Stuart Frost, the Chief Executive Officer of Geminos, is a visionary leader in the fields of data management, industrial IoT (IIoT), and artificial intelligence. With over three decades of experience, he has founded and led multiple successful technology startups, shaping the future of industrial digitization.
A graduate of Nottingham University with a degree in Electronic and Computer Engineering, Frost began his entrepreneurial journey with SELECT Software Tools, which he guided to a successful NASDAQ IPO in 1996. His expertise in building data-driven companies has resulted in major achievements, including the founding of DATAllegro, which was later acquired by Microsoft for $275 million. His work has consistently pushed the boundaries of analytics and automation, driving efficiency and innovation across industries.
Now at the helm of Geminos, he is pioneering advancements in Causal AI, a groundbreaking approach to industrial data analysis. Through his leadership, Geminos is tackling complex challenges in industrial intelligence, offering AI-driven solutions that enhance operational decision-making. With a proven track record of turning ambitious ideas into industry-defining companies, Frost continues to shape the future of AI-powered analytics, solidifying his reputation as a key influencer in the tech sector.
Describe how you got into the business of Causal AI.
I founded a number of AI startups in the 2010s that targeted industrial decision-making. It was very difficult to get consistent results that stood the test of time. This problem has been highlighted by IBM's finding that 87% of industrial AI projects fail. That simply isn't good enough.
I set out to solve this problem, and in founding Geminos, I quickly realized that the core problem is that traditional AI is based on correlations. Hence, I started to research the field of Causal AI and came across the work by Prof Judea Pearl at UCLA. That led to us deciding to build a comprehensive, end-to-end platform to bring the power of Causal AI to industrial decision-making.
Looking back on the last five years, what has been the highlight of your career?
Seeing the light go on with major customers has been incredibly rewarding. There’s a distinct moment when they realize the limitations of traditional machine learning and deep learning models—when they see firsthand how Causal AI fundamentally changes the way they approach data-driven decision-making.
Once you've started using Causal AI, there's no going back. It’s simply a more effective, transparent, and scalable way to build AI-driven systems. The most gratifying part is hearing from customers who tell us that they now rely on Causal AI as an essential tool in their operations, helping them drive better business outcomes with greater confidence.
How do you think your background has impacted the success of Geminos?
Having founded over 30 startups and run four of them as CEO, I'm what you might call a seasoned entrepreneur. A key tenet I've learned and live by in business is to focus on customer success, rather than investor-driven milestones such as raising the next round of funding.
If your customers love your product, everything else will work out just fine. Along the same lines, I'm also less focused on being a pure product company these days. It's important to be flexible and deliver a solution to customers that they can buy and successfully ingest. If that means offering more consulting, then so be it.
How do you incorporate new strategies/tactics in Geminos?
A big part of my job is to continually refine and expand the product vision, ensuring it aligns with both industry needs and technological advancements. That vision can’t be developed in isolation—it has to be shaped by real-world challenges. That’s why I make it a priority to engage directly with customers, understanding their pain points, workflows, and evolving expectations.
These conversations provide insights that drive our innovation strategy, allowing us to stay ahead of the curve. Rather than getting caught up in internally focused issues, I focus on ensuring that our solutions remain practical, adaptable, and impactful for the businesses that rely on them.
Describe a few accomplishments you are most proud of.
One of the achievements I’m most proud of is building a company culture that attracts and retains top talent—so much so that many employees have chosen to work with me repeatedly over the years, in some cases for more than three decades. Creating an environment where people feel valued, challenged, and genuinely excited to come to work has always been a priority for me.
It’s incredibly satisfying to hear from employees that they consider the teams and workplaces I’ve built to be the best they’ve ever experienced. Beyond that, I take great pride in the success of the startups I’ve founded—not just in terms of financial outcomes, but in their ability to drive real-world impact, solve complex problems, and push the boundaries of what’s possible in AI and industrial intelligence.
What motivates you to continue doing what you do?
Addiction! I’m hooked on the challenge of identifying complex customer problems and leading a team to solve them in ways that haven’t been done before. There’s something incredibly satisfying about seeing an idea evolve into a real-world solution that drives measurable impact.
Every time we help a company unlock new efficiencies, improve decision-making, or transform the way they operate, it fuels my passion to push even further. Technology is constantly evolving, and so are the challenges industries face—that dynamic keeps me engaged and excited about what’s next.
What’s ahead for your next endeavor?
I'm 100% focused on Geminos.
Anything that you are currently working on that you would like to share?
Our major initiative right now is to blend Causal AI with LLMs to create a Causal Knowledge Graph (CKG) that can be used to capture and share information across a large enterprise. That's been a dream of many startups over the last couple of decades, but earlier technologies weren't good enough. We are delivering on that vision.
We like to draw a parallel with Daniel Kahneman's "Thinking, Fast and Slow", with the LLM (backed up by the CKG) playing the part of 'fast' thinking and Causal AI delivering 'slow' thinking. The former helps to spread information across the organization via a natural language interface, while the latter addresses critical decisions that need to be truly data-driven. It represents a massive breakthrough and we're getting a great response from initial customers.
