About Us
Our Approach
Praxis is built around a simple idea: most of the value in AI comes from how it’s applied, not from the technology itself.
Over the past few years, businesses have been flooded with new tools and possibilities. But turning those possibilities into something that actually improves day-to-day operations is a different challenge entirely. That gap—between what AI can do and what it actually does inside a business—is where we focus.
The name Praxis comes from philosophy. It refers to the process of turning theory into action. That idea shapes how we approach every project.
Rather than experimenting with AI in isolation, the goal is to design systems that fit naturally into how a business already operates. The emphasis is on making workflows more reliable, reducing manual work, and ensuring that important processes don’t depend on memory or ad hoc effort.
In practice, this often means rethinking how work moves through an organization. That could involve automating back-office processes, building systems for customer outreach, organizing internal data, or replacing fragmented workflows with something more structured and consistent.
The approach itself is straightforward: understand how the business actually runs, identify where time and value are being lost, and build systems that address those gaps in a durable way.
About Me
My interest in AI did not start with coding. It started with philosophy.
I first became interested in the space after reading Superintelligence by Nick Bostrom. At the time, AI felt distant and theoretical. What drew me in was the question of what it would mean for machines to reason, make decisions, and operate in the world.
As an undergraduate at the University of Virginia, I focused on philosophy of mind and philosophy of AI, continuing to explore those questions from a more formal perspective.
That framing shifted as the technology began to catch up. When tools like ChatGPT started to emerge, AI moved from something abstract to something immediately practical. The question was no longer just what these systems are, but how they can be used.
After graduating, I pursued a Master’s in Commerce at UVA’s McIntire School of Commerce, where I focused on finance and analytics. During that time, I worked as a research assistant studying how AI can be applied in real business environments, with a focus on how it moves from theory into practice.
Since then, I have worked with small businesses to design and implement AI systems in real operating environments, where data is messy, constraints are real, and solutions actually need to hold up.
The common thread throughout has been simple. Taking ideas about AI and turning them into systems that work.