Private Markets AI Targets the Work Before the Investment Memo
F2, an AI startup focused on private markets, says generative AI can help deal teams process messy investment data faster. For managers, the useful question is how to redesign diligence workflows while keeping experienced humans in control.
For employers in private equity, private credit and related advisory work, the next wave of AI is being aimed at a familiar bottleneck: the long hours spent turning scattered deal information into material senior investors can use.
F2, a startup building an AI platform for private markets, says it is targeting the manual work that sits at the front end of investment and lending decisions. Don Muir, the company’s co-founder and CEO, described the sector on AI Business’s Targeting AI podcast as one shaped by inconsistent data, unstructured files and information that arrives in many formats.
That matters because private markets do not operate like public markets, where disclosures, pricing data and reporting formats are more standardised. Private equity financing and private credit can be harder to compare across opportunities, and firms often rely on associates to sort, clean and interpret the material before an investment thesis reaches senior decision-makers.
F2 is pitching AI at the diligence workflow
Muir said the associate’s role often begins with taking raw information, standardising it, cleaning it and converting it into analysis that can support investment decisions. F2’s proposition is that much of this early-stage preparation can be handled by AI systems, if those systems are configured around a firm’s existing standards and decision processes.
“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.”
The company’s aim, according to Muir, is to configure a client’s platform so it reflects the institutional knowledge and firm-specific standards that have guided investment decisions for years. He said F2 can apply those standards “instantly with complete data access and powered by generative AI.”
F2 uses models from Anthropic, Google and OpenAI for this work, according to the AI Business report. The company has also built MCPs, or model context protocols, that allow its AI agents to connect with clients’ agents across private markets tasks including screening, underwriting and portfolio monitoring.
In plain English, an MCP is a way for AI systems to share context and use tools in a more controlled manner. For a private markets firm, that could mean connecting an AI agent to approved internal data, workflow steps or analytical templates rather than leaving a model to operate as a standalone chatbot.
The management issue is workflow design, not headcount math
The practical significance for leaders is not simply that AI can summarise documents or extract figures. Those are useful functions, but private markets work depends on judgement, risk appetite, relationship context and accountability. The larger question is how firms redesign the diligence process so trained employees can spend less time wrestling with inconsistent inputs and more time testing assumptions.
That requires more than giving every associate access to a general-purpose AI tool. Firms would need to define which documents can be ingested, what outputs are acceptable, who reviews them and how exceptions are escalated. They would also need controls around confidential information, model performance and audit trails, especially when analysis feeds into lending or investment recommendations.
F2’s approach points to a broader pattern in enterprise AI adoption. The most valuable deployments are often not the flashiest. They sit inside established workflows, handle repetitive preparation work and make human review more focused. In private markets, that could mean faster first-pass screening, more consistent underwriting packs and earlier identification of gaps in company information.
There are also cultural implications. Associates have traditionally learned the business by doing much of this preparatory work themselves. If AI takes over parts of that process, firms will need to be deliberate about training. Junior employees still need to understand how inputs become conclusions, where errors enter the analysis and what a good investment memo looks like.
What leaders should watch next
F2’s claims sit within a fast-moving market for AI agents in professional services and financial workflows. The key test will be whether these systems can handle private markets data reliably enough for high-stakes use, while fitting into the governance practices that investment firms already need.
For managers, the immediate opportunity is to map the work before buying the tool. Which parts of diligence are repetitive and rules-based? Which require expert interpretation? Where do teams lose time because information arrives in the wrong format or lives in too many places? Those answers determine whether AI becomes a useful colleague in the process or another system employees must manage.
The reported facts show that vendors are moving beyond generic AI assistants toward specialised platforms for complex sectors. The practical interpretation is more measured: private markets teams may gain speed and consistency, but only if experienced people remain responsible for standards, review and final judgement.
Reported by Hybrion Insights with reference to AI Business.
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