On the buy side of private markets, most deal work still runs on human patience. A firm receives a teaser, then a memo, then a data room stuffed with spreadsheets and profit-and-loss statements. Someone has to read all of it, pull the numbers that matter, judge them against the firm's criteria, and type the results into a CRM. Multiply that by the hundreds of deals a fund sees in a year, and the math gets brutal. The people hired to make sharp investment calls spend most of their hours on data entry.

TJ Richardson is the Founder and CEO of Rubi, a deal intelligence platform built for private equity funds, family offices, corporate development teams, and independent sponsors. A lifelong entrepreneur raised in Harlem, TJ describes himself as a builder and a competitor first, and both instincts show up in the product.

In this episode of Lead with AI, host Dr. Tamara Nall speaks with TJ Richardson about the framework behind Rubi, how it screens deals against a firm's investment thesis, why he built it to sharpen investors rather than sideline them, and what he thinks still separates human judgment from a model's.  

 A Builder Since the Streets of Harlem   

TJ started his first company at nine, selling custom pens on the streets of Harlem with business cards and an assembly line. Almost every man in his family ran a business, so building was the water he swam in. At twelve, he built his first Minecraft contraption using redstone, the material that acts like wiring inside the game. That detail is the origin of the company name and the clearest way to understand what Rubi is. Redstone does not do the work by itself. It connects the pieces so the whole machine runs. TJ thinks about large language models the same way. A model is one component. Rubi is the framework that gives models the right context, tools, and structure to do real work inside private equity.

 What Rubi Actually Does   

TJ has a plain way of describing the product. He calls it an office for AI agents. At its core sits an orchestrator agent, the boss, which deploys sub-agents for specific jobs and even more granular agents for narrow tasks like pulling a single figure out of a document. The firm's team works through a clean layer on top, built to match how they already do business. Underneath, Rubi hooks into the firm's system of record, extracts data out of teasers, memos, and spreadsheets as they arrive, runs the analysis, and populates the CRM on its own. The first time TJ watched Rubi automate a full deal analysis and load it into a client's long-standing CRM in seconds, he knew he had something. That job normally takes hours, sometimes days, and no analyst signs up to do it.

 Can an AI Tell a Firm to Pass on a Deal?   

Yes, and TJ argues that is the point. Rubi works with each firm to codify its investment thesis in detail. Not just themes and sectors, but the specifics: revenue and EBITDA figures, margins, capital expenditure, the customer concentration a firm will tolerate. Once that thesis is encoded, Rubi analyzes an incoming deal and returns a recommendation. Pass, or take a closer look. The call is grounded in the firm's own criteria, not a generic score. The platform is industry agnostic and works across geographies, though TJ resists the urge to chase every market at once. The larger prize, in his view, is the data itself. Every deal a firm processes becomes part of a usable intelligence layer, giving investors more visibility into their own activity and the markets they are betting on.

 Where Human Judgment Still Wins 

Dr. Nall closes with a question passed along from a previous guest: if AI keeps outperforming humans at more kinds of thinking, what does it mean to be intelligent as a human? Richardson treats intelligence as an asset rather than a virtue. Thrown at the wrong problem, raw intelligence on its own is not always the right answer. What a person brings that a model does not, in his view, is nuance: reading a room, sensing whether someone across the table actually wants to make a deal, understanding what a firm is really trying to accomplish before pointing any tool at the problem.

He extends the point further. The economy, he says, is something human beings built. It has no purpose of its own outside of that. For AI to make sense, it has to operate inside a system that stays valuable to the people running it, not the other way around.

 Upskilling Investors, Not Replacing Them   

TJ splits ethics into two questions. The first is data and security. Rubi silos every customer's data, keeps firms inside what he calls a zone of trust, and holds zero-data-retention terms with its foundation model providers so a firm's information is not used to train outside models. The second question is the one he thinks every AI founder should sit with. What is the human cost of the thing you are building? His answer shapes the product. Rubi is designed to train the next generation of investors and hand them sharper tools, not make them redundant. He wants AI to be a force multiplier for people. Dr. Nall echoed the point with her own team, where output climbed after they adopted AI and the company ended up hiring more people, not fewer.

 The Bet on Where All This Goes   

Ask TJ about 2030 and he zooms out. Private markets are fragmented and still surprisingly manual, and his vision for Rubi is an orchestration layer in the middle of it all, helping firms see more deals, deploy capital better, and spend their time on what he thinks investing is really about, building businesses and building people. Then he goes bolder. He predicts AI could erode some of our largest institutions because resources that used to be scarce, intelligence and access, are being handed out widely. His parallel is the clothing industry. Once manufacturing got cheap and order sizes shrank, anyone with a couple thousand dollars and a social page could launch an Instagram brand. TJ thinks the same shift is coming for knowledge work, and the institutions that made their money gatekeeping it will shrink. Even so, he bets on human nuance, the ability to read a room and decide what matters, as the thing that keeps people valuable.

 Quick Answers   

What is a deal intelligence platform?

A deal intelligence platform turns the documents a firm receives, like teasers, memos, and spreadsheets, into structured, usable data. Rubi is a deal intelligence platform for private equity that extracts that data, analyzes it, and keeps the firm's records current automatically.

How does AI deal with screening work? 

Rubi codifies a firm's investment thesis, including target revenue, EBITDA, margins, capital expenditure, and customer concentration. It then analyzes each incoming deal against those criteria and recommends passing or taking a closer look.

Can AI automate CRM data entry for private equity firms? 

Yes. Rubi connects to a firm's system of record, pulls data out of deal documents as they arrive, runs the analysis, and populates the CRM in seconds, work that previously took hours or days by hand.

What are AI agents in private equity? 

AI agents are programs that carry out tasks on their own. Rubi runs an orchestrator agent that deploys sub-agents for jobs like data extraction and analysis, working together like an office built for deal work.

Who is Rubi built for? 

Rubi is built for the buy side, mostly middle market and lower middle market private equity funds, along with family offices, corporate development teams, and independent sponsors evaluating acquisitions.

Who is TJ Richardson? 

TJ Richardson is the Founder and CEO of Rubi. A lifelong entrepreneur raised in Harlem, he started his first business at nine and built Rubi to bring AI orchestration to private equity deal work while keeping investors at the center.

For private equity funds, family offices, and buy-side teams tired of losing good hours to data entry and thin market visibility, Rubi is worth a look. TJ prefers to start with a conversation rather than a hard sell, so the fastest path is to reach out to him directly or head to rubi.ai.

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