Scaling AI in private markets — whitepaper

AI is changing how deal teams work. Most firms have adopted a model, connected a few tools, and seen early productivity gains. The challenge now is different: how do you move from individual output to institutional intelligence? How do you make sure the work your team does today is available to the whole firm tomorrow, and that you are not paying a frontier model to start from scratch every time someone opens Claude? This whitepaper explains why scaling AI in private markets is not a tooling problem. It is an architecture problem. And it sets out what the right architecture looks like.

In this guide, you’ll learn:

"Without a layer to capture and structure outputs, you are not building intelligence. You are just generating answers."

Martin Pomeroy, co-founder and CPO, Deal Engine

Who this is for

For deal partners and investment teams: understand why your AI spend is growing faster than the value you are getting from it, and what a better-architected system looks like in practice.

For CTOs and heads of data: a clear framework for moving beyond ad hoc AI usage to a governed, cost-controlled operating model built on a proprietary intelligence warehouse.

Whitepaper: How to scale AI in private markets

In this guide, you’ll learn:

Why most firms stall at piloting

The hidden cost of unstructured AI

The three-layer operating model

The right model for each task

Where Deal Engine fits

"Without a layer to capture and structure outputs, you are not building intelligence. You are just generating answers."

A clear and bold heading

Our Clients

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