to higher-value work.

She added: 鈥淭his could be the difference in entire towns being connected to fiber or not over the next 12 months.鈥�

From the field to the back office

The startup says it grew ARR 100x between February and the end of 2025. It has dozens of customers, including , , , and , as well as , which is expanding the 鈥渆xpert cloning鈥� model to livestock and food supply.

Unlike generic AI that requires coding or clean data, Cloneable鈥檚 platform 鈥渟hadows鈥� experts. AI watches an expert perform a workflow, such as a complex utility-pole design. It then captures audio and documentation from the expert in real time. Next, it turns that contextual experience into an AI agent capable of executing the same task.

鈥淥ur differentiation is a decade of lived experience in how these industries actually operate, and the proprietary data and workflows we’ve captured from being inside these companies,鈥� Reich said, adding that everything is highly specific 鈥� from tools to how they鈥檙e configured per customer.

Large foundation model companies focus on the model itself, she said.

鈥淲e’re focusing on a framework that leverages different model types, including small, specific ones,鈥� she said. 鈥淲e clone our customers鈥� knowledge and experience into a small model, which makes it extremely cost-effective to do their work. We’ve built it so all the agent needs to know is: my company, my rules, my industry, my tools.鈥�

Cloneable makes money from its field offering through seat-based licenses per field collection device. With its new agent, charges are per-token and usage-based.

Solving for both data and the agents to act on it

, a partner at Congruent Ventures, said her firm鈥檚 investment in Cloneable was the culmination of many conversations with founders about AI adoption in legacy industries.

鈥淲e’ve seen companies focus either on data capture with complex, expensive, purpose-built hardware 鈥� or on agentic AI for the back office where they struggle to get the high-fidelity data needed to power those agents,鈥� she wrote. 鈥淐loneable has solved both.鈥�

She said the firm is betting on Cloneable鈥檚 team to bring AI to industries where horizontal solutions 鈥渁ren鈥檛 deep enough.鈥�

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