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Open to discussions on Fractal Risk Intelligence partnerships, consulting engagements, and senior roles at the intersection of capital markets and frontier AI.
I am Founder and Principal Researcher at Capital Markets AI, building agentic AI platforms and publishing original research at the intersection of institutional risk and frontier AI. My career spans 20+ years in capital markets — Global Head of Technology Presales at Calypso Technologies ($50M ARR, 10 Solutions Architects across NA/EMEA/APAC), Director, Product Engineering at EZOPS (cloud-native AI reconciliation, 25-member engineering team, ISO-27001 attained in 3 months), and a recently completed Principal Software Consultant engagement at Fidelity Investments. I hold certificates in Quantitative Finance (Paul Wilmott) and Risk Management (Nassim Taleb) and am pursuing the Claude Certified Architect credential.
Two SSRN working papers, an open Treasury PoC pairing Mandelbrot’s MMAR with LLM-emitted regime signals, and three open-source projects on the Anthropic Claude API form the core of that work. Most recently, an architectural paper extends the same framework to the quantum compute layer — a seven-layer design for deep-tail risk that runs today on two production-ready streams, multifractal mathematics and bounded LLM orchestration, and is structured to absorb quantum compute at the tail-sampling layer if and when it reaches commercial viability. It now pairs that architecture with an empirical proof-of-concept on the loading-layer question — which reopened, rather than closed, as a candidate for quantum advantage; no quantum-advantage claim is made.
My core interest is the intersection where institutional capital markets domain depth meets frontier AI capability — and, increasingly, the quantum compute architectures that the next decade’s risk computation will run on. I believe fractal risk intelligence — combining Mandelbrot’s MMAR with LLM orchestration via Model Context Protocol — represents the most rigorous and verifiable reasoning domain available for training frontier AI models. That same verifiable substrate is where that evolving compute stack — quantum included — will ultimately be tested, and extending the framework to a quantum-augmented architecture is the natural next step. I am building the case through open-source platforms, two SSRN working papers, an architectural perspective on quantum-augmented risk, and direct engagement with AI labs and institutional risk teams who are asking the same questions from opposite sides.
The thesis: institutional capital markets fractal risk is the hardest reasoning domain in finance — and uniquely well-suited for training frontier AI models that can reason under uncertainty with provable correctness.
Open to discussions on Fractal Risk Intelligence partnerships, consulting engagements, and senior roles at the intersection of capital markets and frontier AI.
