In this episode of Selling Signals, we're joined by Vinesh Jha, CEO of ExtractAlpha and former quant researcher at Morgan Stanley's PDT, to explore how quantitative data products are developed, validated and commercialised.

Drawing on ExtractAlpha's new AI Earnings Transcript Model, Vinesh explains how his team identifies predictive opportunities, builds investment signals and differentiates its research from what hedge funds can develop internally.

We also get into the commercial realities of selling to funds: how they evaluate external signals, why research transparency matters, how ExtractAlpha structures trials and what vendors routinely get wrong when bringing datasets to market.

We finish by discussing how AI is changing the economics of quantitative research and allowing vendors to evaluate more potential sources of alpha.

In this episode of Selling Signals, we’re joined by Arsham Ghahramani, CEO of Ribbon AI. Arsham explains how a product built to interview job candidates found a new market in expert systems and investment research.

He describes a hedge fund that went from consulting a handful of experts to interviewing a hundred on the same question. We discuss what this changes about research and how Ribbon handles the confidentiality of a fund’s questions.

We also explore the value of expertise that has never been written down. Arsham shares his view on why demand for human knowledge could grow as AI improves, including the possibility of models calling experts when they get stuck.

In this episode of Selling Signals, we’re joined by Matthew Bernath, co-founder of Alternata. Matthew works with companies exploring whether their existing data could support a new business.

We discuss how to assess the commercial opportunity before committing resources. Matthew explains how prototypes built with synthetic data can help test a proposed product with buyers. He also shares his approach to pricing around the value a customer expects to receive.

The conversation turns to consumption pricing and Matthew’s preference for long-term relationships in which buyers help improve the product. He explains why one-off AI training deals can still make sense when the revenue justifies the work involved.

Throughout the discussion, Matthew returns to one constraint. A data business should not damage the core operation that produces its data. We explore how that affects the opportunities a company should pursue.

What happens when the person sourcing your data is no longer a person?

In this episode of Selling Signals, we’re joined by Freeman Lewin, co-founder of Brickroad. Freeman began his career in private equity and later worked as an attorney negotiating data and AI contracts.

Brickroad uses agents to discover new data suppliers and automate what Freeman calls the “messy middle” of procurement. Its agents search thousands of sources and infer key details about a dataset. They then surface the suppliers most worth investigating.

We also discuss how vendors should think about monetising their data with both frontier AI labs and smaller specialist AI teams.

In this episode of Selling Signals, we’re joined by Tjeerd van Cappelle, founder of aiLiftOff. Tjeerd spent more than 16 years on the buy side as a quantitative investor before building a company that forecasts revenues and earnings for stocks globally.

We discuss what quants look for in data vendors and why a backtest is only the start of the conversation. We also cover the challenge of selling model output, where trust can matter as much as accuracy.

The episode ends with a fascinating discussion on artificial stock markets and what they could mean for AI-driven investment research.

Matt Ober was looking for unconventional datasets before alternative data became an established industry. He has led data strategy at WorldQuant, served as Chief Data Scientist at Third Point and now works with data providers through Social Leverage and Initial Data Offering. In this episode, he explains how the market operates from the fund's side.

The buying process differs sharply between quant and fundamental investors. A quant team may begin by examining coverage, history, frequency, delivery and point-in-time integrity. A fundamental analyst usually begins with a company and a specific investment question.

We discuss what vendors should provide during a trial, why external research cannot replace a fund's own testing and how poor documentation can waste a limited evaluation window. Matt also gives his views on pricing, renewals, distribution and the importance of maintaining good relationships with buyers.

In this episode of Selling Signals, we’re joined by Kai Searle, Senior Consultant at Engage PSG. Kai helps data, research and AI businesses build go-to-market teams across the US, UK and EMEA.

We discuss why hiring for data companies differs from hiring for SaaS. Kai explains what separates elite data sellers from average ones, and how candidates can assess whether an opportunity is as strong as the hiring team says it is.

This episode is for founders building commercial teams and for candidates navigating the data sales market.

In this episode of Selling Signals, we’re joined by Nick Derewlany, a data monetisation leader who has worked across GlobalData and Trustpilot building new data products inside larger organisations.

Nick brings a practical view on how corporates should think about commercialising existing data assets. We talk about why “we have lots of data” is not the same as “we have a data business”, and what companies should prove before hiring a team or putting large revenue targets in front of the board.

The conversation also covers the path from 0 to 1, the importance of repeatable demand, and why $1m ARR across several customers is a very different signal from one large bespoke deal.

This episode is for anyone building, buying or selling data products, particularly inside companies where data monetisation sits alongside a much larger core business.