An expert team of engineers, AI researchers, risk quants, and builders who believe autonomous systems should be trustworthy by construction — and provable.
Quant Trader, Mathematician, Investor and serial entrepreneur having helped lead/participated in multiple $billion+ exits. Founder and Director of XBTO, proprietary trading firm. Graduate of Columbia University.
Previously at NVIDIA, Thomson builds the software infrastructure for secure, compliant, and accountable enterprise AI agents. His work focuses on making agents first-class participants in business operations through controlled authority, delegation, and automation. Holds an M.S. in Artificial Intelligence from Colorado State University.
Electrical engineer with experience spanning safety-critical systems, Microsoft, and quantitative trading, where he built ML systems trading up to $3.5B daily. He leads Mace Ai’s alignment and mechanistic interpretability research and architected Fisher’s evolutionary vulnerability discovery pipeline. Graduate of the University of Cape Town and Harvard.
Founder and CEO of two successful quant trading companies. Started his career trading and structuring equity derivatives on Wall Street. Brings technical and management expertise to Mace Ai's client execution and commercial businesses. Graduate of Macalester College and Harvard University, and was enrolled in the London School of Economics General Course Programme.
Previously VP of Regulatory Affairs at Power TakeOff, acquired by Lime Rock New Energy. Anna founded the Strategic Energy Management Collaborative and develops AI adoption strategies for the energy sector. She is the author of Good Work (Springer) and an Association of Energy Engineers award winner. At Mace Ai, she leads new initiatives from concept to handoff and drives company priorities.
Previously Director of Marketing at XBTO, co-founded a successful marketing agency and a social trading platform, with earlier roles across venture capital and real estate. Brings category positioning and go-to-market expertise to Mace Ai's commercial growth.
We started mace because the industry’s answer to AI risk was to deploy fast and hope. We think trust should be something you build, prove, and maintain. Fisher is where we started; Deep Model Trust is where we’re going. Mace Ai’s work spans adversarial agent red teaming through Fisher, independent model red teaming, and agentic swarm defense to help companies prevent harm from their own multi-agent systems.
Talk to Mace Ai about a model, configured agent, or multi-agent system.