Garret Brennan: Deterministic AI for Institutional Quant Workflows | Blushing Quants #23
Notas del episodio
Garret Brennan is the co-founder and CEO of Epoch, an AI-native quantitative research startup building tools for institutional investors who want to integrate AI into their workflows without sacrificing rigor, determinism, or trust. With a background on the fixed income desk at Bank of Montreal in New York, Garret brings both market experience and startup urgency to the problem of making quantitative research faster, more accessible, and more usable in real institutional settings.
In this episode, we talk about what it actually takes to build AI infrastructure for quantitative finance in a market that is both highly technical and deeply conservative. Garret explains why Epoch is not trying to let language models perform the computation itself, but instead uses AI as a structured interface atop a traditional institutional-grade research stack. We get into determinism, hallucination risk, backtesting infrastructure, internal libraries, multi-agent systems, and why infrastructure has to come before AI hype if you want a product that can survive real production use.
We also discuss customer workflows across the buy side and sell side, what different types of traders actually need, how to think about product-market fit in a fragmented market, and why speed to decision is one of the clearest sources of value. Garret also shares lessons from his path from sales and trading into entrepreneurship, how his market experience shaped the product, and why building for quantitative finance requires a rare mix of technical depth, workflow understanding, and obsessive attention to correctness.
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Trading and investing involve substantial risk, including the possible loss of all or part of your capital. You are solely responsible for your own decisions, and you should consult a qualified professional before making financial decisions. By listening to this podcast, you agree that the hosts, guests, and producers are not liable for any losses or damages arising from the use of any information discussed.