Fantora raises $100 million to create targeted companies as material AI dominance ends the era of shared software solutions

Fantora raised $100 million in its first external investment, led by SilverSmith Capital Partners, in a move that reflects a fundamental shift in startup studio models toward physical AI sectors. The company, founded four years ago as UB Labs, has abandoned the traditional philosophy of developing technology products sold to the open market and has chosen to devote its engineering capabilities to building specialist companies that serve exclusively the major industrial enterprises of its clients. This shift means that every startup created inside the lab is designed to solve a specific operational problem for a particular founding partner, with the option of being fully absorbed into the partner’s internal structure, away from commercial competitors.

Building proprietary technical ownership precedes broad commercial expansion in heavy automation fields.The company launched in 2022 through an initial founding partnership with automaker Porsche, developing several specialist firms for them before expanding its activities to include prominent founding partners such as Alaska Airlines, GB Hunt for freight transport, Wabash, and the TD G Group, owner of the Ashley Venture brand, in addition to new clients in industrial manufacturing and the oil and gas sector. Fantora is based in California, sharing office space with the venture-capital firm UB Partners without any financial link between them, and the new funding gives it the ability to expand a dedicated merger-and-acquisition track that ensures partner institutions can invest in the startup, use it first, and ultimately integrate it.

The CEO and founder John Colt says that the focus on physical AI forced this strategic change decisively, because massive industrial firms listed in the Fortune 100, when they begin upgrading their heavy machinery to become autonomous, need to assert full sovereignty over the AI layer that controls that physical equipment. Previously, the company had to drop highly important strategic ideas and projects, such as an automation proposal it developed for the freight company GB Hunt, because the client categorically refused to let that intellectual property enter the open market. Rather than waste those high-value opportunities, the new model ensures that institutions keep the intelligent systems secret operationally, protected from leakage.

This shift puts digital-transformation officers and industry leaders in the Gulf, Egypt and the Levant in front of a different regulatory pattern for handling automation of critical assets. In regional markets that are investing billions of dollars to modernize mining, petrochemicals, ports and renewable-energy complexes, the risks of relying on off-the-shelf AI platforms that recycle operational data or produce solutions similar to regional competitors become apparent. The lesson here is that owning sovereignty over the physical-AI system is not achieved by buying packaged software suites, but by adopting proactive acquisition pathways that design solutions around the enterprise’s own infrastructure and embed them organically into operating capital. If you manage an institutional investment portfolio or lead an engineering sector, the bet today requires owning the code and sensor data within your own system rather than renting it from external parties that could strip away your competitive edge tomorrow.