Private markets investing and lending rely on inconsistent, unstructured, multimodal data sets, according to Don Muir, co-founder and CEO of F2, a native AI private markets platform vendor.

Muir described the sector as one he aims to transform with AI technology. In contrast with public investing and credit, which are highly standardized, private equity financing and private credit are often opaque, and making sense of the information requires substantial human labor.

On the Targeting AI podcast from AI Business, Muir said the job of a private equity or private credit associate is to take that data, standardize and clean it, and then turn it into an investment thesis that helps senior teams make decisions. That workflow sits at the center of how private markets firms evaluate opportunities, monitor risk and maintain consistency across deals, which is why automation in this area can matter for firms that handle large volumes of documents and communications.

"All of that blocking and tackling that's done up front can be automated. The throughput of deal teams who are doing that work can be force-multiplied with AI," Muir said. "Which in our view at F2, means accelerating the growth of the private markets economy on the back of AI-native rails."

F2 aims to configure a client’s platform so it mirrors the same institutional knowledge set of firm standards that companies have relied on for decades when making investment decisions.

"We can just do it instantly with complete data access and powered by generative AI," Muir said.

F2’s go-to models for this work come from Anthropic, Google and OpenAI.

The vendor has also built MCPs, or model context protocols, that allow its agents to communicate with clients’ agents for private markets, screening, underwriting and portfolio monitoring, a setup meant to fit into existing enterprise workflows rather than replace them outright.