Background

The Pattern

Every role I've held has been a version of the same problem: how do you take an investment thesis and build the team, systems, and capital infrastructure to actually execute it at scale?

I started my career at Vornado Realty Trust, acquiring and asset managing pieces of a $5 billion mixed-use institutional portfolio in Washington, D.C. That experience gave me something I didn't fully appreciate at the time — a front-row seat to how disciplined, institutional-quality investing actually works at scale. The rigor. The reporting. The systems that let a platform absorb billions without losing discipline.

At Aspen Heights Partners, I launched the firm's student housing acquisitions program and built a proprietary database of value-add targets across 91 university markets. That taught me the power of systematic deal sourcing — a discipline I've carried into everything since.

At Casoro Group, I served as Chief Investment Officer and led the firm's investment strategy, capital raising, and portfolio growth. My team completed 18 transactions exceeding $600 million. I also helped re-launch a public non-traded REIT. That experience crystallized the central insight behind this practice: the gap between having an investment thesis and having the infrastructure to execute it is where most firms get stuck.

Across these roles, I raised $150 million in equity and completed $300 million of debt financing with institutional partners. Nearly $3 billion of transactions across office, multifamily, retail, and specialty asset classes.

The Technology

Over the past two years, I've built and deployed production AI systems on live CRE transactions. Not experiments — production workflows that run daily.

Systems that screen dozens of real estate listings against specific return criteria overnight. That produce a full underwriting model, IC memo, and pitch deck from an offering memorandum and public data in hours. That scrub enormous diligence datasets and prepare institutional-quality reporting at accuracy levels that surprised me when I first tested them.

I was skeptical at first. Over time, I used them on live transactions and watched the technology produce in hours what used to take my teams weeks. That experience opened my eyes to the leverage we create by combining deep investment judgment with agentic speed and capability.

Today I build and deploy these systems for my own firm and for clients. The technology handles the eighty percent that consumes capacity. The last twenty percent — the judgment calls, the structural instincts, the pattern recognition that comes from two decades of investing — remains a human job. That boundary is where I help clients draw the line correctly.

The Practice

There's a concept in Sanskrit called Seva — selfless service. It's been a guiding principle in my life, and it's the reason I structured my practice the way I did. I take on a small number of engagements where I can go deep, bring everything I've learned, and make a meaningful difference.

I serve as a fractional Chief AI Officer for commercial real estate PE firms, working alongside the CEO and CIO to build the operational infrastructure that lets the platform scale. This isn't technology consulting. It's investment infrastructure — built by someone who's run the investment process and knows what the output needs to look like.

The best measure of my work is the outcomes my clients are able to accomplish.

Education

Georgetown University — MBA, Real Estate
Johns Hopkins University — M.S. Real Estate, Finance & Investments
Virginia Tech — B.S. Property Management

Recognition

GlobeSt Top 50 Under 40 in Commercial Real Estate (2020)

Community

Advisory Board, Huston-Tillotson University School of Business & Technology — created curriculum framework for real estate program (launched Fall 2023)

AI for CRE Collective — active member

Chinmaya Mission Austin — former board member