Artificial intelligence has entered a zeitgeist that mirrors the early days of the internet, with a vast sense of possibility and exponential pace leading the movement, and the companies that define the future of AI may not be the ones leading headlines today.
Allan Grosvenor, founder of MSBAI (Microsurgeonbot Inc.) and co-founder of Tam Fortis Solution and Nexcavate, recognizes this shift and predicts an opportunity where reliable AI for digital engineering and operations will be the foundation for tomorrow’s most critical industries, whether it's aerospace, energy, or space infrastructure.
“I think what we’re seeing in AI right now is very much like the beginning of the internet,” Grosvenor reflects. “At the time, there were some companies that made impacts early on, but they didn't necessarily become the giants we know today. The first browsers that existed aren't even used today. We’re at a similar inflection point with AI, where some companies might disappear, and entirely new ones will become the titans; that’s what I predict for ours.”
MSBAI, an autonomous expert workflow system, focuses on what Grosvenor calls “digital engineering and operations,” which involves creating computer models of complex systems, whether aircraft components, reactors, or industrial facilities. Aside from building these systems, MSBAI runs countless simulations to design better, safer, and more efficient prototypes before they ever leave the digital space.
Grosvenor points out that traditional design cycles move slowly and are costly, but MSBAI incorporates an AI-driven simulation strategy, through which a plethora of variations could be modeled in seconds.
“Digital engineering entails building computer models of a product design, varying the design properties and running simulations to evaluate their impact on performance, manufacturability, cost, etc.” He explains, “If you don’t take advantage of what you can accomplish in virtual prototyping, a project can quickly go over budget and over time, or even fail. We built an AI-driven autonomous system that sets up models, runs computations, and performs sophisticated analysis for designers and operators – minimizing the laborious and time-consuming effort that can otherwise become a barrier to productivity.”
Reliability is a theme that dominates each of Grosvenor’s endeavors. Unlike conventional Large Language Models (LLMs) that autoregressively predict tokens in a pattern-matching approach that can result in a lot of errors, MSBAI has built a hierarchical intelligence strategy to maximize performance and reliability. From graph network-based strategies for navigation, to semantic latent embedding training of Joint Embedding Predictive Architectures, to the Distributed Proximal Policy Optimization they employ in Reinforcement Learning and Model Predictive Control for planning, Grosvenor’s team have built and validated a system for serious industrial autonomy where ‘hallucination’ is unacceptable whether you’re designing an aircraft, performing space traffic control services, or operating a nuclear reactor.
On the operations side of things at MSBAI, Grosvenor mentions there’s a “part of the puzzle” that needs to be navigated for seamless workflows, and that is the complex time series: patterns that evolve in ways making it difficult for humans to track or predict. These problems are present everywhere, in stock market movements, in satellite trajectories, in subtle nuclear instabilities, or in a fusion facility. Yet, MSBAI enables efficient anomaly detection and learns from them, which helps to curate predictions and even autonomous responses.
This capability makes MSBAI a natural fit for challenges such as space domain awareness, understanding, and preventing satellite collisions, and identifying unusual or suspicious behavior in orbit. Grosvenor has also applied it to fusion research facilities, where detecting anomalies early can prevent catastrophic failures. The technology is also helpful in navigating nuclear reactors in remote locations, where it can trigger alerts or even safe shutdowns.
MSBAI has also become a springboard for Grosvenor’s other ventures, including Nexcavate, which applies the same technology to an overlooked bottleneck in the economy: mining permits. Grosvenor points out that often, it can take a significantly long time to move from exploration to production. Witnessing this trend, he saw how his AI could streamline permitting by addressing the core inefficiencies that drag timelines into decades.
Growing up in a small town north of Toronto, Grosvenor's path to his visionary career was inspired by the science fiction he had immersed himself in. Watching these films made him want to bring these ideas to life, leading him to build rockets, robotic arms, and machines from salvaged parts at his family’s workbench. That passion carried him through engineering and aerospace studies, into work with supercomputers, and ultimately into mastering AI. And it’s this body of work that has been the catalyst for MSBAI, Nexcavate, and Tam Fortis, rooted in the philosophy of innovation that insists on reliability, scale, and adaptability.
“Today’s AI is just the beginning. The companies that will endure will be the ones solving fundamental problems in ways people can trust. That’s exactly why we built what we built.”
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