AI Strategist · Enterprise Architect · Author
I build the systems that make enterprises intelligent. From fragmented data to deployed AI agents — I translate complexity into compounding operational advantage.
I spent a decade at the intersection of finance, data, and technology — building the infrastructure that lets organizations make faster, smarter decisions. At MIO Partners (McKinsey's hedge fund office), I engineered ML-enhanced portfolio tools and broker data pipelines for Goldman Sachs, Morgan Stanley, and JPMorgan. At BNY Mellon, I led $3M+ AI and data governance programs modernizing one of the world's largest financial institutions.
Today I work with organizations navigating the shift from fragmented legacy systems to unified, agent-powered intelligence. My focus: making AI operational, not theoretical.
How a global financial institution eliminated data silos across 12 lines of business and deployed autonomous agents to handle end-to-end reconciliation and regulatory reporting — reducing manual effort by over 60%.
End-to-end design and build of a modular operating system spanning acquisitions, compliance, analytics, and investor reporting — replacing 14 disconnected tools with a single intelligent platform.
Redesigning broker data pipelines, SLA structures, and MNPI controls for a Tier 1 asset manager — creating a governance framework adopted across the enterprise's front-to-back investment lifecycle.
Designing and deploying machine-learning enhanced portfolio management tools and real-time broker data visualization — cutting manual data prep time by 50% for investment teams managing multi-billion dollar funds.
When a financial institution runs $2 trillion in assets across dozens of business lines, its data doesn't just live in silos — it lives in different languages, different schemas, different time zones. Reconciliation is manual. Reporting is late. Compliance is reactive.
This is the environment I walked into at BNY Mellon. Here's how we approached the architecture — and what deploying AI agents across accounting and reporting actually looks like in practice.
Read the Full Case StudyThe problem isn't the model. It's the data underneath it. Here's what I've seen — and what to fix first.
AI StrategyBeyond the hype — a practical breakdown of how autonomous agents get deployed in regulated financial environments.
Enterprise AIIt's an organizational one. The firms that get this right don't win on stack — they win on governance.
Data ArchitectureContact
Whether you're modernizing a legacy system, deploying your first AI agents, or need a strategic partner who's done this at institutional scale — I'm open to conversations that matter.