Beyond Chat, AI’s Potential for Funds

If there’s one application that represents the AI boom, it’s the chatbot. Highly capable chatbots are being spun up across every industry, and PE or VC is no exception. It’s not hard to see why. Chat apps are both powerful and incredibly easy to use. But the field is evolving quickly, and additional AI capabilities can provide significant value for funds.

In this post, we’ll share a few AI features beyond chat, their application in a fund’s workflow, and how Metal is integrating these technologies to improve the investment process.

AI Abilities and Financial Use Cases

If you remove the interface from a chatbot, you’ll be left with a sophisticated "reasoning" engine. Trained on extensive data sets, these AI models (i.e., Large Language Models, or LLMs) can replicate human-like thinking by piecing together information in a way that mirrors our own analytical processes.

Here are a few examples:

• Summarization: LLMs can sift through extensive data sets, identify patterns, and distill the essence into concise summaries. For instance, they can analyze dozens of expert call transcripts and capture their themes or sentiments, saving analysts hours of time in the process.
• Extraction: Models can pinpoint specific information within documents, both qualitative and quantitative. For example, extraction can streamline portfolio company reporting by parsing quarterly updates for KPIs and qualitative statements.
• Autonomous Behavior: Representing the cutting edge of AI, LLMs can be directed to achieve specific objectives. Using their reasoning capabilities in sequence, they can perform multi-step tasks like drafting investor memos or financial models.

Integrating AI into Fund Workflows

While these abilities are useful on their own, they’re more valuable together in support of a user’s workflow.

For example, let’s say you’re on an industry deal team and you review multiple CIMs a week. A single one of these documents can take hours to read! But an integrated LLM application can remove this cost. You can use extraction to capture key qualitative and quantitative metrics, synthesize them into concise summaries, and finally draft a single page write up from these summaries – reducing hours of work into minutes.

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CIM summary generated by Metal (source document)

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This is What We’re Building at Metal

While chat remains a vital component of the AI stack, there are many valuable applications to be explored. We’re actively integrating these advanced capabilities into Metal while tailoring them to the needs of fund workflows.

If you're interested in learning more, please connect with us to learn about what Metal is developing next.