Founders Everywhere: Rob May
Rob May is the co-founder & CEO of Neurometric, a startup providing automated inference orchestration for multi-model AI systems.
Welcome to Founders Everywhere, where we highlight the incredible people behind the companies we’ve backed at Everywhere Ventures, a global pre-seed fund supported by a community of 500 founders and operators.
GPT-5, the LLaMA family, Claude, and Gemini are some of the most widely used AI systems today. AI models don’t just generate answers, they “think” in different ways depending on how you guide them. These guidance methods, often called “thinking algorithms”, influence how carefully a model reasons, how thoroughly it checks its work, and how much time or compute it spends on a task. The challenge is that every model and algorithm behaves differently, and choosing the right combination can dramatically change the results. Neurometric makes this easy by automatically picking the best models and reasoning strategies for each use case, giving companies better performance at a lower cost and without the guesswork.
The founding team brings deep experience across AI, systems engineering, and company-building, with ten successful startups between them. CEO Rob May is a veteran technology entrepreneur and investor with over a decade of experience building and scaling AI and infrastructure companies. He teamed up with Byron Galbraith, who has a PhD related to reinforcement learning and is a former colleague from the AI startup Talla. Calvin Cooper joined the team through mutual connections and Dave Rauchwerk came on as a freelancer before quickly becoming a co-founder. Together, they bring a unique combination of technical expertise and operational experience to Neurometric.
Why Neurometric and why now?
About a year ago, the first reasoning model launched, OpenAI o1. I started digging into how it actually worked. That’s when it became clear that the space of ‘thinking algorithms’ was very large, mostly unexplored, and far cheaper to innovate in than training bigger and better models. You don’t need hundreds of millions of dollars.
Through our research and market observations, we became convinced that companies would soon move toward combining multiple models, not just relying on one, and that there was an opportunity to help engineering teams design and optimize their AI systems with the best algorithms and model choices for their goals, whether that’s accuracy, latency, cost, or consistency. That’s ultimately what led us to start Neurometric.
Tell us about some recent milestones that Neurometric crushed.
We’re launching the first leaderboard that compares thinking algorithms, not just models. Unlike existing leaderboards, ours shows you which algorithm works best for different kinds of reasoning tasks, so you can make informed choices, design better pipelines, and understand the tradeoffs behind every decision. If you’re building an AI system and want to optimize for real-world performance, our tool is designed to help you pick the best models and algorithms.
We were just featured in TheDeepView, a leading AI newsletter with 600,000 subscribers.
What’s Neurometric’s North Star?
Right now, our North Star is really about awareness. I want people to understand that there’s a whole layer of AI optimization that companies aren’t even thinking about yet. We’re focused on driving visits to our leaderboard and educating the ecosystem. We believe that highlighting the performance impact of thinking algorithms will drive meaningful adoption of what we’re building.
What sets Neurometric apart?
Our team is deeply experienced. Unlike most AI companies that focus narrowly on data science, all of us are “systems people.” We’ve worked at various layers of the tech stack and care about total system optimization. We aren’t attached to any particular model or approach; we’re building tools that let teams figure out what really works best based on their own data and needs.
Any favorite books?
I highly recommend Why Machines Learn: The Elegant Math Behind Modern AI by Anil Ananthaswamy. It’s a fantastic technical deep dive. For fun I recently read I’m Not Trying to Be Difficult: Stories from the Restaurant Trenches, by Drew Nieporent, which tells the behind-the-scenes story of New York’s legendary restaurants.
Fun Fact:
I’m the co-host of the show AI in NYC, where we have conversations at the intersection of AI, technology, and New York City.
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